Starting a robotics company out of school? Not so fast, suggest investors – TechCrunch

Every once in a while, a college student or recent graduate dares to launch a robotics startup and . . . everything goes as well as could be expected. Such is the case, for example, with Alex Rodrigues and Brandon Moak, two former University of Waterloo students who worked on self-driving technologies together in college and formed their now venture-backed, self-driving truck company, Embark, instead of graduating. (Originally called Varden Labs, the startup’s trip through Y Combinator undoubtedly helped.)

Still, to capture the sustained interest of robotics investors, it helps to either have experience in a particular industry or to pull in someone, quickly, who does. That much was established yesterday at UC Berkeley, when three veteran investors — Renata Quintini of Lux Capital, Rob Coneybeer of Shasta Ventures, and Chris Evdemon of Sinovation Ventures — took the stage of a packed Zellerbach Hall to talk about where they’ve invested previously, and where they are shopping now.

Though the three expressed interest in a wide range of technologies and plenty of optimism about what’s to come, each lingered a bit on one point in particular, which was the difficulty robotics founders face who are completely unfamiliar with the particular industry they may hope to reshape with their innovation.

You can catch the entire interview below, but we thought college students — and their professors and mentors — might want to pay particularly close attention to this concern if they’re thinking about hitting up investors in the not-too-distant future.

Quintini on how comfortable she and her colleagues at Lux are when it comes to backing recent college graduates:

What we care the most about what is your unique insight and what do you know about tackling a certain market or problem that’s not obvious or easy to replicate. In some cases, it’s very fair for someone right out of university who finds a technological breakthrough and . . . that breakthrough alone is understandable and comprehensible to the market and it’s a very backable company, and we’ve done that in the past.

But in some cases, and you’ve heard today, [CEO] Patrick [Sobalvarro] from Veo Robotics speak — and [Veo is] actually giving robotic arms perception sensors to allow people and robots to work together — all his insights came because he came from industry. He was at Rethink Robotics; he’s been in the robotics industry, selling to people who use robots as part of the manufacturing process. And so he actually understands the importance of safety and the selling of those systems to customers. Because he knew that, it made a big difference in how he approaches his go-to-market strategy and how he approaches building a product. And somebody who’s just thinking about, ‘Oh, let me figure out the technology and how to understand when a human is close or not’ and who didn’t think about the other angle wouldn’t be so successful or differentiated in our opinion.

Coneybeer sounded a similar tone. In fact, when asked if he felt there were other overlooked opportunities like that identified by Veo — which is refitting existing robotic arms, rather than trying to remake them from scratch — Coneybeer said the most attractive thing of all to him are startups in search of a problem that actually exists:

What we’re very cognizant of is people who love robots and are trying to invent a market or invent a need and kind of force fit it, as opposed to people who understand a need and are using robotics as a tool to truly solve that need. That’s a really key differentiator.

We directed an entirely different question to Evdemon, about how Sinovation thinks about domestic versus industrial robots and whether it expects to commit more capital to one or the other. But Evdemon first took the time to note that the problem of founders who don’t know their industries is a very big one, and deserved more discussion:

Chiming in to what Renata and Rob were saying, you understated [the issue]. The majority of the teams that we are looking on both the consumer and industrial robot [worlds] at the moment are more of a technology trying to find a fit in the market, and that’s obviously a very big problem from a venture point of view.

We also see a lot of teams that are fresh out of school, usually a supervising professor with a couple of his or her PhD students having come across some kind of technological breakthrough in university and trying to commercialize that. But robotics are all about what sectors they are being applied to. An ag tech team that knows nothing about agriculture, or a security robot that has a team that’s come up with a great computer vision breakthrough around security issues but that has no idea how the security industry in the U.S. or other parts of the world is structured, is obviously not a good starting point — at least not from a business-minded point of view.

And all of these companies run across tremendous difficulty when it comes to sales. Complementary of teams and market fit [both, are] important for [students] who are thinking about such a move straight out of school.

Business Is Booming for the UK’s Spy Tech Industry – The Intercept

Chris Dunning-Walton, the founder of a nonprofit called Cyber Cheltenham, or Cynam, organizes quarterly events in the town attended by politicians and entrepreneurs. “Historically, there has been a need for the companies that are working here to be very off the radar with their relationships with GCHQ and to some extent, that does exist,” says Dunning-Walton. But since Edward Snowden leaked information in 2013 about GCHQ’s sweeping surveillance activities, the agency has been forced to come out of the shadows and embrace greater transparency. One consequence of this, according to Dunning-Walton, is that GCHQ is now more open to partnering with private companies, which has helped fuel the cyber industry around the Cheltenham area.

Northrop Grumman, the world’s fifth-largest arms manufacturer, has located its European cyber and intelligence operations in Cheltenham, where it has two offices in the center of the town. In the nearby city of Gloucester, a 20-minute drive west of Cheltenham, Raytheon, the world’s third-largest arms company, in 2015 opened a Cyber Innovation Centre that it says is focused on “big data, analytics and network defense.” BAE Systems Applied Intelligence, the cyber arm of the world’s fourth-largest arms company, also has offices in Gloucester, where it says it “delivers information intelligence solutions to government and commercial customers.”

Many of these companies are secretive about the work they do – especially when it concerns surveillance technology – and refuse to speak to the media. But L3 TRL Technology – which is based in Tewkesbury at the northern tip of this new cyber corridor – does grant an interview via email.

L3 says it provides “electronic warfare” equipment that can jam communication signals and gather intelligence. A spokesperson for the company says it plays “a crucial role in counter terrorism and the protection of military forces with our electronic warfare solutions.” He declines to provide any information about any of the company’s customers. But a video posted on YouTube by a Middle Eastern news agency reveals one potential client: It documents a recent meeting between L3’s parent company and Mohammed bin Zayed, the crown prince of Abu Dhabi and deputy commander of the UAE military.

According to government records, the U.K. has sold weapons and other equipment worth £7.3 billion ($9.9 billion) to the UAE in the past decade, including components for telecommunications eavesdropping technology and “intrusion software,” which is used to hack into targeted phones and computers.

Another Cheltenham-based company is CommsAudit, whose flagship product is a surveillance system called Spectra Black, a portable device that can monitor cellphone calls and other wireless communications. CommsAudit did not respond to a request for comment and does not publicly disclose the identities of its customers. The company was, however, showcasing its products at the 2017 DSEI arms fair in London, which was attended by government delegations from across the world.

Latching onto this wave of innovation, last year, the British government pledged £22 million ($30 million) in funding for a new cyber business park on a patch of land close to GCHQ’s headquarters. “It will act as a ‘honeypot’ for cyber security and high tech supply chain businesses,” the promotional literature said, creating 7,000 jobs, while boosting the number of private companies in the area that can then potentially become GCHQ’s clients. There is a lot of largesse to go around. GCHQ takes the majority of the share of the roughly £2.8 billion ($3.8 billion) budget for Britain’s intelligence services and has twice the number of personnel of MI5 and MI6 combined.

David Woodfine, a former head of the Ministry of Defence’s Security Operations Centre, worked inside GCHQ’s Cheltenham headquarters for two years. He left in September 2013 to found Cyber Security Associates, a Gloucestershire-based company providing cyber consultancy services to the public and private sector.

Woodfine says toward the end of his tenure at GCHQ, there was a realization that the agency needed to partner more with private industry. “From a GCHQ perspective, I think their whole attitude has changed from quite a hard approach – ‘we’ll keep everything in-house’ – to ‘actually, we need to open up.’ They changed their recruiting, their apprenticeship schemes, so they are attracting more young talent into their organization.”

The National Cyber Security Centre – which opened in 2016 under the remit of GCHQ – is currently piloting new “Cyber Schools Hubs” in Gloucestershire. The idea is to send staff into local schools to “encourage a diverse range of students into taking up computer science,” in effect grooming the next generation of cyber-competent spies.

GCHQ offers meager salaries compared to the private sector, but the agency can offer prospective employees the chance to work with technologies that they could not use anywhere else – because if they did, they would be breaking the law. “That’s a good way of retaining people on public sector pay,” says Woodfine. “So you can argue that they don’t join for the money, they join for the ability to learn and to test their techniques and their abilities.”

A GCHQ employee can work with the agency for a few years, learn about its tools and methods, and then take that knowledge with them to a job in the more lucrative private sector, where there are plenty opportunities for surveillance innovation. According to the London-based advocacy group Privacy International, the U.K. has 104 companies producing surveillance equipment for export to foreign governments and corporations. Only the United States – with 122 companies – has more.


A view of the 24-hour operations room at Government Communication Headquarters in Cheltenham on Nov. 17, 2015.

Photo: Ben Birchall/AFP/Getty Images

Since 2013, sales of surveillance and hacking technology have been controlled under the Wassenaar Arrangement, which was signed by 42 countries, including the U.S. and most of Europe. The arrangement is intended to prevent authoritarian regimes from obtaining arms and sophisticated spy tools that could be used to commit human rights violations. However, it is not legally binding. And the U.K. has continued to sell eavesdropping equipment to a number of countries with questionable human rights records, such as Honduras, Bahrain, Saudi Arabia, China, and Qatar.

Inside the bustling Victoria train station in central London, Digital Barriers, the world’s premier video analytics company, has its offices. Video analytics sounds like an arcane branch of the high-tech industry, but in terms of surveillance technology, it is a field that has rapidly advanced in recent years. Zak Doffman, chief executive at Digital Barriers, founded the company in 2010 after recognizing that in the area of video intelligence, there was a gap in the international market. Digital Barriers’s technology is designed to analyze video – and identify people’s faces – in real time, where the cameras are placed, rather than having to rely on retrospective analysis.

In its London offices, the company demonstrates to this reporter how even with a scarf wrapped around a person’s face, its software can successfully identify them within a few seconds using a standard surveillance camera. Facial-recognition technology is notoriously inaccurate and can produce false positives, but Digital Barriers claims its software can pick out obscured and blurred faces in crowds and match them with photographs that are held on databases or published on the internet. It is, the company says, most useful for counterterrorism operations. But in the wrong hands, wired up to a nationwide camera network, the technology could potentially be used to trace the movements of millions of people in real time. “We built the business primarily in the public sector working for government agencies,” says Doffman. “We are now working increasingly in the private sector with the commercial customers.”

Digital Barriers’s website boasts that it has clients in more than 50 countries. Doffman won’t reveal the names of his customers, and when questioned about the export licensing process, he says the company’s products are exempt. “It’s not export control per se,” he says, “so there’s no formal restrictions on the technology.” What would he do if countries with authoritarian governments wanted to buy the system? Doffman says only that Digital Barriers has a “moral code on this stuff.”

People within this industry want the technology to remain uncontrolled; they argue that countries with authoritarian governments don’t want this type of video surveillance anyway. “Countries where you have a lot of corruption, the last thing they want is facial recognition,” says one industry source, because of elite factionalism. But that seems scant reassurance for dissidents living in dictatorships that can now freely access this technology at the right price.

Support for this article was provided by the Pulitzer Center on Crisis Reporting.

Top photo: An aerial view of the Government Communications Headquarters, also known as GCHQ, in Cheltenham, Gloucestershire, on July 1, 2014.

National technology day: here’s how CIOs reforming technology implementation – ETCIO.com

11th May is celebrated as National Technology day since 1998, when India carried first nuclear tests at Pokhran and indigenous aircraft “Hansa-3” test flown at Bangalore. This year the National Technology Day is marked as “Technology for Inclusive and Sustainable Growth”.

After the technology boom in 1988, IT industry started contributing to India’s GDP with an initial surge of 1.2% in the same year.

Information technology industry in India comprises two major components- IT services and business process outsourcing (BPO).

According to NASSCOM, the sector aggregated revenues of US$160 billion in 2017, with export revenue standing at US$99 billion and domestic revenue at US$48 billion, growing by over 13%.

How AI Takes Wearables to the Next Level – IoT For All (blog)

The market for wearable technology is increasing steadily. Around 115 million wearables were shipped in 2017, which is 10.3 percent more compared to 2016.

Apple took the lead with Apple Watch Series 3, and doesn’t seem to let go of the gold in 2018. Together with Xiaomi, Fitbit, Garmin and Huawei, Apple makes up the majority of the market share and keeps smartwatch and fitness trackers first.

However, the trend for other wearables is more than optimistic. According to BI Intelligence, they’ll likely outrun popular wearable solutions in terms of growth rate and take a significant slice of a third of the market by 2021.

There are many features that define today’s wearables – increased functionality, lighter and less bulky hardware, seamless user experience and improved connectivity. Enhanced intelligence would probably be the number one element to define this market across different verticals.

We’ve interviewed the executives and founders of successful wearable companies to find out how AI-enabled technology takes wearables to the whole new level.

AI Assistants in Wellness and Sports

Today, many wearables rely on popular smart assistants, such as Alexa or Siri in Apple Watch. When Amazon rolled out its Mobile Accessory Kit, it became possible to insert Alexa directly into a wearable, not always successfully though. In the meantime, some wearable manufacturers decided to equip their systems with custom, intelligent assistants that excel at certain tasks instead of going all-purpose. And it paid off.

One of such examples is Sensoria Fitness. Award-winning producer and vendor of smart sport apparel, the company provides consumers with an AI in-app coaching to improve running routines using performance analytics.

According to Sensoria Fitness, “Mara artificial intelligence coach provides real-time, actionable, audio and visual feedback on metrics that help you improve performance while decreasing your likelihood of injuries. She cheers you up to keep you motivated, but also provides reminders when your running form falls outside of preset parameters. Mara will tell you not only how far and how fast, but how well you run.”

Game Your Game is another example. The company figured out how to leverage AI and enhance its GAME GOLF wearable system with Caddie – personal golf assistant available in GAME GOLF app from April, 2018. Here’s what the company’s CEO and founder John McGuire says about the upcoming feature:

“Smart Caddie uses artificial intelligence to help golfers make data-driven decisions as they play. It’s like having your own personal caddie who considers every shot you’ve ever hit, and has identified all of your tendencies, understands the course and adjusts for weather and elevation in making its recommendations.”

John McGuire points out the importance of machine learning, defying it as “the core of this product.”

“GAME GOLF has the largest dataset of on-course usage in the industry and allows us to draw knowledge from over two million rounds with users in 137 countries. That is what 60 billion GPS data points can do for you and Smart Caddie will be available to use on over 36,000 courses worldwide.

Machine learning is at the core of this product. These things can only be accurate if the data set is large enough for your machine learning algorithms to be pointed at the data set and basically be learning from the data set that we collect.”

AI Analytics in Wearable Healthcare

Connected devices and AI-enabled technology are likely to increase life expectancy and improve life quality. Wearables play a significant role in this outlook as the simplest, most convenient tools to collect health data, monitor and interact with users on the go. Here’s how medical and care wearables use AI analytics to accellerate their performance.

Take Qardio products as an example. The company produces a variety of smart health tools, such as Qardio armband, intelligent scales and medical-grade ECG trackers. Behind these devices there’s an AI-powered QardioMD platform for doctors that uses vital health data from wearables, analyzes this data and uses an algorithm to prioritize patients who need more attention.

Propeller Health, another medical wearable producer, also works with patient data analytics, but on a bigger scale. Building tracking devices attached to the inhalers for the people who suffer from asthma, the company went further than the analytics of an individual inhaler use.

Propeller Health rolled out an open API for air service that predicts the changes in asthma conditions in certain locations. The company uses machine learning to analyze data from various respiratory medication intake and environmental conditions and forecast potential asthma attacks.

AI Tools to Improve Security

Helping people lead healthier lifestyles and achieve sports goals is one thing, saving people’s lives is an entirely new level, and powered by AI.

One such compelling example is Lumenus. The company started when the founder, Jeremy Wall, was almost killed while riding his bicycle. It was this life-changing event that pushed him to create products that use technology for something meaningful—to save lives. Today, Lumenus designs and produces apparel equipped with wearable LED lights for runners, bicyclists, and motorcyclists. This is what Jeremy Wall says about the product:

“We use IoT data points from a litany of different sources: sensors, 3rd party APIs, GPS, and more to control the color, brightness, and animation of wearable LED lights. Think like the smart-lights we’re used to in homes, but instead of being screwed into a socket in one place, Lumenus makes them mobile.”

And this is how AI helps Lumenus wearables save lives:

“Our software aggregates the different information into a single source, but that’s where we take it to the next level with AI. A Lumenus user opens the app, puts in a final destination and maybe some other notes and then puts the phone away. The goal is what we refer to as Zero UI — once you start the system there is no interaction with the mobile app or even the hardware directly — every command is autonomous.

The system knows where you’re heading and automatically gives a turn signal, alerting the drivers around you. It engages the brake lights that can sense your deceleration while AI helps us know when you’re hitting the brakes and not on a hill. It uses training guidance pace lights, which understand your target training speeds and visually keeps you on pace in real-time. Moreover, it engages the flash for extra safety in danger zones like intersections or roundabouts where over 1/3 of fatal accidents occur.

Today, AI allows us to know which information is necessary and which information is just noise. Soon, we plan to connect Lumenus users directly to in-vehicle car systems and with the help of AI determine if there is a potential of a collision and predictively warn drivers.

Another safety wearable tool by Jiobit was also inspired by a real-life case when the company’s CEO and founder John Renaldi found his own kid wandered away at Millennium Park, according to TechCrunch.

Smart Jiobit trackers for children monitoring also use GPS data, however, for different purposes. The company relies on machine learning algorithms to analyze daily routes and routines of the kids who wear the device and free parents from setting manual “rules” or maps for tracking. It helps parents get a full data-driven picture of their children’s activity. By the way, Jiobit also works for pets.

AI Wearables for Everyone

Jiobit trackers are good both for people and pets. Some wearable manufacturers, in turn, focus on building smart devices for four-legged companions and also rely on AI to excel technology capabilities.

This is what Trackener system does to enable users provide utmost horse care, for example. The wearable tracks horse’s activity, location, behavior and health conditions to inform on nutrition needs, stress levels and predict and prevent anxiety.

The true gem is in the AI-powered analytics, which enables more intelligent monitoring and management. Company’s CEO and co-founder Pauline Issard explains:

“The power of Trackener lies in the data analysis we are doing. The more data our device has been collecting on a horse, the more accurate the problem detection, prediction and recommendation system will be. Trackener is all about comparing data with previous analytics from this same horse, but also with data from other similar horses (age, breed, gender, etc.).”

Other AI-powered wearable Whistle, a Fitbit for Dogs, does for dogs what Trackener does for horses, but with a 3k mile radius of location tracking. The company combined advanced cellular and GPS technology to enable this incredible coverage plus Bluetooth and WiFi connection for activity tracking. On top of that, the Whistle system relies on machine algorithm methodologies to classify dog’s activities, analyze and understand how individual activities such as walking and playing affect pet’s health and wellbeing and identify particular type of activity a dog is doing at a certain moment.

All of these examples showcase the ability of AI-enabled technology to enhance the capabilities of today’s wearable devices and analytics, regardless of industry, field and wearer. Despite occasional disappointments, this market continues growing and surprising us, and AI appears to be the means to streamline this move.

CA Technologies promotes Abhilash Purushothaman to VP of devops & automation for APJ – ETCIO.com

Mumbai: CA Technologies today announced the promotion of Abhilash Purushothaman to vice president, DevOps & Automation for the Asia Pacific and Japan (APJ) region. Purushothaman will be responsible for driving and growing CA’s DevOps portfolio that comprises Continuous Delivery, Agile Operations and Automation (CA Automic) business across.

Purushothaman began his career as a software engineer with Satyam Computer Services and Wipro before joining CA in 2004 as a team lead with the India Technology Center (ITC) in Hyderabad, India. During his time at ITC, he was recognized for multiple patents in the areas of Wireless Triangulation Algorithms and Wireless Site Management by the US Patents & Trademarks Organization. Purushothaman has also held various sales leadership positions in CA India. He was most recently Senior Director of CA’s DevOps Business for APJ and will continue to be based in New Delhi.

“I am very excited to embark on this new journey to further grow and establish CA’s leadership and brand presence across our DevOps & Automation portfolios in APJ,” said Purushothaman. “I have been with CA for more than a decade and I am extremely proud to be part of an organization that puts customers at the heart of everything we do. I believe that is our true differentiator in the market. I am confident that with our talented team and our industry-leading solutions, we will keep helping our customers go from ideas to outcomes faster.”

Purushothaman is a graduate in Electrical and Electronics Engineering from the College of Engineering, Thiruvananthapuram. He also pursued an Executive Management course from the National University of Singapore under CA’s Leadership Development Program.

DSP BlackRock’s Chief Technology Officer Bhavesh Lakhani resigns – ETCIO.com

Bhavesh Lakhani, Chief Technology Officer at DSP BlackRock has resigned, confirmed by the sources.

In his tenure of three years as CTO at DSP BlackRock, Bhavesh was responsible for establishing the technology vision and strategy which includes Business applications, Information and Cybersecurity, Business Intelligence and infrastructure.

He was also accountable for the technology operating expense, technology capital expenditure and operational standards.

Bhavesh has 19 years of experience where he has successfully driven technology strategy partnering with Business and yielding measurable benefits.

He carries a strong educational background with Masters in Computer Engineering from Wayne State University, Michigan and BE Electronics (Honors) from the University of Mumbai along with various certifications and leadership accolades throughout his professional career.

Meheriar Patel joins Jeena & Company as Group Chief Information Officer – ETCIO.com

Meheriar Patel has joined Jeena & Company as Group Chief Information Officer, Consultant and Advisor to Governing Board. In his new role, Patel will drive technology implementations and business strategies in Jeena and 14 associated Companies for global landscape.

Jeena & company has a legacy of 100 years in Supply Chain & Logistics and spearheaded by the 4th generation of its founders. The conglomerate is one of the largest Indian Freight forwarder in the global markets and currently service a host of industries which include Chemical, Pharmaceutical, Automobile, Engineering, Retail & Fashion.

“Today every organisation is on the surge to deploy digital strategies in primary operations, hence my priority list begins with automating transaction systems, IT assets, global IT operations and aligning business synergies”, Meheriar Patel, Group CIO, Jeena & Company

Mumbai based Patel will report to Governing Board of management in Jeena & Company. He has over 25 years of core business IT experience, Patel is adept at successfully delivering organizational IT. His responsibilities include IT leadership, innovation and adoption across multiple industries such as retail, airline, pharmaceutical, banking, finance and logistics service sectors.

Sponsored: The 10 best universities for game development programs – Gamasutra

Presented by 80 LEVEL

Game development resource 80 LEVEL has compiled a comprehensive list of the “10 Best Universities for Game Development.” The mini-site, to be updated annually, will help aspiring game developers find the best fit for the programs they’re seeking with information on curriculum, educational staff, student success stories, and more.

To assist students interested in the world of video game development, 80 LEVEL analyzed more than 105 universities for both undergraduate and graduate programs, and these are the results for the top 10:

DigiPen is a private school, which was founded back in 1988 by Claude Comair. In 1998 they became the first school in the world to offer a bachelor’s degree in video game development. Today, DigiPen is a prominent school for games and technology, with campuses in the US (Redmond, Washington), Singapore, and Spain.

The curriculum here is pretty versatile, covering a number of different topics, including computer science, game design, music and sound design, and digital art and animation. DigiPen also has an active R&D department, which develops tech for different clients including Boeing, Formula One and INDYCAR.

University of Southern California is a large private school located in Los Angeles. It was founded in 1880, nearly a hundred years before the videogame industry. The Interactive Media & Games Division was added to the school’s extensive portfolio in 2001. Today, USC Games is considered one of the best in US by the Princeton Review. This was achieved thanks to the close collaboration between the faculty members of Viterbi School of Engineering’s Department of Computer Science and the Interactive Media & Games Division.

Michigan State University was founded in 1855 and is a public research university in East Lansing, Michigan. MSU is one of the largest universities in the United States (in terms of enrollment) and has approximately 552,000 living alumni worldwide. It’s famous for its research contributions, sports activities and game development courses.

The Game Design and Development Program at Michigan State University was founded back in 2005, and has grown leaps and bounds into a Top 10 Ranked program by the Princeton Review. The program involves a mix of disciplines and backgrounds, comprised of Designers, Artists and Programmers.

The Entertainment Arts and Engineering Master Games Studio (EAE: MGS, MEAE) provides a very interesting opportunity to jump into the wonderful world of videogame design for students enrolling at the University of Utah. This educational establishment provides an intriguing cohort model, where students remain together throughout the entire two years of the program! There are four possible tracks to apply for: Game Arts, Game Engineering, Game Production, or Technical Art. Plus there’s a lot of nice electives in videogame development.

Students enrolled in the Master of Entertainment Arts and Engineering degree program (MEAE) are typically interested in careers in interactive entertainment. The curriculum is built with this goal in mind. The university also offers the opportunity to develop and enhance a professional game portfolio through our “studio simulation” projects courses.

MIT is internationally recognized as one of the best technical schools in the world. It’s no wonder they also excel in videogame development. Actually, there’s a whole new division called MIT Game Lab, which deals with game design and e-sports, helping to train the next generation of game creators.

Full Sail University is a private university based in Florida. Widely appreciated for the amazing music education (41 Full Sail graduates were credited on 46 artists’ releases that were nominated in 36 separate categories during the 2017 Grammy Awards), this school also provides many courses for future game developers. There are a number of Bachelors and Masters degrees available in Game Art, Game Design, Game Development and Mobile Gaming. Most of these courses are available online as well as on campus.

The Center for Games and Playable Media at UC Santa Cruz was formally established in 2010, building on work done since the founding of their videogame degree. The center houses the school’s five games-related research labs including the Expressive Intelligence Studio — one of the largest technical game research groups in the world.

There is a great diversity in the faculty’s topics of research. Projects range from work on artificial intelligence and interactive storytelling to natural language dialogue systems, cinematic communication, procedural content generation, human computer interaction, rehabilitation games, computational photography, and level design. Members of the group have published in some of the most respected journals in the fields of game studies, game AI, and game culture. Currently, the group has more than 20 active research grants on games and is the only non-European university taking part in the European Union’s SIREN Project.

Oklahoma Christian University provides a wonderful opportunity to get a degree in gaming and animation. Game artists and animation students are introduced to the tools and principles used by the animation and game development industries. Integral to the university’s game development philosophy is the notion that you don’t just spend your time in classes, but also go on studio and conference field trips, and explore animation and game development career opportunities. The curriculum is pretty broad, covering traditional, 3D, and experimental animation. Their classes do not just concentrate on 3D modeling, but also involve learning texturing, rigging and game production. Also included are courses on the history of film, video, and animation, which can serve as an excellent way to get the necessary background knowledge for game development.

The Garvey Center for the Arts is the home of the program. It offers spacious studios and labs, drafting/drawing tables, easels, model stand and ample computer equipment as well. Plus there’s a 1,200-square-foot University Art Gallery, where works of prominent artists and students are featured.

Entertainment Technology Center (ETC) at Carnegie Mellon University was founded in 1998. ETC is a professional graduate program for interactive entertainment, which mostly focuses on a two-year, Master of Entertainment Technology (MET) degree, which was established as a joint venture between Carnegie Mellon University’s School of Computer Science and the College of Fine Arts.

The School of Design and Informatics is the home of Abertay’s undergraduate and postgraduate degree programmes in games, digital arts, cybersecurity and applied computer science. Abertay was the first university to offer degrees in Computer Games Technology and Ethical Hacking, and continues to be recognised as an international leader in its fields; the school is designated the National Center for Excellence in Computer Games Education, and has pioneered integrated cross-disciplinary practice-based learning through its workplace simulation approach and the White Space environment. In 2015, it was designated by the Princeton Review as the best school in Europe to study game design.

The School undertakes research and knowledge exchange activities to ensure the development and health of its subjects and disciplines within the University as a whole. The School is also home of Dare Academy and the Securi-Tay conference. It has long-established professional links with Dundee’s thriving computer games community and international companies including Microsoft, Rockstar North, and Sony, as well as industry bodies such as BAFTA, UKIE and TIGA.

“We hope the 80 LEVEL top 10 universities site becomes the go-to guide for future game developers to help them choose the school of their dreams,” said Kirill Tokarev, co-founder and editor-in-chief of 80 LEVEL. “We put a lot of thought into the list, and we will continue to expand it and add relevant information as we update every year.”

The 80 LEVEL top-10 universities scoring is based on over 15 criteria, divided into four categories, which enabled the team to analyze the schools, identifying the best of the best. To obtain the final rating of a particular university, results for each criteria were summarized, providing an overall rating for the university. The results of the calculations were compared with the published ratings by the Princeton Review and Game Designing, with similar yet diverse results. For more information, please visit http://universities.80.lv/

Suspicious event hijacks Amazon traffic for 2 hours, steals cryptocurrency – Ars Technica

said on Twitter. The malicious redirection was caused by fraudulent routes that were announced by Columbus, Ohio-based eNet, a large Internet service provider that is referred to as autonomous system 10297. Once in place, the eNet announcement caused Hurricane Electric and possibly Hurricane Electric customers and other eNet peers to send traffic over the same unauthorized routes. The 1,300 addresses belonged to Route 53, Amazon’s domain name system service

In a statement, Amazon officials wrote: “Neither AWS nor Amazon Route 53 were hacked or compromised. An upstream Internet Service Provider (ISP) was compromised by a malicious actor who then used that provider to announce a subset of Route 53 IP addresses to other networks with whom this ISP was peered. These peered networks, unaware of this issue, accepted these announcements and incorrectly directed a small percentage of traffic for a single customer’s domain to the malicious copy of that domain.”

eNet officials didn’t immediately respond to a request to comment.

The highly suspicious event is the latest to involve Border Gateway Protocol, the technical specification that network operators use to exchange large chunks of Internet traffic. Despite its crucial function in directing wholesale amounts of data, BGP still largely relies on the Internet-equivalent of word of mouth from participants who are presumed to be trustworthy. Organizations such as Amazon whose traffic is hijacked currently have no effective technical means to prevent such attacks.

In 2013, malicious hackers repeatedly hijacked massive chucks of Internet traffic in what was likely a test run. On two occasions last year, traffic to and from major US companies was suspiciously and intentionally routed through Russian service providers. Traffic for Visa, MasterCard, and Symantec—among others—was rerouted in the first incident in April, while Google, Facebook, Apple, and Microsoft traffic was affected in a separate BGP event about eight months later.

Tuesday’s event may also have ties to Russia, because MyEtherWallet traffic was redirected to a server in that country, security researcher Kevin Beaumont said in a blog post. The redirection came by rerouting traffic intended for Amazon’s domain-name system resolvers to a server hosted in Chicago by Equinix that performed a man-in-the-middle attack. MyEtherWallet officials said the hijacking was used to send end users to a phishing site. Participants in this cryptocurrency forum appear to discuss the scam site.

In a statement, Equinix officials wrote: “The server used in this incident was not an Equinix server but rather customer equipment deployed at one of our Chicago IBX data centers. Equinix is in the primary business of providing space, power and a secure interconnected environment for our more than 9,800 customers inside 200 data centers around the world. We generally do not have visibility or control over what our customers – or customers of our customers – do with their equipment.”

The attackers managed to steal about $150,000 of currency from MyEtherWallet users, most likely because the phishing site used a fake HTTPS certificate that would have required end users to click through a browser warning. Still, Beaumont reported, the attacker wallet already contained about $17 million in digital coins, an indication the people responsible for the attack had significant resources prior to carrying out Tuesday’s hack.

The small return, when compared to the resources and difficulty of carrying out the attack, is leading to speculation that MyEtherWallet wasn’t the only target.

“Mounting an attack of this scale requires access to BGP routers are major ISPs and real computing resource [sic] to deal with so much DNS traffic,” Beaumont wrote. “It seems unlikely MyEtherWallet.com was the only target, when they had such levels of access.”

Another theory is that Tuesday’s hijacking was yet another test run. Whatever the cause, it’s a significant development because anyone who can hijack Amazon cloud traffic has the ability to carry out all kinds of nefarious actions.

Post updated to add comment from Equinix and Amazon.

How artificial intelligence is transforming the world – Brookings Institution

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it.1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Authors

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values.2

In order to maximize AI benefits, we recommend nine steps for going forward:

  • Encourage greater data access for researchers without compromising users’ personal privacy,
  • invest more government funding in unclassified AI research,
  • promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,
  • create a federal AI advisory committee to make policy recommendations,
  • engage with state and local officials so they enact effective policies,
  • regulate broad AI principles rather than specific algorithms,
  • take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
  • maintain mechanisms for human oversight and control, and
  • penalize malicious AI behavior and promote cybersecurity.

I. Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.”3 According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up.4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics.5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

II. Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways.6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.”7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.”8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Finance

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion.9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.”10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.”11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions.12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location.13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels.14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.”15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.”16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict.17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030.18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs.19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well.20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.”21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.”22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity.23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates.24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.”25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.”26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists.27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector.28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps.29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself.30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change.31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service.32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred.33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls.34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards.35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.”36

III. Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm?37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development.38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China.39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices.40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.”41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.”42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create.43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made.44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.”45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers.46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates.47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms.48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms.49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

IV. Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity.50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality.51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy.52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.”53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies.54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology.55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits.56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education.57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom.58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.”59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.”60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.”61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city.62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis.63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected.64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’”65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.”66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.”67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness.68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account.69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends.70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions.71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security.72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.”73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

V. Conclusion

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions.74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.


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John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.