Together with the innovative ski equipment company NOW, Nidecker Group and ADDIT-ION, it introduced the first ski equipment designed by artificial intelligence.

There are many things artificial intelligence can do, many areas where it can operate. As the work in the field of artificial intelligence increases, new ones are added to these fields. Finally, support for artificial intelligence was received in the design of ski equipment.

Ski equipment manufacturer NOW, which is a very innovative company in its field, is; In collaboration with artificial intelligence design experts Nidecker Group and ADDIT-ION, it designed snowboard connection equipment through artificial intelligence and produced it using 3D printers.

A design came out of artificial intelligence where everything was considered:NOW turned to artificial intelligence to design a snowboard fitting that would have the most responsive connections, the most suitable structure for turns, and the highest rigidity with the least weight. For this purpose, support was obtained from ADDIT-IONs artificial intelligence and 3D printing technology.

Artificial intelligence produced different designs by using many different materials in Autodesk program. Then the most ideal one was chosen among these designs. Salo Arms, co-founder of ADDIT-ION, said, This concept will be the starting point for new paradigms where algorithms and personalization provide special and all-purpose designs in sports equipment.

The design of artificial intelligence is lighter than other designs:The connection system designed by artificial intelligence is 25% lighter than its current counterparts. Powder based 3D printing was used in the production of the part. Oriol Massanes from ADDIT-ION states that it is possible to print without support thanks to this method.

3D printing also allows the other parts of the connection equipment to be customized and printed. Thus, it is also possible to have completely personalized products. NOW founder JF Pelchat describes the results of information sharing as incredible. This initiative gives an idea about new products that may arise as a result of the cooperation of companies from different sectors.

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NOW Introduces The First Snowboard Equipment Designed By Artificial Intelligence - Somag News

People are the ultimate power behind AI.

There are a lot of compelling things about artificial intelligence, but people still need to get comfortable with it. As shown in a recent survey of 1,500 decision makers released by Cognilytica, about 40 percent indicate that they are currently implementing at least one AI project or plan to do so. Issues getting in the way include limited availability of AI skills and talent, as well as justifying ROI.

Having the right mindset is half the battle with successfully building AI into the organization. This means looking beyond traditional, cold ROI measures, and looking at the ways AI will enrich and amplify decision-making. Ravi Bapna, professor at the University of Minnesotas Carlson School of Management, says attitude wins the day for moving forward with AI. In a recent Knowledge@Wharton article, he offers four ways AI means better decisions:

AI helps leverage the power and the limitations of tacit knowledge: Many organizations have data that may sit unused because its beyond the comprehension of the human mind. But with AI and predictive modeling applied, new vistas open up. What many executives do not realize is that they are almost certainly sitting on tons of administrative data from the past that can be harnessed in a predictive sense to help make better decisions, Bapna says.

AI spots outliers: AI quickly catches outlying factors. These algorithms fall in thedescriptive analyticspillar, a branch of machine learning that generates business value by exploring and identifying interesting patterns in your hyper-dimensional data, something at which we humans are not great.

AI promotes counter-factual thinking: Data by itself can be manipulated to justify pre-existing notions, or miss variables affecting results. Counter-factual thinking is a leadership muscle that is not exercised often enough, says Bapna relates. This leads to sub-optimal decision-making and poor resource allocation. Casual analytics encourages counter-factual thinking. Not answering questions in a causal manner or using the highest paid persons opinion to make such inferences is a sure shot way of destroying value for your company.

AI enables combinatorial thinking: Even the most ambitious decisions are tempered by constraints to the point where new projects may not be able to deliver. Most decision-making operates in the context of optimizing some goal maximizing revenue or minimizing costs in the presence of a variety of constraints budgets, or service quality levels that have to be maintained, says Bapna. Needless to say, this inhibits growth. Combinatorial thinking, based on prescriptive analytics, can provide answers, he says. Combinatorial optimizations algorithms are capable of predicting favorable outcomes that deliver more value for investments.

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Forget The ROI: With Artificial Intelligence, Decision-Making Will Never Be The Same - Forbes

Artificial intelligence (AI) is increasingly embedded into every aspect of life, and China is pouring billions into its bid to become an AI superpower. China's three-step plan is to pull equal with the United States in 2020, start making major breakthroughs of its own by mid-decade, and become the world's AI leader in 2030.

There's no doubt that Chinese companies are making big gains. Chinese government spending on AI may not match some of the most-hyped estimates, but China is providing big state subsidies to a select group of AI national champions, like Baidu in autonomous vehicles (AVs), Tencent in medical imaging, Alibaba in smart cities, Huawei in chips and software.

State support isn't all about money. It's also about clearing the road to success -- sometimes literally. Baidu ("China's Google") is based in Beijing, where the local government has kindly closed more than 300 miles of city roads to make way for AV tests. Nearby Shandong province closed a 16 mile mountain road so that Huawei could test its AI chips for AVs in a country setting.

In other Chinese AV test cities, the roads remain open but are thoroughly sanitized. Southern China's tech capital, Shenzhen, is the home of AI leader Tencent, which is testing its own AVs on Shenzhen's public roads. Notably absent from Shenzhen's major roads are motorcycles, scooters, bicycles, or even pedestrians. Two-wheeled vehicles are prohibited; pedestrians are comprehensively corralled by sidewalk barriers and deterred from jaywalking by stiff penalties backed up by facial recognition technology.

And what better way to jump-start AI for facial recognition than by having a national biometric ID card database where every single person's face is rendered in machine-friendly standardized photos?

Making AI easy has certainly helped China get its AI strategy off the ground. But like a student who is spoon-fed the answers on a test, a machine that learns from a simplified environment won't necessarily be able to cope in the real world.

Machine learning (ML) uses vast quantities of experiential data to train algorithms to make decisions that mimic human intelligence. Type something like "ML 4 AI" into Google, and it will know exactly what you mean. That's because Google learns English in the real world, not from memorizing a dictionary.

It's the same for AVs. Google's Alphabet cousin Waymo tests its cars on the anything-goes roads of everyday America. As a result, its algorithms have learned how to deal with challenges like a cyclist carrying a stop sign. Everything that can happen on America's roads, will happen on America's roads. Chinese AI is learning how to drive like a machine, but American AI is learning how to drive like a human -- only better.

American, British, and (especially) Israeli facial recognition AI efforts face similar real-world challenges. They have to work with incomplete, imperfect data, and still get the job done. What's more, they can't throw up too many false positives -- innocent people identified as threats. China's totalitarian regime can punish innocent people with impunity, but in democratic countries, even one false positive could halt a facial recognition roll-outs.

It's tempting to think that the best way forward for AI is to make it easy. In fact, the exact opposite is true. Like a muscle pushed to exercise, AI thrives on challenges. Chinese AI may take some giant strides operating in a stripped-down reality, but American AI will win the race in the real world. Reality is complicated, and if it's one thing Americans are good at, it's dealing with complexity.

Salvatore Babones is an adjunct scholar at the Centre for Independent Studies and an associate professor at the University of Sydney.

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China Will Lose the Artificial Intelligence (AI) Race (And Why America Will Win) - The National Interest Online

Artificial intelligence (AI) isnt new. What is new is the growing ubiquity of AI in large organizations. In fact, by the end of this year, I believe nearly every type of large organization will find AI-based cybersecurity tools indispensable.

Artificial intelligence is many things to many people. One fairly neutral definition is that its a branch of computer science that focuses on intelligent behavior, such as learning and problem solving. Now that cybersecurity AI is mainstream, its time to stop treating AI like some kind of magic pixie dust that solves every problem and start understanding its everyday necessity in the new cybersecurity landscape. 2020 is the year large organizations will come to rely on AI for security.

AI isnt magic, but for many specific use cases, the right tool for the job will increasingly involve AI. Here are six reasons why thats the case.

The monetary calculation every organization must make is the cost of security tools, programs and resources on one hand versus the cost of failing to secure vital assets on the other. That calculation is becoming easier as the potential cost of data breaches grows. And these costs arent stemming from the cleanup operation alone; they may also include damage to the brand, drops in stock prices and loss of productivity.

The average total cost of a data breach is now $3.92 million, according to the 2019 Cost of a Data Breach Report. Thats an increase of nearly 12 percent since 2014. The rising costs are also global, as Juniper Research predicts that the business costs of data breaches will exceed $5 trillion per year by 2024, with regulatory fines included.

These rising costs are partly due to the fact that malware is growing more destructive. Ransomware, for example, is moving beyond preventing file access and toward going after critical files and even master boot records.

Fortunately, AI can help security operations centers (SOCs) deal with these rising risks and costs. Indeed, the Cost of a Data Breach Report found that cybersecurity AI can decrease average costs by $230,000.

The percentage of state-sponsored cyberattacks against organizations of all kinds is also growing. In 2019, nearly one-quarter (23 percent) of breaches analyzed by Verizon were identified as having been funded or otherwise supported by nation-states or state-sponsored actors up from 12 percent in the previous year. This is concerning because state-sponsored attacks tend to be far more capable than garden-variety cybercrime attacks, and detecting and containing these threats often requires AI assistance.

An arms race between adversarial AI and defensive AI is coming. Thats just another way of saying that cybercriminals are coming at organizations with AI-based methods sold on the dark web to avoid setting off intrusion alarms and defeat authentication measures. So-called polymorphic malware and metamorphic malware change and adapt to avoid detection, with the latter making more drastic and hard-to-detect changes with its code.

Even social engineering is getting the artificial intelligence treatment. Weve already seen deepfake audio attacks where AI-generated voices impersonating three CEOs were used against three different companies. Deepfake audio and video simulations are created using generative adversarial network (GAN) technologies, where two neural networks train each other (one learning to create fake data and the other learning to judge its quality) until the first can create convincing simulations.

GAN technology can, in theory and in practice, be used to generate all kinds of fake data, including fingerprints and other biometric data. Some security experts predict that future iterations of malware will use AI to determine whether they are in a sandbox or not. Sandbox-evading malware would naturally be harder to detect using traditional methods.

Cybercriminals could also use AI to find new targets, especially internet of things (IoT) targets. This may contribute to more attacks against warehouses, factory equipment and office equipment. Accordingly, the best defense against AI-enhanced attacks of all kinds is cybersecurity AI.

Large organizations are suffering from a chronic expertise shortage in the cybersecurity field, and this shortage will continue unless things change. To that end, AI-based tools can enable enterprises to do more with the limited human resources already present in-house.

The Accenture Security Index found that more than 70 percent of organizations worldwide struggle to identify what their high-value assets are. AI can be a powerful tool for identifying these assets for protection.

The quantity of data that has to be sifted through to identify threats is vast and growing. Fortunately, machine learning is well-suited to processing huge data sets and eliminating false positives.

In addition, rapid in-house software development may be creating many new vulnerabilities, but AI can find errors in code far more quickly than humans. To embrace rapid application development (RAD) requires the use of AI for bug fixing.

These are just two examples of how growing complexity can inform and demand the adoption of AI-based tools in an enterprise.

There has always been tension between the need for better security and the need for higher productivity. The most usable systems are not secure, and the most secure systems are often unusable. Striking the right balance between the two is vital, but achieving this balance is becoming more difficult as attack methods grow more aggressive.

AI will likely come into your organization through the evolution of basic security practices. For instance, consider the standard security practice of authenticating employee and customer identities. As cybercriminals get better at spoofing users, stealing passwords and so on, organizations will be more incentivized to embrace advanced authentication technologies, such as AI-based facial recognition, gait recognition, voice recognition, keystroke dynamics and other biometrics.

The 2019 Verizon Data Breach Investigations Report found that 81 percent of hacking-related breaches involved weak or stolen passwords. To counteract these attacks, sophisticated AI-based tools that enhance authentication can be leveraged. For example, AI tools that continuously estimate risk levels whenever employees or customers access resources from the organization could prompt identification systems to require two-factor authentication when the AI component detects suspicious or risky behavior.

A big part of the solution going forward is leveraging both AI and biometrics to enable greater security without overburdening employees and customers.

One of the biggest reasons why employing AI will be so critical this year is that doing so will likely be unavoidable. AI is being built into security tools and services of all kinds, so its time to change our thinking around AIs role in enterprise security. Where it was once an exotic option, it is quickly becoming a mainstream necessity. How will you use AI to protect your organization?

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Why 2020 Will Be the Year Artificial Intelligence Stops Being Optional for Security - Security Intelligence

Much has been written, and rightly so, about the potential that artificial intelligence (AI) can be used to create and promote misinformation. But there is a less well-recognized but equally important application for AI in helping to detect misinformation and limit its spread. This dual role will be particularly important in geopolitics, which is closely tied to how governments shape and react to public opinion both within and beyond their borders. And it is important for another reason as well: While nation-state interest in information is certainly not new, the incorporation of AI into the information ecosystem is set to accelerate as machine learning and related technologies experience continued advances.

The present article explores the intersection of AI and information integrity in the specific context of geopolitics. Before addressing that topic further, it is important to underscore that the geopolitical implications of AI go far beyond information. AI will reshape defense, manufacturing, trade, and many other geopolitically relevant sectors. But information is unique because information flows determine what people know about their own country and the events within it, as well as what they know about events occurring on a global scale. And information flows are also critical inputs to government decisions regarding defense, national security, and the promotion of economic growth. Thus, a full accounting of how AI will influence geopolitics of necessity requires engaging with its application in the information ecosystem.

This article begins with an exploration of some of the key factors that will shape the use of AI in future digital information technologies. It then considers how AI can be applied to both the creation and detection of misinformation. The final section addresses how AI will impact efforts by nation-states to promoteor impedeinformation integrity.

Read and download the full article, Artificial intelligence, geopolitics, and information integrity.

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Artificial intelligence, geopolitics, and information integrity - Brookings Institution

Researchers in the MIT Department of Civil and Environmental Engineering (CEE) have received a gift to advance their work on a device designed to position living cells for growing human organs using acoustic waves. The Acoustofluidic Device Design with Deep Learning is being supported by Natick, Massachusetts-based MathWorks, a leading developer of mathematical computing software.

One of the fundamental problems in growing cells is how to move and position them without damage, says John R. Williams, a professor in CEE. The devices weve designed are like acoustic tweezers.

Inspired by the complex and beautiful patterns in the sand made by waves, the researchers' approach is to use sound waves controlled by machine learning to design complex cell patterns. The pressure waves generated by acoustics in a fluid gently move and position the cells without damaging them.

The engineers developed a computer simulator to create a variety of device designs, which were then fed to an AI platform to understand the relationship between device design and cell positions.

Our hope is that, in time, this AI platform will create devices that we couldnt have imagined with traditional approaches, says Sam Raymond, who recently completed his doctorate working with Williams on this project. Raymonds thesis title, "Combining Numerical Simulation and Machine Learning," explored the application of machine learning in computational engineering.

MathWorks and MIT have a 30-year long relationship that centers on advancing innovations in engineering and science, says P.J. Boardman, director of MathWorks. We are pleased to support Dr. Williams and his team as they use new methodologies in simulation and deep learning to realize significant scientific breakthroughs.

Williams and Raymond collaborated with researchers at the University of Melbourne and the Singapore University of Technology and Design on this project.

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Gift will allow MIT researchers to use artificial intelligence in a biomedical device - MIT News

Artificial Intelligence technology can today automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks.

The company, which says it conducts automated infectious disease surveillance, notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired. Now nearing the end of January, the respiratory virus thats been linked to the city of Wuhan in China has already claimed the lives of more than 100 people. Cases have also popped up in several other countries, including the United States, and the CDC is warning Americans to avoid non-essential travel to China.

But artificial intelligence can be far more useful than just keeping epidemiologists and officials informed as a disease pops up. Researchers have built AI-based models that can predict outbreaks of the Zika virus in real time, which can inform how doctors respond to potential crises. Artificial intelligence could also be used to guide how public health officials distribute resources during a crisis. In effect, AI stands to be a new first line of defense against disease.

Other data, like traveler itinerary information and flight paths, can help give the company additional hints about how a disease will likely spread. For instance, earlier this month, BlueDot researchers predicted other cities in Asia where the coronavirus would show up after it appeared in mainland China.

The idea behind BlueDots model to get information to health care workers as quickly as possible, with the hope that they can diagnose and, if needed, isolate infected and potentially contagious people early on.

But artificial intelligence can be far more useful than just keeping epidemiologists and officials informed as a disease pops up. Researchers have built AI-based models that can predict outbreaks of the Zika virus in real time, which can inform how doctors respond to potential crises. Artificial intelligence could also be used to guide how public health officials distribute resources during a crisis. In effect, AI stands to be a new first line of defense against disease.

More broadly, AI is already assisting with researching new drugs, tackling rare diseases, and detecting breast cancer. AI was even used to identify insects that spread Chagas, an incurable and potentially deadly disease that has infected an estimated 8 million people in Mexico and Central and South America. Theres also increasing interest in using non-health data like social media posts to help health policymakers and drug companies understand the breadth of a health crisis. For instance, AI that can mine social media posts to track illegal opioid sales, and keep public health officials informed about these controlled substances spread.

Still, all of these advancements represent a more optimistic outlook for what AI can do. Typically, news of AI robots sifting through large swathes of data doesnt sit so well. Think of law enforcement using facial recognition databases built on images mined from across the web. Or hiring managers who can now use AI to predict how youll behave at work, based on your social media posts. The idea of AI battling deadly disease offers a case where we might feel slightly less uneasy, if not altogether hopeful. Perhaps this technology if developed and used properly could actually help save some lives.

Similarly, the epidemic-monitoring company Metabiota determined that Thailand, South Korea, Japan, and Taiwan had the highest risk of seeing the virus show up more than a week before cases in those countries were actually reported, partially by looking to flight data. Metabiota, like BlueDot, uses natural-language processing to evaluate online reports about a potential disease, and its also working on developing the same technology for social media data.

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Artificial Intelligence (AI) in battling the coronavirus - ELE Times

HOUSTONChris Benson, principal artificial intelligence strategist for global aerospace, defense, and security giant Lockheed Martin, will give the opening keynote presentation at the Connected Plant Conference 2020, which will take place February 25 to 27, 2020, at the Westin Peachtree Plaza in Atlanta, Georgia.

Kicking off the event, Benson will shed light on the vast role and potential that artificial intelligence (AI) offers as the world embarks on a definitive fourth industrial revolution. While AI is a technology that is still emerging within the power and chemical processing sectors, it has made notable headway in other industries, including defense, security, and manufacturing, and it is commonly hailed as an integral technology evolution that will take IIoT to the next level. Some even describe AI as the software engine that will drive the fourth revolution.

Bensons address will glean from his deep knowledge of AI as a long-time solutions architect for AI and machine learning (ML), and the emerging technologies they intersectincluding robotics, IoT, augmented reality, blockchain, mobile, edge, and cloud. As a renowned thought-leader on AI and related fields, Benson frequently gives captivating speeches on numerous topics about the subject. He also discusses AI with an array of experts as co-host of the Practical AI podcast, which reaches thousands of AI enthusiasts each week. Benson is also the founder and organizer of the Atlanta Deep Learning Meetupone of the largest AI communities in the world.

Before he joined Lockheed Martin, where he oversees strategies related to AI and AI ethics, Benson was chief scientist for AI and ML at technology conglomerate Honeywell SPS, and before that, he was on the AI team at multinational professional services company Accenture.

This years Connected Plant Conference, scheduled for February 25 to 27 in Atlanta, is the only event covering digital transformation/digitalization for the power and chemical process industries. Presenters will explore the fast-paced advances in automation, data analytics, computing networks, smart sensors, augmented reality, and other technologies that companies are using to improve their processes and overall businesses in todays competitive environment.

To register or for more information, see: https://www.connectedplantconference.com

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Access Intelligence Announces Artificial Intelligence Strategist Chris Benson Will Deliver the Keynote Presentation at Connected Plant Conference 2020...

If I recall correctly, the Shulchan Aruch says that a person a person who is sleepin, and even a deaf person are also counted in a minyan, despite the fact that they cannot answer umein after the chazan. But the SA also says that if there are not nine people who have kavanah during the chazans recitation of the barachos, those barachos are close to being said in vain.

Most meforshim conclude that there is no contradiction (aka the barachos from a minyan sleeping a schnoozer is accepted by the Ebeshter but the barachos from a minyan w/o kavanah may not be yotzeh) because the Shulchan Aruch doesnt explicitly say not accepted but instead uses the terminology are close to being, rejected. They bring down from this that in actuality the chazans barachos are not said in vain for minyanim with snoozers and schmoozers. There are disagreements as to how many people that do not actually answer umein can be counted for a minyan with the kulah being up to four schmoozers (aka no kavanah) and one snoozer (aka sleeping) and the chumrah being there always have to be nine daveners with kavanah answering umein during chazaras hashatz.

HOWEVER, nowhere in Shulchan aruch does it address whether R2D2 and his robotic friends with a virtual neshamah who meticulously answer uuumeeein vuumeeein are to be included in minyanim. They would certainly take major offense at being considered a golam.

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Yiiddeshe Application for Robotics and Artificial Intelligence - Yeshiva World News

In 2018, Google unveiled Duplex, an artificial intelligence-powered assistant that sounds eerily human-like, complete with umms and ahs that are designed to make the conversation more natural. The demo had Duplex call a salon to schedule a haircut and then call a restaurant to make a reservation.

As Googles CEO Sundar Pichai demonstrated, the system at Googles I/O (input/output) developer conference, the crowd cheered, hailing the technological achievement. Indeed, this represented a big leap toward developing AI voice assistants that can pass the Turing Test, which requires machines to be able to hold conversations while being completely indistinguishable from humans.

But not everyone was so enthusiastic. Some technology commentators saw it as a form of deception by design. In a 2018 tweet, prominent University of North Carolina techno-sociologist Zeynep Tufekici described the system as horrifying, and wrote: Silicon Valley is ethically lost, rudderless, and has not learned a thing.

Responding to public pressure, a Google spokeswoman said in a statement, We are designing this feature with disclosure built-in, and well make sure the system is appropriately identified.

But what if knowing that we are interacting with a bot made for a worse human experience? Suppose you are interacting with a customer service agent that you know is just a computer program. Might you give yourself a little more license to use abusive language or to lob insults? After all, you are not going to hurt any real human beings. As satisfying as this might be, could this shift in your behavior lead to longer and less efficient customer service calls and a worse overall experience for you?

To explore these questions, we ran studies in which participants played a cooperation game with either a human associate or a bot that used AI to adapt its behavior to maximize its payoffs. This game was designed to capture situations in which each of the interacting parties can either act selfishly in an attempt to exploit the other, or act cooperatively in an attempt to attain a mutually beneficial outcome.

In some instances, participants were told who they were interacting with: a human or a bot. In others, we gave false information about the associates identity. Some were told they were interacting with a bot when they were actually interacting with a human, and others were told they were interacting with a human, when in fact it was a bot.

The results showed that bots posing as humans were very efficient at persuading the partner to cooperate in the game. In fact, these bots were better at eliciting cooperation with humans than other humans were. When the bots true nature was revealed, however, cooperation rates dropped significantly, and the bots superiority was negated.

In fact, among all conditions we studied, the best outcome was achieved when people interacted with bots but were told they were interacting with humans. This is precisely the situation that outraged people over the Google Duplex demo and that caused Google to back off and indicate that they will disclose the nonhuman nature of the system.

As AI systems continue to approach or exceed human-level performance in various tasks, bots will be increasingly capable of passing as humans. In the near future, we will interact with bots on the phone, social media, or even video, in a variety of contexts, from business to government to entertainment and they will be indistinguishable from their human counterparts.

Our research reveals that while the much-touted algorithmic transparency is important, it may sometimes come at a cost. So now we must ask ourselves: Should we allow companies to deceive us into thinking bots are human if this makes us happier customers or more polite, cooperative people? Or does interacting with a machine we believe is a human violate something sacred, like human dignity? What is important to us: transparency or efficiency? And in what context might we prefer one or the other? Although there is broad consensus that machines should be transparent about how they make decisions, it is less clear whether they should be transparent about who they are.

Science, including our own experiment, cannot answer this question, since it is a question about what we value most transparency or efficiency. Maybe we can have both and as humans learn to work cooperatively with machines. But until we do, society needs to recognize and grapple with the ethics and trade-offs.

Talal Rahwan is an associate professor at New York University Abu Dhabi. Jacob Crandall is an associate professor at Brigham Young University. Fatimah Ishowo-Oloko is a PhD graduate from Khalifa University. Iyad Rahwan is an associate professor at MIT and director of the Max Planck Institute for Human Development.

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Ethics, efficiency, and artificial intelligence - The Boston Globe