Knowledge is more important than money

Those who work really hard throughout their career but don’t take time out of their schedule to constantly learn will be the new “at-risk” group. They risk remaining stuck on the bottom rung of global competition, and they risk losing their jobs to automation, just as blue-collar workers did between 2000 and 2010 when robots […]

Questions for Artificial Intelligence in Health Care

Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems. This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, […]

Split learning for health: Distributed deep learning without sharing raw patient data

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding […]

First compute no harm

Is it acceptable for algorithms today, or an AGI in a decade’s time, to suggest withdrawal of aggressive care and so hasten death? Or alternatively, should it recommend persistence with futile care? The notion of “doing no harm” is stretched further when an AI must choose between patient and societal benefit. We thus need to […]

MIT researchers show how to detect and address AI bias without loss in accuracy

The key…is often to get more data from underrepresented groups. For example…an AI model was twice as likely to label women as low-income and men as high-income. By increasing the representation of women in the dataset by a factor of 10, the number of inaccurate results was reduced by 40 percent. Source: MIT researchers show […]

a16z Podcast: Network Effects, Origin Stories, and the Evolution of Tech

If an inferior product/technology/way of doing things can sometimes “lock in” the market, does that make network effects more about luck, or strategy? It’s not really locked in though, since over and over again the next big thing comes along. So what does that mean for companies and industries that want to make the new […]

The AI Threat to Democracy

With the advent of strong reinforcement learning…, goal-oriented strategic AI is now very much a reality. The difference is one of categories, not increments. While a supervised learning system relies upon the metrics fed to it by humans to come up with meaningful predictions and lacks all capacity for goal-oriented strategic thinking, reinforcement learning systems […]

When AI Misjudgment Is Not an Accident

The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society…But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into […]

The fate of medicine in the time of AI

Source: Coiera, E. (2018). The fate of medicine in the time of AI. The challenges of real-world implementation alone mean that we probably will see little change to clinical practice from AI in the next 5 years. We should certainly see changes in 10 years, and there is a real prospect of massive change in […]

The evolution of Atlas from Boston Dynamics

This overview of the changes in capabilities of the Atlas humanoid robot from Boston Dynamics is both fascinating and bit unsettling. In 5 years Atlas has gone from struggling to stand on one leg, to walking on uneven surfaces, to running on uneven surfaces, to doing backflips and now, in October 2018, to bounding up […]

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