Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide. AI has the potential to reshape medicine and make healthcare more accurate, efficient and accessible; this Review discusses recent progress, opportunities and challenges toward achieving this goal.

Personal use of this material is permitted. N2 - In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, trainingand deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions.

By now, it's a truism that automation will replace certain careers even as leaving others intact. Experts agree with the maximum susceptible are jobs that require routine, rote tasks: a bookkeeper, a secretary, or a manufacturing facility worker. Each of those contains notably repetitive and predictable obligations effortlessly taught to machines. The rise of machine learning and self-replicating artificial intelligence (AI) has jeopardized many other professions, significantly programmers. Ironically, a number of their first-class work can be their downfall: As builders make ever-more effective and shrewd algorithms, they threaten coding themselves into obsolescence.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. During that time, these symposia provided a fertile ground for numerous collaborations and pioneered many discussions revolving around HRI, XAI for HRI, service robots, interactive learning, and more. This unique intersection of expertise, along with the rising interest in robots in mixed human-robot environments, calls for an informed discussion about the future of AI-HRI as a united research community. As such, this year's symposium had no single theme and AI-HRI submissions were encouraged from across disciplines and research interests. Moreover, with the rising interest in AR and VR as part of an AI-HRI system, along with the difficulties in running physical experiments during the pandemic, this year we specifically encouraged researchers to submit works that do not include a physical robot in their evaluation, but promote HRI research in general.

Earlier in the year, our new book was released on Amazon, called the AI Dilemma, and it discussed the impacts of AI on different industries, ranging from: financial services, government, healthcare, media and technology, manufacturing, retail, to name just a few. As we review the AI ethics landscape, there have been many developments, some reinforcing that we do not have sufficient guardrails in place yet, while other developments on ethical AI policy fronts giving us increased confidence that the right approaches on AI risks are seriously being considered. This blog reviews aspects of AI Ethics from A Perfect Storm lens and highlights lessons learned from AI failures. Microsoft's Bot Tay - the bot Tay debacle that came up with racists remarks within 24 hours of its interaction with people -it's an important learning on continued risks of AI bots using ML generalizations from large amounts of data. Microsoft trained Tay's algorithm on public data along with material provided by professional comedians to increase language literacy for the bot.

Every company that derives value from language stands to benefit from NLP, the branch of machine learning that has the most transformative potential. Language is the lowest common denominator in almost all of our interactions, and the ways in which we can capture value from language has changed dramatically over the last three years. Recent advancements in NLP have outsized potential to accelerate business performance. It even has the promise of bringing trust and integrity back to our online interactions. Large incumbents have been the first to jump onboard, but the real promise lies in the next wave of NLP applications and tools that will translate the hype around artificial intelligence from ideology into reality. So, there you have it, these are my personal highlights of 2021 in NLP. I hope you enjoyed this summary and it'd be great to hear about your personal highlights from the past 12 months in NLP. Please comment on this blog post or reach out directly.

In the spirit of the last couple of years, we review developments in what we have identified as the key technology drivers for the 2020s in the world of databases, data management and AI. We are looking back at 2021, trying to identify patterns that will shape 2022. Today we pick up from where we started with part one of our review, to cover AI and knowledge graphs. In principle, we try to approach AI holistically. To take into account positives and negatives, from the shiny to the mundane, and from hardware to software.

In the spirit of the last couple of years, we review developments in what we have identified as the key technology drivers for the 2020s in the world of databases, data management and AI. We are looking back at 2021, trying to identify patterns that will shape 2022. Today we pick up from where we started with part one of our review, to cover AI and knowledge graphs. The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. In principle, we try to approach AI holistically.

Every company that derives value from language stands to benefit from NLP, the branch of machine learning that has the most transformative potential. Language is the lowest common denominator in almost all of our interactions, and the ways in which we can capture value from language has changed dramatically over the last three years. Recent advancements in NLP have outsized potential to accelerate business performance. It even has the promise of bringing trust and integrity back to our online interactions. Large incumbents have been the first to jump onboard, but the real promise lies in the next wave of NLP applications and tools that will translate the hype around artificial intelligence from ideology into reality. So, there you have it, these are my personal highlights of 2021 in NLP. I hope you enjoyed this summary and it'd be great to hear about your personal highlights from the past 12 months in NLP. Please comment on this blog post or reach out directly.