Big Blue wants out of health care, after spending billions to stake its claim, just as rival Oracle is moving big into the sector via its $28 billion bet for Cerner. IBM spent more than $4 billion to build Watson Health via a series of acquisitions. The business now includes health care data and analytics business Truven Health Analytics, population health company Phytel, and medical imaging business Merge Healthcare. IBM first explored a sale of the division in early 2021, with Morgan Stanley leading the process. WSJ reported at the time that the unit was generating roughly $1 billion in annual revenue, but was unprofitable. Sources say it continues to lose money.

When it comes to forecasting the elements, many seem ready to welcome the machine. But humans still outperform the algorithms -- especially in bad conditions.

The global pandemic has heightened our understanding and sense of importance of our own health and the fragility of healthcare systems around the world. We’ve all come to realize how archaic many of our health processes are, and that, if we really want to, we can move at lightning speed. This is already leading to a massive acceleration in both the investment and application of artificial intelligence in the health and medical ecosystems.

As part of our AI for children project, UNICEF has developed this policy guidance to promote children's rights in government and private sector AI policies and practices, and to raise awareness of how AI systems can uphold or undermine these rights. The policy guidance explores AI systems, and considers the ways in which they impact children.

The Commission proposes today new rules and actions aiming to turn Europe into the global hub for trustworthy Artificial Intelligence (AI). The combination of the first-ever legal framework on AI and a new Coordinated Plan with Member States will guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation across the EU. New rules on Machinery will complement this approach by adapting safety rules to increase users' trust in the new, versatile generation of products.

The White House Office of Science and Technology Policy is developing principles to guard against powerful technologies—with input from the public.

China has revealed its first set of ethical guidelines governing artificial intelligence, placing emphasis on protecting user rights and preventing risks in ways that align with Beijing's goals of reining in Big Tech's influence and becoming the global AI leader by 2030.

The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.

If you are interested in governance models, technology and AI, you might find it interesting to learn more about Japan’s recent “Agile Governance” proposal. You can find more info and links, as well as the conversation with Mr. Hiroki Habuka who was the main person driving this project further here.

After Watson triumphed on the gameshow Jeopardy in 2011, its star scientist had to convince IBM that it wasn't a magic answer box, and "explained that Watson was engineered to identify word patterns and predict correct answers for the trivia game." The New York Times looks at what's happened in the decade since.

Watson has not remade any industries. And it hasn't lifted IBM's fortunes. The company trails rivals that emerged as the leaders in cloud computing and A.I. — Amazon, Microsoft and Google. While the shares of those three have multiplied in value many times, IBM's stock price is down more than 10 percent since Watson's "Jeopardy!" triumph in 2011. The company's missteps with Watson began with its early emphasis on big and difficult initiatives intended to generate both acclaim and sizable revenue for the company, according to many of the more than a dozen current and former IBM managers and scientists interviewed for this article.

IBM has released an open dataset of coding samples, which demonstrate programming tasks, to help train AI systems to write code. The dataset, known as Project CodeNet, includes 14 million code samples in 55 different programming languages. Researchers at IBM have already begun using the dataset to train AI systems to write code and found that the systems achieved a 90 percent accuracy rate in most code classification and code similarity experiments.

On December 30, 2019, BlueDot, a Toronto-based startup that uses a platform built around artificial intelligence, machine learning and big data to track and predict the outbreak and spread of infectious diseases, alerted its private sector and government clients about a cluster of “unusual pneumonia” cases happening around a market in Wuhan, China.

Leveling Playing Field between Humans and Algorithms: a conversation with Peter Cotton, Ph.D. (Stanford), who spent his 20+ years career leading AI and ML initiatives at major US banks.

A bird’s-eye view of the corporate topography reveals that, even aside from the big tech titans, more and more companies are acquiring talented AI teams at ever faster rates. Not all AI applications are treated equally, however, and what’s telling is the data on the type of technology that’s being bought in these acquisitions, as it offers a window into how AI might significantly change certain industries in the coming years.

The tech giants have developed a new vocabulary to use when they want to assure the public that they care deeply about developing AI responsibly—but want to make sure they don’t invite too much scrutiny. Here’s an insider’s guide to decoding their language and challenging the assumptions and values baked in.

The use of facial recognition for surveillance, or algorithms that manipulate human behaviour, will be banned under proposed EU regulations on artificial intelligence.

In only a few months the European Commission is set to present a regulation on artificial intelligence (AI). Despite numerous statements from civil society and other actors about the dangers of applying a risk-based approach to regulating AI, the European Commission seems determined to go in this direction. The Commission already said in its 2020 White Paper on Artificial Intelligence that it wanted to apply a risk-based approach.

However, a risk-based approach to regulation is not adequate to protect human rights. Our rights are non-negotiable and they must be respected regardless of a risk level associated with external factors.

This is what a human-centred approach to AI technology could look like.

The State of AI Ethics Report (January 2021) captures the most relevant developments in AI Ethics since October of 2020.

On December 2, after a protracted disagreement over the release of a research paper, Google forced out its ethical AI co-lead, Timnit Gebru. The paper was on the risks of large language models, AI models trained on staggering amounts of text data, which are a line of research core to Google’s business. Gebru, a leading voice in AI ethics, was one of the only Black women at Google Research.

European governments never fail to surprise the world with their innovative minds. Such registries can empower citizens and give them a way to evaluate, examine, or question governments’ applications of AI.

People love convenience and Amazon has prospered by obsessing about how to anticipate our wants before we’re even aware of them. Here is a very detailed news column on how and why Amazon collects data about you.

This week the Guardian published an essay written by GPT-3, OpenAI's language generator, calling it "a cutting edge language model that uses machine learning to produce human like text. It takes in a prompt, and attempts to complete it." For this esssay, the essay was fed the prompt, "I am not a human. I am Artificial Intelligence. Many people think I am a threat to humanity. Stephen Hawking has warned that AI could 'spell the end of the human race.' I am here to convince you not to worry. Artificial Intelligence will not destroy humans. Believe me." Here's what the AI wrote.

Practical use of personal data and AI to save the planet. Using AI algorithm to turn personal photos of forest and nature into a blend of fire and ash to help us visualize the issues of climate change.

It is not uncommon now for AI experts to ask whether an AI is ‘fair’ and ‘for good’. But ‘fair’ and ‘good’ are infinitely spacious words that any AI system can be squeezed into. The question to pose is a deeper one: how is AI shifting power?

Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of "Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?".

How should democracies effectively compete against authoritarian regimes in the AI space? This report offers a “terrain strategy” for the United States to leverage the malleability of artificial intelligence to offset authoritarians' structural advantages in engineering and deploying AI.

A new approach to training artificial intelligence algorithms involves paying people to submit medical data, and storing it in a blockchain-protected system.

This paper discusses the establishment of a governance framework to secure the development and deployment of “good AI”, and describes the quest for a morally objective compass to steer it.

IBM and Microsoft are among the first to sign the Vatican’s “Rome Call for AI Ethics.”

Ruoss et al. published the first method to train AI systems with mathematically provable certificates of individual fairness. Full source code is available on Github.

New proposals around data and artificial intelligence will be subject to restrictions that rivals in China and the United States do not face.

Companies discover data ethics as part of their corporate communications: IKEA promises to embed data ethics into all their processes.

The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.

A Silicon Valley lobby enrolled elite academia to avoid legal restrictions on artificial intelligence.

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

Interesting article using intersectional analysis do define and recommend mitigations to emergent discrimination in algorithmic systems.

A US-based AI institute says that the science behind the technology rests on “shaky foundations”.

The United States Department of Defense (DoD) enduring challenge is to retain a technological and military advantage while upholding and promoting democratic values, working with our allies, and contributing to a stable, peaceful international community. DoD’s development and use of artificial intelligence (AI) reflects this challenge.
The Defense Innovation Board (DIB) recommends five AI ethics principles for adoption by DoD, which in shorthand are: responsible, equitable, traceable, reliable, and governable. These principles and a set of recommended actions in support of them are described in this document.

Earlier this week, I traveled to Paris to attend the Global Forum on Artificial Intelligence for Humanity (GFIAH). The by-invitation event featured one day of workshops addressing issues such as AI and culture, followed by a two days of panels on developing trustworthy AI, data governance, the future of work, delegating decisions to machines, bias and AI, and future challenges. The event was a part of the French government's effort to take the lead on developing a new AI regulatory framework that it describes as a 'third way', distinct from the approach to AI in China and the United States.

Artificial intelligence is rapidly transforming and improving the ways that industries like healthcare, banking, energy, and retail operate. However, there is one industry in particular that offers incredible potential for the application of AI technologies: education. The opportunities — and challenges — that the introduction of artificial intelligence could bring to higher education are significant.

You may have seen the EU Guidelines on Ethics in AI. As I believe we are entering in a 'regulating AI debate', it's worth sharing it here.

Startups are importing and imposing AI systems founded on individualistic and capitalist drives. “Mining” people for data is reminiscent of the colonizer attitude that declares humans as raw material.

A new approach to training artificial intelligence algorithms involves paying people to submit medical data, and storing it in a blockchain-protected system.