Mini Briefs

Microsoft and partners aim to shrink the ‘data desert’ limiting accessible AI

While AI applied to vision and speech systems has the potential to help people in numerous ways, it isn’t always the most helpful for people with disabilities, since it is not trained on very much data from those people. To rectify this, Microsoft has teamed with a number of nonprofit partners to pursue projects aimed to develop AI systems that will be helpful and accessible to people with disabilities such as blindness and ALS. Microsoft and its partners plan to train systems with the primary intention of accessibility using data from those with different sorts of disabilities, ensuring the algorithms see the sort of data they will be applied to from the beginning. Developing inclusive AI is complicated, since deployed AI systems today have a built-in sense of what is “normal”, from how people walk to how they use their devices. While the timeframe may be long, investing in systems that can be used by those who defy current AI’s understanding is a worthwhile investment.

A radical new technique lets AI learn with practically no data

AI systems require many images to properly recognize an object, but humans are able to do so given only a few examples, even as babies. In the vein of attempting to elevate AI to human-level ability, researchers think the same sort of learning should be possible for machine learning algorithms. Researchers at the University of Waterloo successfully trained an algorithm to recognize digits while being trained on only 10 images, instead of the full 60,000 in the MNIST dataset. But, there’s a catch: the 10 images had to be carefully engineered so as to contain the same amount of information as the original 60,000; furthermore, that engineering was only possible because the researchers used the k-nearest neighbors algorithm for testing their method, an algorithm that is visual and easily interpretable. While the method shows promise in that the 10 images could theoretically be distilled even further to just 2 or 3 images, extending it to complicated algorithms like neural networks is difficult because neural networks are not easily interpretable, making the process of engineering data difficult. Despite difficulties, continued research in this direction holds promise for making machine learning more accessible to those who do not have large amounts of compute at their disposal.

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News

Advances & Business

• AI Is Throwing Battery Development Into Overdrive - Improving batteries has always been hampered by slow experimentation and discovery processes. Machine learning is speeding it up by orders of magnitude.

• Machine learning model helps characterize compounds for drug discovery - Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs.

• AI is about to face a major test: Can it differentiate Covid-19 from flu? - With Covid-19 cases surging in parts of the U.S. at the start of flu season, developers of artificial intelligence tools are about to face their biggest test of the pandemic: Can they help doctors differentiate between the two respiratory illnesses, and accurately predict which patients will become severely ill?

• Use AI To Convert Ancient Maps Into Satellite-Like Images - Updates maps could show land use changes over time, including the social and economic impacts of urbanization.

Concerns & Hype

• When AI hurts people, who is held responsible? - Following a Maricopa County Grand Jury decision, last month the woman behind the wheel of a semi-autonomous Uber vehicle was charged with negligent homicide for the 2018 killing of Elaine Herzberg.

• Staying ahead of the curve - The business case for responsible AI - The AI revolution is now. Over 75% of respondents in EIU’s executive survey are already experimenting with AI, if not piloting or implementing the technology.

• Artificial general intelligence: Are we close, and does it even make sense to try? - A machine that could think like a person has been the guiding vision of AI research since the earliest days–and remains its most divisive idea. The idea of artificial general intelligence as we know it today starts with a dot-com blowout on Broadway.

• ‘Machines set loose to slaughter’: the dangerous rise of military AI - Autonomous machines capable of deadly force are increasingly prevalent in modern warfare, despite numerous ethical concerns. Is there anything we can do to halt the advance of the killer robots? The video is stark. Two menacing men stand next to a white van in a field, holding remote controls.

• AI for good: A better, more inclusive future of work - How can we know if AI negatively or positively affects enterprises, employees, and job candidates?

Analysis & Policy

• Dutch debate intensifies over future shape of AI - A lively discussion is underway in the Netherlands after a senior politician, Finance Minister Wopke Hoekstra, made controversial comments about how artificial intelligence (AI) is going to replace radiologists.

• Access Now resigns from Partnership on AI due to lack of change among tech companies - International digital and human rights organization Access Now has resigned in protest from its role as a member of the Partnership on AI (PAI) due to a lack of change among businesses associated with the group or incorporation of opinions posed by civil society organizations.

Expert Opinions & Discussion within the field

• Google Scholar reveals its most influential papers for 2020 - Artificial intelligence amasses more citations than any other research topic. Google Scholar has released its annual ranking of most highly-cited publications. Artificial intelligence (AI) research dominates once again, accumulating huge numbers of citations over the past year.

• Scientists voice concerns, call for transparency and reproducibility in AI research - International scientists are challenging their colleagues to make Artificial Intelligence (AI) research more transparent and reproducible to accelerate the impact of their findings for cancer patients.

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