Mini Briefs

“AI for Good” talk pushes tech usage to mitigate humans’ environmental impact

The third installment of Stanford’s “AI for Good” seminar series discussed leveraging AI and machine learning to mitigate humans’ environmental impact. Stanford assistant computer science professor Stefano Ermon highlighted recent progress in AI research and pointed out that we need to not only consider how to use AI to benefit as many people as possible, but also have representative data and models that provide insight into issues like infrastructure quality, food insecurity, and poverty.

Lucas Joppa, Microsoft’s chief environmental officer, discussed the importance of developing technology to maximize humans’ benefits on Earth’s systems. Joppa’s recent memo, “AI for Earth”, made recommendations about how Microsoft should deploy its technology after investing in AI research. Joppa also created a team within Microsoft focused on answering this question. Ermon and Joppa, in response to questions about furthering the application of AI to environmental protection, both stressed the need for gathering more data about the earth and our impact on it.

Why asking an AI to explain itself can make things worse

Deep learning models have seen a lot of successes in recent years, but how they come about their predictions is largely unknown. This is a problem when such models are used to make decision that impact people’s lives, such as in law enforcement and medical diagnosis. User should be able to understand how predictions are made and have enough information to disagree or rejected automated decision making.

However, recent research into using visualizations to understand a deep learning model its underlying data revealed some striking problems. While tools sometimes helped people spot missing values in data, this usefulness was overshadowed by a tendency to over-trust and misread the visualizations, and in some cases users couldn’t even describe what the visualizations were showing. An online survey of about 200 machine learning professionals found similar confusion and misplaced confidence. Even worse, many participants, despite not understanding the math behind the models, were happy to use the visualizations to make decisions about deploying the models.

Explainable AI researchers today agree that if AI systems are to be used by more people, those people need to be part of the design from the start. In addition, the explanations that AI gives need to be understandable by anyone using it. Previously, the explainable AI movement was dominated by machine learning researchers. Hopefully, with more perspectives from different areas and a human-centered approach, explainable AI can mitigate overconfidence and misplaced trust in AI.

Advances & Business

• If a novel was good, would you care if it was created by artificial intelligence? - Roland Barthes was speaking metaphorically when he suggested in 1967 that “the birth of the reader must be ransomed by the death of the author”. But as artificial intelligence takes its first steps in fiction writing, it seems technology may one day start to make Barthes’ metaphor all too real.

• GM investing $3 billion to produce all-electric trucks, autonomous vehicles - General Motors on Monday, January 27, announced it will invest $3 billion for production of “a variety” of all-electric trucks and SUVs, as well as the automaker’s recently unveiled Cruise Origin autonomous vehicle. The investment will include $2.

• Miso Robotics unveils its next-gen robot kitchen assistant - On January 28, Miso unveiled the Miso Robot on a Rail (ROAR), which it describes as the “next generation” of “cost-efficient” robotic assistant solutions for restaurant chains.

• Towards a Conversational Agent that Can Chat About… Anything - In “Towards a Human-like Open-Domain Chatbot”, we present Meena, a 2.6 billion parameter end-to-end trained neural conversational model. We show that Meena can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots.

• Nio’s AI-powered digital assistant Nomi will impersonate your dead pet, among other problematic behaviors - Chinese electric car company Nio released an advertisement in which its in-car AI assistant, Nomi, orders bereaved cat parents a condolence gift after their beloved special-needs cat passes away. To some, the attempt at marketing felt creepy and disturbing.

• ServiceNow acquires conversational AI startup Passage AI - ServiceNow announced this morning that it has acquired Passage AI, a startup that helps customers build chatbots, something that should come in handy as ServiceNow continues to modernize its digital service platform. The companies did not share terms of the deal.

• Reality Engines offers a deep learning tour de force to challenge Amazon et al in Enterprise AI - Barely a year old, Reality Engines of San Francisco emerged from stealth mode on Tuesday, announcing a slew of artificial intelligence offerings to perform corporate tasks such as budgeting for cloud services or monitoring corporate networks for break-ins.

• Microsoft takes the wraps off $40 million, five-year ‘AI for Health’ initiative - Microsoft has added a new program to its “AI for Good” line-up. Its new “AI for Health” initiative joins its existing AI for Earth; AI for Accessibility; AI for Humanitarian Action; and AI for Cultural Heritage projects. Microsoft is funding the AI for Health project at $40 million over four years.

• Liverpool are using incredible data science during matches, and effects are extraordinary - Liverpool’s sport-leading data science is providing Jürgen Klopp, manager of premier league club Liverpool, with the tools to change football matches as they’re happening.

• AI Helps Warehouse Robots Pick Up New Tricks - Some of the biggest names in artificial intelligence, including two godfathers of the machine learning boom, are betting that clever algorithms are about to transform the abilities of industrial robots. A number of AI luminaries, including Geoffrey Hinton and Yann LeCun, have invested in Covariant.ai, a startup developing AI technology for warehouse bin-picking bots.

• Neuromorphic Chip Maker Takes Aim At The Edge - Neuromorphic computing has garnered a lot of attention over the past few years, largely driven by its potential to deliver low-power artificial intelligence to the masses. A neuromorphic computing company called BrainChip has just put the finishing touches on its silicon and is about to introduce its first commercial offering into the wild.

• UPS is buying thousands of electric vans and teaming up with Waymo to accelerate the future of delivery - For years, UPS has been gesturing toward a future where some of its delivery vehicles are electric, autonomous, or drones. The delivery giant recently made three announcements: a pilot project with Waymo to use self-driving minivans to shuttle packages; a purchase of 10,000 electric delivery vans from UK startup Arrival, which it will add to its international fleet over the next few years; and a plan to bring its drone delivery testing to San Diego.

• Smarter Delivery Hinges on Smarter Robots - As customers expect to get packages faster, companies and researchers are racing to develop artificial-intelligence systems that can enable warehouse robots to handle new and varied objects without the need for extensive additional training or human help.

Concerns & Hype

• Facial Recognition Startup Clearview AI Is Struggling To Address Complaints As Its Legal Issues Mount - Clearview AI, the facial recognition company that claims to have amassed a database of more than 3 billion photos scraped from Facebook, YouTube, and millions of other websites, is scrambling to deal with calls for bans from advocacy groups and legal threats.

• Technology created deepfakes–does it have a way to stop them, too? - As the 2020 election nears, the weaponization of information has become a growing concern among members of both political parties. Machine learning technology has made it easy for anyone to manipulate videos of public figures for malicious use.

• AI License Plate Readers Are Cheaper–So Drive Carefully - The town of Rotterdam, New York, has only 45 police officers, but technology extends their reach. Last year, Rotterdam embraced a newer generation of automated license plate reader (ALPR) technology, software that can discern plates from more or less any conventional security camera. Rotterdam’s supplier Rekor Systems charges as little as $50 a month to read plates from a single camera.

• Netflix’s “The Circle” Gets One Key Thing Right About A.I. - Part of the show’s novelty comes in the form of an app called the Circle, a “voice-activated” social media platform displayed on TVs around contestants’ hotel rooms. The Circle is more human-powered than the show lets on, and highlights that much of artificial intelligence today is powered by manual, tedious work done by humans.

• Artificial Intelligence Will Do What We Ask. That’s a Problem. - The danger of having artificially intelligent machines do our bidding is that we might not be careful enough about what we wish for. The lines of code that animate these machines will inevitably lack nuance, forget to spell out caveats, and end up giving AI systems goals and incentives that don’t align with our true preferences.

• YouTube’s algorithm seems to be funneling people to alt-right videos - How do we know that? More than 330,000 videos on nearly 350 YouTube channels were analyzed and manually classified according to a system designed by the Anti-Defamation League.

• AI still doesn’t have the common sense to understand human language - While the field of Natural Language Processing (NLP) has made huge strides and machines can now generate convincing passages at the push of a button, recent research demonstrated that the Winograd challenge, a benchmark that evaluates the common-sense reasoning of NLP systems, has made us believe that the field of NLP is farther along than it actually is.

Analysis & Policy

• Government privacy watchdog under pressure to recommend facial recognition ban - The Privacy and Civil Liberties Oversight Board (PCLOB), an independent agency, is coming under increasing pressure to recommend the federal government stop using facial recognition.

Expert Opinions & Discussion within the field

• I Know Some Algorithms Are Biased–because I Created One - Creating an algorithm that discriminates or shows bias isn’t as hard as it might seem. A first-year graduate student’s machine learning algorithm for analyzing a survey sent to US physics instructors, using a particular technique, didn’t find any differences between instructors who taught and did not teach programming.

Explainers

• An introduction to (and puns on) Bayesian neural networks - In this post, we aim to make the argument for Bayesian neural networks from first principles, as well as showing simple examples (with accompanied code) of them working in action.

• Data Labeling & The Secret Language of Autonomous Flight: Part 1: The Role of Data Labeling in Machine Learning and AI - Building an autonomous aircraft that is efficient, reliable, safe, and capable of functioning at scale with thousands of other airborne autonomous vehicles is no small feat. It may come as a surprise to learn that the most important technical process that goes into creating an autonomous vehicle is also extremely simple: data labeling.

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