Last Week in AI #177: OpenAI commercializes DALL-E 2, Sony AI beats human competitors in racing game, Gmail getting smarter searches, and more!


Last week OpenAI moved DALL-E 2, the image generation tool, into Beta (the company hopes to expand its current user base to 1 million) while granting users the "the right to reprint, sell, and merchandise" images they generate with DALL-E. This is useful for users who wish to use the generated images for commercial purposes, like making illustrations for children's books. Other openly available AI image generation models face similar problems. Also, it's not clear if OpenAI violated any IP laws for just training on these Internet images and then commercializing their model. While the UK is exploring allowing commercial use of models trained on public but trademarked data, the U.S. may not follow suit.

ARK Invest's Big Ideas 2022: The 14 transformative technologies to watch this year


ARK Invest solely invests in disruptive innovations. ARK's thematic investment strategies span market capitalizations, sectors, and geographies to focus on public companies that we expect to be the leaders, enablers, and beneficiaries of disruptive innovation. ARK's strategies aim to deliver long-term growth with low correlation to traditional investment strategies. ARK Invest defines "disruptive innovation" as the introduction of a technologically enabled new product or service that potentially changes the way the world works. ARK focuses solely on offering investment solutions to capture disruptive innovation in the public equity markets.

Demographic Confounding Causes Extreme Instances of Lifestyle Politics on Facebook Artificial Intelligence

Lifestyle politics emerge when activities that have no substantive relevance to ideology become politically aligned and polarized. Homophily and social influence are able generate these fault lines on their own; however, social identities from demographics may serve as coordinating mechanisms through which lifestyle politics are mobilized are spread. Using a dataset of 137,661,886 observations from 299,327 Facebook interests aggregated across users of different racial/ethnic, education, age, gender, and income demographics, we find that the most extreme instances of lifestyle politics are those which are highly confounded by demographics such as race/ethnicity (e.g., Black artists and performers). After adjusting political alignment for demographic effects, lifestyle politics decreased by 27.36% toward the political "center" and demographically confounded interests were no longer among the most polarized interests. Instead, after demographic deconfounding, we found that the most liberal interests included electric cars, Planned Parenthood, and liberal satire while the most conservative interests included the Republican Party and conservative commentators. We validate our measures of political alignment and lifestyle politics using the General Social Survey and find similar demographic entanglements with lifestyle politics existed before social media such as Facebook were ubiquitous, giving us strong confidence that our results are not due to echo chambers or filter bubbles. Likewise, since demographic characteristics exist prior to ideological values, we argue that the demographic confounding we observe is causally responsible for the extreme instances of lifestyle politics that we find among the aggregated interests. We conclude our paper by relating our results to Simpson's paradox, cultural omnivorousness, and network autocorrelation.

The rise of industrial AI and AIoT: 4 trends driving technology adoption


The AI adoption rate in industrial settings has increased from 19% to 31% in slightly more than two years, according to data from the recently released 252-page Industrial AI and AIoT Market Report 2021–2026. On top of the 31% of respondents that have fully or partially rolled out AI technology in their operations, an additional 39% are currently testing or piloting the technology. Increased AI adoption can be witnessed across the board but is especially strong in the energy vertical and in process industries, such as oil and gas or chemicals. The combination of high-value assets, large volumes of operational data, and processes that rely on hundreds of parameters contributes to the strong adoption in these industries. Common industrial AI applications include maintenance (e.g., predictive maintenance [PdM]), predictive quality control, the use of machine vision for fault detection, AI-optimized inventory management, and AI-based production planning and optimization.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

Robo-taxis are headed for a street near you

MIT Technology Review

In the coming years, mobility solutions--or how we get from point A to point B--will bridge the gap between ground and air transportation--yes, that means flying cars. Technological advancements are transforming mobility for people and, leading to unprecedented change. Nand Kochhar, vice president of automotive and transportation for Siemens Software says this transformation extends beyond transportation to society in general. "The future of mobility is going to be multimodal to meet consumer demands, to offer a holistic experience in a frictionless way, which offers comfort, convenience, and safety to the end consumer." Thinking about transportation differently is part of a bigger trend, Kochhar notes: "Look at few other trends like sustainability and emissions, which are not just a challenge for the automotive industry but to society as a whole." The advances in technology will have benefits beyond shipping and commute improvements--these technological advancements, Kochhar argues, are poised to drive an infrastructure paradigm shift that will bring newfound autonomy to those who, today, aren't able to get around by themselves. Kochhar explains, "Just imagine people in our own families who are in that stage where they're not able to drive today. Now, you're able to provide them freedom." Laurel Ruma: From Technology Review, I'm Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is the future of mobility. In 2011, Marc Andreessen famously said, "Software is eating the world."

Why Every Home Will Be Voice Controlled in 10 Years


I was inspired by a recent twitter conversation to write this article stating what I consider a fairly obvious fact, but clearly not everyone agrees. So let's talk about why I believe voice control will be prevalent in every single home within the next 10 years. To start, let's talk about one of the biggest objections I hear: voice control isn't always the best method of control. Sometimes you have company over, there's a lot of background noise, you're worried about the system hearing you correctly, etc, etc. This argument often suggests switches and keypads and remotes are more natural for some people.

Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs Artificial Intelligence

Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we examine how offline training can be leveraged to minimize data costs (accelerate convergence) and program implementation costs. We present two approaches to doing so: pretraining our model to warm start the experiment with simulated tasks, and using a planning model trained to simulate the real world's rewards to the agent. We present results that demonstrate the utility of offline reinforcement learning to efficient price-setting in the energy demand response problem.

Why Artificial Intelligence Isn't Intelligent


This might seem like a purely academic debate. Whatever we call it, surely what matters most about "AI" is the way it is already transforming what can seem like almost every industry on earth? Not to mention the potential it has to displace millions of workers in trades ranging from white to blue collar, from the back office to trucking? And yet, across the fields it is disrupting or supposed to disrupt, AI has fallen short of many of the promises made by some of its most vocal advocates--from the disappointment of IBM's Watson to the forever-moving target date for the arrival of fully self-driving vehicles. And--ask any branding or marketing expert--names, in particular, carry weight.

Why Artificial Intelligence isn't intelligent


"In a certain sense I think that artificial intelligence is a bad name for what it is we're doing here," says Kevin Scott, chief technology officer of Microsoft. "As soon as you utter the words'artificial intelligence' to an intelligent human being, they start making associations about their own intelligence, about what's easy and hard for them, and they superimpose those expectations onto these software systems." This might seem like a purely academic debate. Whatever we call it, surely what matters most about "AI" is the way it is already transforming what can seem like almost every industry on earth? Not to mention the potential it has to displace millions of workers in trades ranging from white to blue collar, from the back office to trucking?