For the 3rd episode of our interview series with AI experts, we had a great conversation with Anna Bethke! Anna Bethke is a Principal Data Scientist focused on fair, accountable, transparent, &…

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.

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Google has sued Sonos, alleging that its new voice assistant violates seven patents related to its own Google Assistant technology, CNET has reported. It's the latest salvo in a long-running smart speaker battle between the companies, with each suing and countersuing the other following a period when they worked together. "[Sonos has] started an aggressive and misleading campaign against our products, at the expense of our shared customers," a Google spokesperson said in a statement. Sonos' Voice Control assistant arrived in June, letting users give commands with the phrase "Hey Sonos," much like Amazon's Alexa or Google Assistant. In the complaint, Google said it "worked for years with Sonos engineers on the implementation of voice recognition and voice-activated devices control in Sonos products... even providing its Google Assistant software to Sonos for many years."

AI Ethics Advisory Boards are essential but also require focus and attention, else they can fall ... [ ] apart and be untoward for all concerned. Should a business establish an AI Ethics advisory board? You might be surprised to know that this is not an easy yes-or-no answer. Before I get into the complexities underlying the pros and cons of putting in place an AI Ethics advisory board, let's make sure we are all on the same page as to what an AI Ethics advisory board consists of and why it has risen to headline-level prominence. As everyone knows, Artificial Intelligence (AI) and the practical use of AI for business activities have gone through the roof as a must-have for modern-day companies. You would be hard-pressed to argue otherwise. To some degree, the infusion of AI has made products and services better, plus at times led to lower costs associated with providing said products and services. A nifty list of efficiencies and effectiveness boosts can be potentially attributed to the sensible and appropriate application of AI.

"This is it," said SCOTUSblog media editor Katie Barlow on TikTok, posting live from outside the court. Barlow was one of the few correspondents on camera the moment the opinion was released. She was silent for a few seconds, glancing down at her phone, nodding, before looking up again and succinctly announcing the crux of it: "The Constitution does not confer a right to abortion." A reader on TikTok commented that it was hard to watch live as Barlow silently read the opinion, "to see the reality of the decision wash over you," adding: "Thank you for your work." It was a fitting way to enter the official post-Roe age: on platforms that can feel so personal to their publics, even as history unfolds.

The UK recently published its National AI strategy that outlines its vision to maintain, build on and sustain its past position within AI. The intention is to build the most "pro-innovation regulatory environment in the world" that drives AI prosperity within the nation and helps solve some of the pressing problems the UK and the world are facing currently such as climate change. The country already has had a long and exceptional history in AI -- from Alan Turing's early work-through to DeepMind's recent feats such as AlphaFold. UK comes in the top nations in terms of AI startups, research, and private capital invested within AI. In 2020, it ranked third in the world for private investment into AI companies, behind only the USA and China.

Much of the focus on the CHIPS and Science Act of 2022 has centered around strengthening the semiconductor industry, but the lion's share of the funds being allotted in the $280 billion bill targets industry and academic institutions conducting research and development on emerging technologies. The federal government will dole out around $50 billion over the next five years, mainly to chip manufacturers that will come out of the newly established CHIPS for America Fund. That leaves nearly $230 billion for vendors, researchers and developing technologies, and to create programs to educate students and train employees on technologies destined to become mainstream in the years ahead. Two such technologies prominently mentioned are quantum computing and AI. For instance, the National Science Foundation (NSF), one of the federal agencies responsible for allocating the funds, will establish a quantum education pilot program to promote a quantum information science workforce across the U.S. The agency also must produce a study on the educational challenges in creating a diverse and sustainable workforce in the quantum industry. NSF will also expand the existing Quantum User Expansion for Science and Technology program, making it easier for researchers to access government quantum computing hardware and clouds.

Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be rationally adopted to achieve different epistemic ends. Applied to XAI, different topics, stakeholders, and goals thus require different instruments. I call this the means-end account of XAI. The means-end account has a descriptive and a normative component: on the one hand, I show how the specific means-end relations give rise to a taxonomy of existing contributions to the field of XAI; on the other hand, I argue that the suitability of XAI methods can be assessed by analyzing whether they are prescribed by a given topic, stakeholder, and goal.

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.