It's no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago. These complex use cases require some rigorous MLOps planning.

Beware the hype about AI systems. Although AI is powerful and generates trillions of dollars of economic value across the world, what you see in science fiction movies remains pure fiction. In this blog post, I will focus on the use of the word autonomous, the dangers of using it with stakeholders, and, in the context of customer experience, the inaccurate perception that all things can be automated, eliminating the need for interactions between employees and customers. According to the dictionary, autonomous means "having the freedom to govern itself or control its own affairs." To have autonomy is to have the freedom to exercise self-determination, to rule oneself, to make decisions in accordance with one's own goals, without external interference.

AI in medicine, particular in pediatric medicine holds much promise in taking scarce human expertise and making it available throughout rural America and to the rest of the world. Rwanda has one pediatric cardiologist in the country. In 2015, when neural network technology succeeded in building computer algorithms which were better than humans at image recognition signaled the beginning of this renaissance in AI. But, as the above chart courtesy of Jeff Dean, head of Google Brain shows, the only way to get increasing degrees of accuracy is to have more and more data. Any of you in major metro areas will see Waymo vans driving around collecting more and more data to feed autonomous driving software development.

The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively-aware AI, but when it will. What will it mean for society when machines -- and the corporations and governments they serve -- can "read" people's minds and emotions? What should developers and operators of such AI do, and what should they not do? The goal of this article is to pre-empt some of the potential implications of these developments, and propose a set of guidelines for evaluating the (moral and) ethical consequences of affectively-aware AI, in order to guide researchers, industry professionals, and policy-makers. We propose a multi-stakeholder analysis framework that separates the ethical responsibilities of AI Developers vis-\`a-vis the entities that deploy such AI -- which we term Operators. Our analysis produces two pillars that clarify the responsibilities of each of these stakeholders: Provable Beneficence, which rests on proving the effectiveness of the AI, and Responsible Stewardship, which governs responsible collection, use, and storage of data and the decisions made from such data. We end with recommendations for researchers, developers, operators, as well as regulators and law-makers.

We propose a models with lower latency and power consumption while Bayesian approach to trend detection in which also ensuring privacy. However, as there is no access to the probability of a keyword being trendy, given actual data from participating devices, it poses a problem a dataset, is computed via Bayes' Theorem; the for the analysis of federated learning models. Federated analytics probability of a dataset, given that a keyword (Ramage & Mazzocchi) is a practice introduced to is trendy, is computed through secure aggregation solve this problem. It uses the same infrastructure as federated of such conditional probabilities over local learning to aggregate the computed metric by each datasets of users. We propose a protocol, named participating device using local data and shared models. SAFE, for Bayesian federated analytics that offers Federated analytics has already gone beyond just measuring sufficient privacy for production-grade use the quality metric to computing descriptive statistics cases and reduces the computational burden of (Ramage & Mazzocchi; Zhu et al., 2020), generating synthetic users and an aggregator. We illustrate this approach data (Xin et al., 2020; Chaulwar, 2020) and learning with a trend detection experiment and discuss new insights (Chen et al., 2019). These methods are generally how this approach could be extended further combined with secure aggregation protocols to ensure to make it production-ready.

Explainability of AI systems is critical for users to take informed actions and hold systems accountable. While "opening the opaque box" is important, understanding who opens the box can govern if the Human-AI interaction is effective. In this paper, we conduct a mixed-methods study of how two different groups of whos--people with and without a background in AI--perceive different types of AI explanations. These groups were chosen to look at how disparities in AI backgrounds can exacerbate the creator-consumer gap. We quantitatively share what the perceptions are along five dimensions: confidence, intelligence, understandability, second chance, and friendliness. Qualitatively, we highlight how the AI background influences each group's interpretations and elucidate why the differences might exist through the lenses of appropriation and cognitive heuristics. We find that (1) both groups had unwarranted faith in numbers, to different extents and for different reasons, (2) each group found explanatory values in different explanations that went beyond the usage we designed them for, and (3) each group had different requirements of what counts as humanlike explanations. Using our findings, we discuss potential negative consequences such as harmful manipulation of user trust and propose design interventions to mitigate them. By bringing conscious awareness to how and why AI backgrounds shape perceptions of potential creators and consumers in XAI, our work takes a formative step in advancing a pluralistic Human-centered Explainable AI discourse.

Although learning from data is effective and has achieved significant milestones, it has many challenges and limitations. Learning from data starts from observations and then proceeds to broader generalizations. This framework is controversial in science, yet it has achieved remarkable engineering successes. This paper reflects on some epistemological issues and some of the limitations of the knowledge discovered in data. The document discusses the common perception that getting more data is the key to achieving better machine learning models from theoretical and practical perspectives. The paper sheds some light on the shortcomings of using generic mathematical theories to describe the process. It further highlights the need for theories specialized in learning from data. While more data leverages the performance of machine learning models in general, the relation in practice is shown to be logarithmic at its best; After a specific limit, more data stabilize or degrade the machine learning models. Recent work in reinforcement learning showed that the trend is shifting away from data-oriented approaches and relying more on algorithms. The paper concludes that learning from data is hindered by many limitations. Hence an approach that has an intensional orientation is needed.

While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines. We argue that a main underlying cause for this is that AI developers face a social dilemma in AI development ethics, preventing the widespread adaptation of ethical best practices. We define the social dilemma for AI development and describe why the current crisis in AI development ethics cannot be solved without relieving AI developers of their social dilemma. We argue that AI development must be professionalised to overcome the social dilemma, and discuss how medicine can be used as a template in this process.

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability.

I was fortunate enough to be given early access to GitHub's new "AI pair programmer," Copilot, which generates quite a stir. My early ideas and experiences with this tool are shared in this blog post. It's made me shout "wow" a couple of times in the last few hours, which isn't something you'd expect from your developer tools! However, there are some real-world limits to this tool right now, which I'll go through in this article. In summary: Copilot appears out of nowhere, interrupting my flow.