Brokerages who use artificial intelligence could find opportunities to upsell based on changes in a client's lifestyle, according to a software vendor executive. The more data you feed a machine learning model and the more you train it, the better it gets, said Kevin Deveau, managing director of FICO Canada, part of San Jose, Calif.-based Fair Isaac Corp., in a recent interview. Artificial intelligence (AI) is when technology mimics human cognition such as learning from experience, identifying patterns and deriving insights, said Mark Breading, a partner with Boston-based Strategy Meets Action. Machine learning is a type of AI in which computers act without being explicitly programmed, SAS Institute Inc. notes. Bigger brokerages with enough money to invest in AI and machine learning could use those technologies to build a "360-degree view" of a customer, said Deveau, in the context of how the COVID-19 pandemic is forcing companies to change the way they operate.

Clinical voice assistant developer Suki has created a new voice platform with improved artificial intelligence. The Suki Speech Service, referred to by the company as S3, makes Suki's voice assistant faster, more accurate, and flexible enough that it could be used by professionals outside of the healthcare sector. Suki's current voice assistant is built to reduce the amount of time and energy doctors spend on administrative tasks and records. The voice assistant records, transcribes, and organizes a doctor's conversations with a patients and any notes on the case. Suki can then automatically complete the data entry necessary for Electronic Health Records (EHR).

Cognius.ai is a IVA solution company headquartered in Singapore with significant experience in Artificial Intelligence and Machine Learning technologies.

What AI is, and what it isn't. Since 2012, our Company has had a busy team of software engineers who are expert in both research and development of Artificial Intelligence, "AI", working to launch our AI workbench platform. Our researchers are led by a retired IBM Gold Consultant. Since 2013 a requirement of our researchers is that they have a PhD in a field related to AI. Our developers are highly competent senior developers.

I would like to relay Rediet Abebe's call to support local organizations. As Rediet says "These problems have been and will be here for a very long time. For the last year, I have been co organizing a theory of machine learning seminar at Harvard. Following the format of our prior Harvard/MSR/MIT theory reading group, these have been extended blackboard talks with plenty of audience interaction. Following COVID-19, the last three talks in the semester (by Moritz Hardt, Zico Kolter, and Anima Anandkumar) were given virtually.

UK-based autonomous vehicle software specialist Oxbotica has developed and deployed a "deepfake" technology that is capable of generating thousands of photo-realistic images in minutes. It said this helps to expose its autonomous vehicles to "near infinite variations" of the same situation without real-world testing of a location. Deepfaking has been used to create viral internet videos and employs deep learning artificial intelligence (AI) to generate fake photo-realistic images. The AV software firm believes that the technology will make the vehicles of tomorrow smarter and safer, and help to accelerate the shift to autonomy. The algorithms used in the technology allow Oxbotica to reproduce the same scene in poor weather or adverse conditions, and subject its vehicles to rare occurrences.

Observing electronic ecosystems and value chains change over time is fascinating. For instance, the design chain for mobile devices fundamentally changed over the past two decades with waves of disaggregation and aggregation. Today, the area of computing and data centers is amid tectonic shifts and transformation, with the combination of hyperscale, networking, artificial intelligence (AI), and machine learning (ML) fundamentally re-shuffling value creation. Back in 2002, Grant Martin and I wrote "A Design Chain for Embedded Systems" for IEEE Computer. We described the embedded SoC provider-integrator design chain and argued that "what used to be a vertically integrated process within each product company has become significantly fragmented. Platform-based design can accelerate the flow in this chain."

When you think of artificial intelligence, you might think of examples from science fiction like Terminator or The Matrix. Modern AI exists without the limitations that you see in movies, operating on everything from the smartphones in your pocket to the website that uses machine learning to track COVID-19. Artificial intelligence was designed to solve for specific tasks and has applications that can dramatically impact the bottom line in your restaurant, from automating features to the burger-flipping robots of the future. As we look into an uncertain future, the opportunities to employ artificial intelligence to automate your restaurant is an excellent way to put AI to work for you. As the coronavirus has taught us, a robust communications network is critical to ensuring that everyone is kept aware of changes.

Since AI involves interactions between machines and humans--rather than just the former replacing the latter--'explainable AI' is a new challenge. Intelligent systems, based on machine learning, are penetrating many aspects of our society. They span a large variety of applications--from the seemingly harmless automation of micro-tasks, such as the suggestion of synonymous phrases in text editors, to more contestable uses, such as in jail-or-release decisions, anticipating child-services interventions, predictive policing and many others. Researchers have shown that for some tasks, such as lung-cancer screening, intelligent systems are capable of outperforming humans. In many other cases, however, they have not lived up to exaggerated expectations.

Typically this requires casting the original deterministic physics-based model into a probabilistic framework where inputs or control variables (CVs) are treated as random variables with probability distributions derived from available experimental data, manufacturing constraints, design criteria, expert judgment, and/or other domain knowledge (e.g., see [1]). Running the physics-based model with CVs sampled according to these distributions yields corresponding realizations of the system response as characterized by quantities of interest (QoIs). Analysis of the uncertainty propagation from the CVs to the QoIs informs decision-making, e.g., it informs engineering decisions aimed at improving the quality and reliability of designed products and helps identify potential risks at early stages in the design and manufacturing process. Quantitatively assessing uncertainty propagation presents a fundamental challenge due to the computational cost of the underlying physics-based model. Even for a low number of CVs and QoIs, uncertainty quantification (UQ) for, e.g., accelerating the simulation-aided design of multiscale systems and data-centric engineering tasks more generally ([2]), requires a large number of repeated observations of QoIs to achieve a high degree of confidence in such an analysis. The sampling cost is further exacerbated in real-world applications where distributions on QoIs are typically non-Gaussian, skewed, and/or mutually correlated, and therefore need to be characterized by their full probability density function (PDF) rather than through summary statistics such as mean and variance. The computational cost of nonparametric methods to estimate these densities can become prohibitively high when using a fully-featured physics-based model to compute each sample. One approach to alleviate the computational burden is to derive a cheaper-to-compute surrogate for the physicsbased model's response enabling much faster generation of output data and thus overcoming computational bottlenecks.