Several years ago, Bella Abrahams, the public affairs director at Intel Israel, spoke to a group of female students from the Ben-Gurion University of the Negev. She discussed her career journey and shared her challenges and decisions along the way. She also provided some insights on how to prepare for job applications and sending resumes. After some time, Abrahams got a call. A young student on the line told her how helpful her speech was and how, by using Abrahams' tools, the young woman got the job of her dreams.

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The text message from Billy arrived on students' phones the week of final exams. "It took a lot of hard work, perseverance, and strength to get here, but you've finally made it to the other side -- the end of the semester! I wanted to take a minute and say that I am so proud of you ..." Three emoji hearts concluded the message. "Love you Billy thank you." Heart heart heart. "Thanks Billy, we did it together."

NEW DELHI: The Indian Institute of Technology (IIT), Bombay, has seen good response to its fund raising initiatives in the current academic year, with Rs26.16 crore contributed by alumni currently residing in the US. Sharad Saraf, chairman, Technocraft Group and Sudarshan Saraf, co-chairman of Technocraft Group, have given Rs15 crore to build a'Technocraft Centre for Applied Artificial Intelligence' at IIT Bombay. The donors believe that the future of technology is with the growth of Artificial Intelligence (AI) and it is necessary to expose students to AI through a dedicated AI Center, according to IIT Bombay. A campaign spearheaded by IIT Bombay Heritage Foundation in the US received good response, the institute said. "This year IIT Bombay alumni in USA have contributed to $3.6 million (Rs26.16 IIT Bombay had initiated annual fund-raising drive, 'Cherish IIT Bombay' for donors all over India and the world. It has been raising funds for various causes for the benefit of its students and faculty. According to IIT Bombay, the campaign that stands out this year is the IT hardware campaign. "As IIT Bombay moved to online classes in response to the pandemic, many of our students couldn't access the online classes as they couldn't afford the investment in IT hardware at their respective homes.

The responses flowed into the data bank of Billy Chat, a robot that uses artificial intelligence to text. Billy and other "chatbots" were launched at California State University campuses in 2019 to help students stay on track to graduate. But after students were sent home last spring at the onset of the COVID-19 pandemic, Billy evolved into more of a friend, blurring the line between artificial and real when the world turned away from human touch and connections.

The year 2020 will go down as the period when organizations responded to new risks, pivoted to new business models and accelerated their digital transformation programs in an effort to weather a lethal pandemic. In the 2020 COVID-19 epoch, going digital was no longer a business luxury but a matter of survival. Digital transformation was crucial to enabling remote working, transitioning to collaboration workflows, and to realigning operations from supply chain management through customer experiences. CIO and IT leaders no longer have to sell the business on how critical technology is to all aspects of operations. In 2020, the question was how fast could IT partner with business leaders to deliver collaboration, workflow and analytics capabilities in the cloud.

Our program is uniquely positioned to strengthen your organization's cybersecurity measures through an advanced understanding of state-of-the-art machine learning applications. This certificate is offered remotely with live online classes.

We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.

Abstract--Machine learning (ML) models are constructed by expert ML practitioners using various coding languages, in which they tune and select models hyperparameters and learning algorithms for a given problem domain. They also carefully design an objective function or loss function (often with multiple objectives) that captures the desired output for a given ML task such as classification, regression, etc. In multi-objective optimization, conflicting objectives and constraints is a major area of concern. In such problems, several competing objectives are seen for which no single optimal solution is found that satisfies all desired objectives simultaneously. In the past VA systems have allowed users to interactively construct objective functions for a classifier. In this paper, we extend this line of work by prototyping a technique to visualize multi-objective objective functions either defined in a Jupyter notebook or defined using an interactive visual interface to help users to: (1) perceive and interpret complex mathematical terms in it and (2) detect and resolve conflicting objectives. Visualization of the objective function enlightens potentially conflicting objectives that obstructs selecting correct solution(s) for the desired ML task or goal. We also present an enumeration of potential conflicts in objective specification in multi-objective objective functions for classifier selection. Furthermore, we demonstrate our approach in a VA system that helps users in specifying meaningful objective functions to a classifier by detecting and resolving conflicting objectives and constraints. Through a within-subject quantitative and qualitative user study, we present results showing that our technique helps users interactively specify meaningful objective functions by resolving potential conflicts for a classification task. In the past, researchers in visual analytics (VA) have investigated making ML model construction interactive, which means developing visual interfaces that allow users to construct ML models by interacting with graphical widgets or data marks [1], [2]. For example, the system XClusim helps biologists to interactively cluster a specified dataset [3], Hypermoval [4] and BEAMES [5] allows interactive construction of regression models, Axissketcher allows dimension reduction using simple drag-drop interactions [6]. Workflow adopted in the system CACTUS. Recently, Das et al. have demonstrated that may result into incorrectly predicting many relevant data a VA system, QUESTO [7] that facilitated interactive creation of instances, though improving the generalizability of the model. Here objective functions to solve a classification task utilising an Auto-the objective to train a model with high accuracy on a set of ML system.

There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", whereby AI algorithms and agents no longer simply learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through embodied physical interactions with their environments, whether real or simulated. Consequently, there has been substantial growth in the demand for embodied AI simulators to support a diversity of embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of artificial general intelligence, but there is no contemporary and comprehensive survey of this field. This paper comprehensively surveys state-of-the-art embodied AI simulators and research, mapping connections between these. By benchmarking nine state-of-the-art embodied AI simulators in terms of seven features, this paper aims to understand the simulators in their provision for use in embodied AI research. Finally, based upon the simulators and a pyramidal hierarchy of embodied AI research tasks, this paper surveys the main research tasks in embodied AI -- visual exploration, visual navigation and embodied question answering (QA), covering the state-of-the-art approaches, evaluation and datasets.