New Delhi, India – What if Michael had died instead of Sonny in The Godfather? Or if Rose had shared the debris plank, and Jack hadn't been left to freeze in the Atlantic in Titanic*? Eros International, one of India's largest production houses, with more than 4,000 films in its catalogue, has decided to explore this sort of what-if scenario. It has re-released one of its major hits, Raanjhanaa, a 2013 romantic drama, in cinemas – but has used artificial intelligence (AI) to change its tragic end, in which the male lead dies. In the AI-altered version, Kundan (played by popular actor Dhanush), a Hindu man who has a doomed romance with a Muslim woman, lives.

Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downstream tasks. However, traditional foundation models struggle with out-of-distribution prediction and can produce outputs that are unrealistic and physically infeasible. We propose the notation of physics-guided foundation models (PGFM), that is, foundation models integrated with broad or general domain (e.g., scientific) physical knowledge applicable to a wide range of downstream tasks.

With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application domains, spanning Earth sciences, agriculture, smart cities, and public safety. Such emerging geospatial and spatiotemporal big data, coupled with recent advances in deep learning technologies, foster new opportunities to solve problems that have not been possible before. For instance, remote sensing researchers can potentially train a foundation model using Earth imagery big data for numerous land cover and land use modeling tasks. Coastal modelers can train AI surrogates to speed up numerical simulations. However, the distinctive characteristics of spatiotemporal big data pose new challenges for deep learning technologies. This vision paper introduces various types of spatiotemporal big data, discusses new research opportunities in the realm of deep learning applied to spatiotemporal big data, lists the unique challenges, and identifies several future research needs.

The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel machine learning tools to identify providers that overbill Medicare, the US federal health insurance program for elderly adults and the disabled. Using large-scale Medicare claims data, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for Medicare fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and several case studies validate our approach and findings both quantitatively and qualitatively.

So far, there have been few attempts to provide a succinct language for representing them that can also support efficient centralized and distributed privacy preserving planning. In this paper we suggest an approach for representing interacting actions succinctly and show how such a domain model can be compiled into a standard single-agent planning problem as well as to privacy preserving multi-agent planning. We test the performance of our method on a number of novel domains involving interacting actions and privacy.

Shekhar Gupta has been involved in high technology for the last 25 years, either working for fortune 100 companies or his start-ups. He had his own technology companies that he has developed successfully. Shekhar has also been engaged in different sectors including the cloud since 2001, AI/ML since 2006, and blockchain since 2011. On the other hand, he has developed multiple products and networks using these technologies in various industries such as Telecom, Govtech, EdTech, and even AgTech now. Recently, Shekhar started an animal health company called MyAnIML that uses AI to analyze an animal's face and predict a disease before any symptoms are visibly seen and the animal becomes sick and contagious.

For Bengaluru resident Srishti Shekhar, the stay-at-home situation and her last year of school made her try something she had never done before: online consultation to solve her acne issues. "I had been to two dermatologists before coming across Remedico's service on Instagram. The sign-up process was very easy and all I had to do was send a few photos and I had a treatment plan designed for me within a day," says Shekhar. Like Shekhar, thousands of Indians turned to the internet when going to a clinic seemed risky. By September, the number of internet subscribers in India had risen to 776.45 million, up from 718.74 million in December 2019--474.11

Imagine you are considering buying a long-term place with a view of mountains or ocean. For due diligence, your partner asks about flood risk in the area. FEMA maps show the place is outside the 100-year flood zones (1% annual chance). However, you have heard that climate change is making extreme events more extreme and some places have seen multiple 100-year floods within a few years. Next, you browse information about climate change and its impact.

It seems like a perfect opportunity for technology to step in and solve problems in the natural world – using tiny helicopter drones to pollinate crops as the number of bees plummets. But amid all the buzz, could this plan for'robot bees' have a sting in the tail? One scientist has suggested the robobees could be taken over by hackers – and turned into killing machines. The robots are under development in both the US and Japan, and it is hoped they could be ready for use within a decade. Under the plans, the drones would wear fuzzy'jackets' that pollen would then stick to, allowing them to pollinate flowers.