The COVID19 outbreak has changed the world faster than anyone could have imagined. Forced isolation has shifted meetings and activities to go on through web collaboration tools. Arguably most importantly, the virus has ground the entire economy to a halt. For some industries (travel, retail, food services, fitness), unemployment is already rampant, and for more remote-friendly industries (tech, media), work may go on, but sales are slowed or frozen, and receivables less likely to convert to revenue than ever. The downsides of the virus are easy to see – but business and public sector leaders are forced to think about the silver lining.

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders. Business leaders, managers, and consultants with an eye on AI aren't just trying to learn what AI can do, they're trying to discover ways to gain an AI advantage. For this reason, discovering AI trends can be particularly important. Most of the work that we do with our AI Capability Map services is about finding trends in quantitative data – which requires hundreds of hours of expert research, and established frameworks for interpreting and categorizing data for insight.

Kasisto has an AI platform called KAI that they claim can help financial institutions create chatbots for their customers to ask questions and use to make payments and review account details. The company also claims KAI chatbots can help users manage their funds from multiple accounts. The software can send a chatbot's conversation to a human customer service employee in cases where it cannot solve a customer's problem. The company also claims to have a deep learning tool for business banking chatbots that helps train new machine learning models, however, they do not offer a detailed explanation of how multiple neural networks would work to make a chatbot more effective These chatbots can purportedly converse with customers about financial tasks such as applying for credit cards, product discovery and managing funds. They can also fulfill some requests directly within the chatbot interface.

From the soaring stock price of NVIDIA, to the cutting-edge developments at Facebook and Google, AI hardware is a hot topic. We set out to learn more about what executives should know about the coming developments in AI hardware – and how it might impact different industries and sectors. In the subsections of the article that follows, we will delve deeper into these questions, highlighting the key insights from the professionals we corresponded with. AI software has always received the lion's share of attention, but as the computational resources needed to process this software soar exponentially, a new generation of AI chips is coming into being. Developments in AI hardware will take the spotlight, as companies converge at the AI Hardware Summit September.

It's been over a month since our last major artificial intelligence consensus (which covered 33 AI researcher perspectives on the 20-year risks of AI), and we decided that this time around, we'd speak with AI executives directly about the future of artificial intelligence and machine learning in consumer tech. The media is awash with buzz-stories about autonomous vehicles, speech recognition, robotics, and more, but it seems difficult to glean a perspective on which consumer AI tech trends are likely to make the biggest impact in the coming 5 years. While there's certainly no crystal ball, our preference as a market research firm is to combine research and news analysis with a strong consensus from dozens of experts in the field. When it comes to AI for consumer tech, we decided to ask executives and founders of artificial intelligence companies what they believe to be the most important AI consumer tech trends in the next half decade. You can see a full list of the answers to our "AI Consumer Tech Trends" below in our large infographic.

This author account is for Partner Content with Emerj advertising and content partnership clients. In 2018, James Kobielus wrote an article on the AI market's shift to workload-optimized hardware platforms, in which he proposed: Workload-optimized hardware/software platforms will find a clear niche for on-premises deployment in enterprises' AI development shops. Before long, no enterprise data lake will be complete without pre-optimized platforms for one or more of the core AI workloads: data ingest and preparation, data modeling and training, and data deployment and operationalization. We are seeing Kobielus' words come true. In the past year, nearly 100 companies have announced some sort of AI-optimized IP, chip, or system optimized, primarily for inferencing workloads but also for training.

The great power nations that master the use of artificial intelligence are likely to gain a tremendous military and economic benefits from the technology. The United States benefitted greatly from a relatively fast adoption of the internet, and many of its most powerful companies today are the global giants of the internet age. I believe these to be fatal assumptions. The decade ahead will make it clear that the United States must, as it has in the past, earn its prosperity and its technological leadership – something that many Americans now take completely for granted. This will involve a focus on the competitiveness of the US economy – and a willingness to continually earn its place in the international order.

Claims processing and underwriting are two areas of insurance that could benefit from AI-based information extraction/document search software. That said, neither are developed use-cases for AI in insurance right now. This will likely change over time as AI becomes more accessible to businesses, perhaps with autoML or a shift in the culture of innovation at older enterprises. At that point, AI use-cases in insurance will likely move from the cost-saving benefits of document search applications to more complex machine learning systems that involve document search, machine vision, and prescriptive analytics, allowing for capabilities that drive growth, such as tailor-made insurance policies.

Deepfakes have made their way into the radar of much of the First World. As with many technology phenomena, deepfakes have their origins in pornography – editing (the Reddit page that originally popularized deepfakes was banned in early 2018). In April of this year, I was asked by UNICRI (the crime and justice wing of the UN) to present the risks and opportunities of deepfakes and programmatically generated content at United Nations headquarters for a convening titled: Artificial Intelligence and Robotics: Reshaping the Future of Crime, Terrorism, and Security. Instead of speaking about the topic, we decided it would be better to showcase the technology to the UN, IGO, and law enforcement leaders attending the event. So we took a video of UNICRI Director Ms. Bettina Tucci Bartsiotas, and created a deepfake, altering her words and statements by using a model of her face on another person.

Radhika previously worked in content marketing at three technology firms, and graduated from Sri Krishna College Of Engineering And Technology with a degree in Information Technology. According to the National Institute of Mental Health, the United States is currently battling a mental health epidemic. One in every five Americans struggles with mental illness in one form or another. According to the Center for Workplace Mental Health founded by the American Psychiatric Association, up to 7% of full-time workers in the U.S. suffer from major depressive disorder, the economic cost of which is estimated to be $210.5 billion per year. When compared to other developed nations, traditional healthcare in the U.S. is notoriously costly; mental healthcare, even more so.