Zillow, an online marketplace that facilitates the buying, selling, renting, financing, and remodeling of homes, employs lots of AI technologies to do things like estimate home prices. But the output of AI systems like these can be opaque, creating a "black box" problem where practitioners and customers can't audit the systems properly. Without transparency, serious problems like algorithmic bias can persist undetected, and trust in the models becomes impossible. For obvious ethical reasons, this is why explainable AI (XAI) is so crucial to the creation and deployment of AI systems, but pragmatically, it's also key to the success of AI-powered products and services from companies like Zillow. David Fagnan, director of applied science on the Zillow Offers team, discussed with VentureBeat how and why XAI is indispensable for the company.

PUNE: Observe.ai, an artificial intelligence powered voice customer service firm said it had raised $26 million in Series A financing, led by Scale Venture Partners, with participation from Nexus Venture Partners, Steadview Capital, 01 Advisors, and Emergent Ventures. The funding will be used to expand its teams in the United States and India, and accelerate product development. Further, Andy Vitus, partner at Scale, will be joining Observe.AI's board. This brings the company's total funding to $34 million, having earlier raised $8 million from Nexus Venture Partners. "Legacy speech analytics systems are simply not meeting the needs of the world's top brands," said Swapnil Jain, CEO, Observe.AI.

Greg LaBlanc has been teaching at the Haas School of Business and Berkeley Law since 2005. He teaches primarily in the areas of finance and strategy in the MBA and MFE programs and in Executive Education. He has also worked in competitive intelligence and litigation consulting and has advised consulting teams in finance, marketing, and strategy. His research interests lie at the intersection of law, finance, and psychology, in the area of business strategy and risk management. He is the recipient of teaching awards including the Earl F. Cheit Award for Outstanding Teaching, 2009; and the Haas EWMBA Graduate Instructor of the year, 2004-2005.

Growth in this market is mainly driven by growing number of cross-industry collaborations and partnerships, the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications & services, and the impending patent expiry of blockbuster drugs. On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market. The immuno-oncology segment accounted for the largest share in 2019. Based on application, the artificial intelligence in the drug discovery market is segmented into neurodegenerative diseases, immuno-oncology, cardiovascular disease, metabolic diseases, and other applications. The immuno-oncology segment accounted for the largest share of 44.6% of the AI in the drug discovery market in 2018, owing to the increasing demand for effective cancer drugs.

Artificial Intelligence (AI), defined as a system's ability to correctly interpret external data, to learn from such data, and to use what it learns to achieve specific goals and tasks through flexible adaptation, will doubtlessly lead to a multitude of changes in today's world. Given the significant uncertainties around artificial intelligence, it is not astonishing that the opinions on it reach from highly euphoric like the vision of best-selling author Raymond Kurzweil to straight out alarmist as frequently expressed by tech entrepreneur and investor Elon Musk. Theoretical physicist Stephen Hawking called AI "either the best, or the worst thing, ever happen to humanity". For at least three reasons, ethics as well as a human approach to AI and its progress are an absolute imperative. First, an AI system will do whatever assignment it has been asked to do, independent of whether these tasks are illegal, unethical, or simply produce negative outcomes.

Long wait times for patients can be the undoing of a large clinic or a hospital system. According to a recent survey, 84% of patients consider a reasonable wait somewhat or very important to a quality experience, and 30% say they've left a medical appointment because of an inordinately long wait. And a whopping 20% of patients say they'd consider changing providers due to wait times alone. That's why Mohan Giridharadas, a Stanford graduate and former McKinsey and Company partner, founded Charlotte, North Carolina- and Santa Clara-based LeanTaaS. It's a predictive analytics company that taps data science to "radically" improve health care provider performance, using cloud-based solutions to solve problems like resource utilization.

It is no secret that artificial intelligence is being tested in clinical settings around the world, but is it a realistic ambition to apply sophisticated algorithms to outdated healthcare systems? We speak to Matej Adam about the research being carried out by IBM. We invested in artificial intelligence by developing an AI platform, Watson, named after one of the founders of IBM. When we were developing the technology, it was evident from the beginning that its use in healthcare would make sense for multiple reasons. One reason is that AI can help to sift through a lot of unstructured data of different types and formats.

Data and AI need to be unified: the best AI applications require massive amounts of constantly updated training data to build state-of-the-art models. So far, Apache Spark is the only unified analytics engine that combines large-scale data processing with state-of-the-art machine learning and AI algorithms. Combining Spark AI topics, this conference is a unique "one-stop shop" for developers, data scientists, and tech executives seeking to apply the best tools in data and AI to build innovative products. The sessions and training at this conference will cover data engineering and data science content, along with best practices for productionizing AI: keeping training data fresh with stream processing, quality monitoring, testing, and serving models at a massive scale. The conference will also include deep-dive sessions on popular software frameworks--e.g., Delta Lake, MLflow, TensorFlow, SciKit-Learn, Keras, PyTorch, DeepLearning4J, BigDL, and deep learning pipelines.

Older people in Japan are strapping on exoskeletons to help meet the physical demands of their jobs and remain in the workforce for longer. Japan's population is rapidly ageing, with a record 28 per cent of people aged 65 or older. This has led to a shortage of workers, particularly in manual labour industries like construction, manufacturing and farming.

Cimon, the world's first AI-powered astronaut assistant, has returned to the International Space Station--this time with a heightened ability to analyse human emotion. The objective, according to the researchers who fine-tuned the robot after its first successful mission aboard the spacecraft, is to transform Cimon from a scientific assistant into "an empathetic companion". As was the original Cimon, which spent 14 months in space, the new and improved Cimon-2 is a joint project by IBM, Airbus and the German Aerospace Center (DLR). Cimon-1 returned to Earth from the International Space Station (ISS) in August. Cimon-2 is heading back to the space station on a SpaceX rocket that launched 4 December from Kennedy Space Center in Cape Canaveral, Florida.