Justine Bateman told Fox News Digital the increased use of artificial intelligence makes her sad because she feels it takes away from genuine human connection. Some experts have raised concerns that artificial intelligence (AI) could have its own gender gap if more women aren't involved in its development and dataset analysis. "It's not just AI, but I would say engineering as a whole," Dr. Georgianna Shea, chief technologist at the Foundation for Defense of Democracies' Center on Cyber and Technology Innovation (CCTI), told Fox News Digital. "Whenever there's any type of engineering process for anything, you don't want to end up with bias-based engineers." Adding to the debate, Melinda French Gates, co-chair of the Bill & Melinda Gates Foundation, recently said in an interview that she was concerned there was a lack of women working in the field of artificial intelligence, which she said made her nervous about potential biases in platforms.

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs. With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools. Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity.

Emerging tech such as IoT and AI is the second lowest priority for businesses next year for the third year running, second only to print services. That is outlook from a survey conducted by business tech provider Softcat, which asked its 1,600 customers across 18 different industries about their intentions for tech spending in 2020. Among those industries, real estate, private health and social work, and energy and utilities ranked big data, IoT and AI seventh and eighth priority respectively – the highest ranking by those questioned. The survey also reports that 56 percent of industries rank end user computing and mobility, the technology which allows for remote working, as their second biggest technology priority. The construction, education and healthcare industries ranked this as their number one priority, ahead of cyber security investment.

Apache Spark has become the de-facto data processing and AI engine in enterprises today due to its speed, ease of use, and sophisticated analytics. As the first Unified Analytics engine to unify data with AI, Spark allows data engineering and data science teams to simplify data preparation and model training -- enabling innovative AI use cases that leverage advanced analytics like machine learning, graph analytics, and deep learning. Join Bill Chambers, author of the book "Spark: The Definitive Guide," and Matei Zaharia, Chief Technologist and Co-founder of Databricks and the orginal creator of Apache Spark, in this Data Science Central webinar as they break down the basic operations and common functions of Spark and walk through sample use cases where Spark has helped accelerate AI innovation. In this webinar, we will cover: A gentle overview of big data and Spark Expert guidance on how to use, deploy and maintain Spark The fundamentals of monitoring, tuning, and debugging Spark An exploration into machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library Speakers: Bill Chambers, Product Manager -- Databricks Matei Zaharia, Co-founder and Chief Technologist -- Databricks Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Deep Learning is thus far a tale of two stories. The more publicized story is one of its step change performance that has astounded even longer-term term practitioners in the field. In trend prediction in IoT, and in face recognition and visual classification, Deep Learning hasn't beaten the competition so much as crushed it. The dark side of Deep Learning is the lack of a'shallow end of the pool' for IoT big data practitioners intending to dip their toes into it as an addition to their predictive analytics toolbox. Even a tentative foray into Deep Learning involves choosing between rapidly evolving (and competing) frameworks, and making non-trivial design choices (data sufficiency, data augmentation, network topology, guards against too slow or too fast learning rates to name a few) that are more art than science.

A six-week-long research accelerator, championed by NASA's Office of the Chief Technologist and hosted at the SETI Institute, is engaging young researchers from around the world to take on one of the truly existential threats to our species. The NASA Frontier Development Lab (FDL) is bringing together a team of postgraduate researchers in data analytics and planetary science and challenging them to think outside the box on the threat of asteroid impacts. The initiative is under the aegis of experts from the space agency and the SETI Institute, with deep-learning expertise contributed by NVIDIA and Autodesk. Asteroids that collide with Earth are one cosmic danger that it's now possible to mitigate. In 2013, NASA's Asteroid Grand Challenge charged participants with identifying all possible asteroid threats, and determining what to do about them. FDL co-director, James Parr, describes the concept: "Grand challenges, such as detecting and characterizing the potentially hazardous asteroids we can't see, demand ingenious new applications of emerging technologies.