Artificial intelligence (AI) was by far the hottest trend discussed in sessions and across the expo floor at the world's largest radiology conference, the 2018 Radiological Society Of North America (RSNA). At the meeting in late November, there was an explosion of AI and deep learning algorithms across the expo floor. How machine learning will impact medical imaging was the key takeaway from the opening session, where examples of how AI will alter medical imaging in the near future were highlighted. Here is an overview of the types of AI software being developed and a few examples from RSNA that are specific to cardiovascular imaging. Artificial intelligence has been a growing topic in past years at RSNA, but this year several companies showed products that recently gained U.S. Food and Drug Administration (FDA) market clearance.

AI-driven Operations (AI Ops) will drive significant changes as IT organizations leverage AI, machine learning, and automation. This transition will provide opportunities for individuals to develop new skills that can enhance their careers. Experts expect the use of AI, machine learning (ML), and deep learning technologies will transform business and impact a wide range of occupations. In the previous blog in this series describing Cisco's AI Ops strategy we examined how tools can be used to self-optimize infrastructure resources. In this blog we will discuss how the adoption of AI Ops will affect people and processes.

These diseases attract a ton of research effort and funding, and for good reason: They afflict tens of millions of people each year. But there are about 7,000 known rare diseases that rarely get attention. Also called "orphan" diseases, these conditions collectively affect about 400 million worldwide and were historically neglected by the drug industry, which could not justify the costs of developing drugs to address the small number of affected patients. Salt Lake City-based Recursion Pharmaceuticals focuses on drug discovery across several therapeutic areas, including hundreds of rare diseases that currently lack treatments -- such as Sandhoff disease, an inherited, often-fatal disorder that destroys neurons in an infant's brain and spinal cord. The condition affects less than 1 in 100,000 people in Europe.

Where were you when we defeated the robots? If you missed yesterday's Starcraft II stream, we'll fill you in on the details of DeepMind's latest gaming exploits. Also, MoviePass is ready to try unlimited tickets again, and we've got another new phone with (almost) no ports. Rise and fly, humans.DeepMind AI AlphaStar goes 10-1 against top'StarCraft II' pros After laying waste to the best Go players in the world, DeepMind has moved on to computer games. Its AI agent, named AlphaStar, managed to pick up 10 wins against StarCraft II pros TLO and MaNa in two separate five-game series that took place back in December.

DeepMind has achieved yet another milestone in the gaming world. The Google-owned artificial intelligence lab announced on Thursday that its new "AlphaStar" AI had beaten two of the world's best StarCraft II players. The pros that AlphaStar beat are Dario Wunsch and Grzego rz Komincz -- they're ranked 44th and 13th in the world respectively. The victories are being hailed as a major breakthrough by academics and AI industry watchers. While there have been several successes in video games such as Atari, Mario, Quake III Arena Capture the Flag, and Dota 2, AI systems haven't been able to deal with the complexity of StarCraft.

Sometime around 1am on a warm night in June 2017, Fei-Fei Li was sitting in her pyjamas in a Washington, DC hotel room, practising a speech she would give in a few hours. Before going to bed, Li cut a full paragraph from her notes to be sure she could reach her most important points in the short time allotted. When she woke up, the five-foot three-inch expert in artificial intelligence put on boots and a black and navy knit dress, a departure from her frequent uniform of a T-shirt and jeans. Then she took an Uber to the Rayburn House Office Building, just south of the United States Capitol. Before entering the chambers of the US House Committee on Science, Space, and Technology, she lifted her phone to snap a photo of the oversized wooden doors. Then she stepped inside the cavernous room and walked to the witness table. The hearing that morning, titled "Artificial Intelligence – With Great Power Comes Great Responsibility," included Timothy Persons, chief scientist of the Government Accountability Office, and Greg Brockman, co-founder and chief technology officer of the non-profit organisation OpenAI. But only Li, the sole woman at the table, could lay claim to a groundbreaking accomplishment in the field of AI. As the researcher who built ImageNet, a database that helps computers recognise images, she's one of a tiny group of scientists – a group perhaps small enough to fit around a kitchen table – who are responsible for AI's recent remarkable advances. That June, Li was serving as the chief artificial intelligence scientist at Google Cloud and was on leave from her position as director of the Stanford Artificial Intelligence Lab.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.

For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

Following a resounding Go victory in 2017, Alphabet's DeepMind turned to conquering StarCraft II. The game is a "grand challenge" for how successful AI agents are at complex tasks, with DeepMind and Blizzard tomorrow live streaming a demonstration of the latest progress. DeepMind and other researchers have long used games to determine if artificial intelligence can beat complex tasks that are relatively simple for humans. StarCraft is considered a "grand challenge" because it requires AI agents to "carry out and balance a number of sub-goals" in order to ultimately "beat the opponent." For example, while the objective of the game is to beat the opponent, the player must also carry out and balance a number of sub-goals, such as gathering resources or building structures.

So there's been a lot of coverage by various websites, data science gurus, and AI experts about what 2019 holds in store for us. Everywhere you look, we have new fads and concepts for the new year. This article is going to be rather different. We are going to highlight the dark horses – the trends that no one has thought about but will completely disrupt the working IT environment (for both good and bad – depends upon which side of the disruption you are on), in a significant manner. So, in order to give you a taste of what's coming up, let's go through the top four (plus 1 (bonus) five) top trends of 2019 for data science: This single innovation is going to change the way machine learning works in the real world.