Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis.
Atomwise's system only generates potential drugs the compounds created by the neural network aren't guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market.
The company believes that the speed at which it can generate trial-ready drugs based on previous safe molecular interactions is what sets it apart.
"You can take an interaction between a drug and huge biological system and you can decompose that to smaller and smaller interactive groups.
To generate the drugs, the model starts with a 3D model of a molecule for example a protein that gives a cancer cell a growth advantage.
Relatively few healthcare organizations have the resources or analytics maturity to develop their own intricate big data analytics infrastructure from scratch, but a growing number of vendors are starting to make the daunting and costly process easier by offering artificial intelligence and machine learning as a service (MLaaS).
With a potential global compound annual growth rate of 38.40 percent, the machine learning as a service market is likely to be worth close to $20 billion by 2025, says Transparency Market Research, as stakeholders across multiple industries try to integrate cutting edge analytics capabilities into their platforms and services.
Coupled with an artificial intelligence sector slated to bring more than $46 billion in revenue to vendors by 2020, MLaaS could fundamentally revolutionize the way healthcare organizations approach big data analytics by making these tools more budget-friendly for a broader range of organizations.
"Intelligent applications based on cognitive computing, artificial intelligence, and deep learning are the next wave of technology transforming how consumers and enterprises work, learn, and play," says David Schubmehl, research director, cognitive systems and content analytics at IDC, which compiled the AI report.
"These applications are being developed and implemented on cognitive/AI software platforms that offer the tools and capabilities to provide predictions, recommendations, and intelligent assistance through the use of cognitive systems, machine learning, and artificial intelligence.
"We're in a golden age of machine learning and AI," said Ralf Herbrich, Director of Machine Learning Science and Core Machine Learning at Amazon when the web giant announced the launch of the Partnership on Artificial Intelligence to Benefit People and Society.
Four in five bankers believe AI will “revolutionize” the way in which banks gather information as well as how they interact with their clients, said the Accenture Banking Technology Vision 2017 report, which surveyed more than 600 top bankers and also consulted tech industry experts and academics.
More than three-quarters of respondents to the survey believed that AI would enable more simple user interfaces, which would help banks create a more human-like customer experience.
“The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we’ve seen coming through here is the view that technology will actually help banking become a lot more personalized,” said Alan McIntyre, head of the Accenture’s banking practice and co-author of the report.
The report also found that, while the number of human interactions in bank branches or over the phone was falling and would continue to do so, the quality and importance of human contact would increase.
"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly -- and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.