John Keefe is the investigations editor at Quartz and leads the Quartz AI Studio. Keefe also teaches classes on product prototyping, design, and development at the Craig Newmark Graduate School of Journalism at CUNY and runs a product tinkering company called Really Good Smarts LLC. Before joining Quartz, he was Senior Editor for Data News at public radio station WNYC, leading a team of journalists who specialize in data reporting, coding, and design for visualizations and investigations. He was previously WNYC's news director for nearly a decade. A self-described "professional beginner," Keefe is the author of Family Projects for Smart Objects: Tabletop Projects That Respond to Your World from Maker Media, which grew from his effort to make something new every week for a year.

"A Breakthrough in machine learning would be worth ten Microsofts" – Bill Gates Yes, due to many obvious reasons, Bill Gates is right and we will prove it in this blog. Though the term, machine learning was tossed by Arthur Samuel in 1959 while working at the IBM, the actual serviceability of it started popping up after 2010. So, Dave Waters compares the advancement of machine learning with the baby – "A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning." Recently, machine learning market has witnessed exceptional growth and it is estimated to reach $21 billion by 2024.

Natural gas is an important energy source for much of our industrial, heating and electricity needs. The price of natural gas can fluctuate greatly. I made a time series analysis with external regressors to investigate how well modeling could forecast the price of natural gas. Using data from the US Energy Information Administration, I acquired monthly pricing data for Natural Gas from January of 1990 until present. I also acquired data on a number of related energy features.

To increase the accuracy of the analysis, deep sequencing data were filtered; target sequences with deep sequencing read counts below 200 and background indel frequencies above 8% were excluded as similarly performed previously (21). DNase-sequencing (DNase-seq) narrow peak data from ENCODE (36) were used to calculate chromatin accessibility as previously described (21). For each target site, 23 bases of the PAM plus protospacer sequence were aligned to the hg19 human reference genome using bowtie (41). Only the target sites that overlapped with DNase-seq narrow peaks were considered as DNase I hypersensitive target sites. We divided the Endo_Cas9 dataset into paired subsets by stratified random sampling from strata of DHS and non-DHS sites so that a similar ratio of DHS/non-DHS sites was assigned to each subset.

This article was originally published in the Autumn 2019 issue of The Record. Subscribe for FREE here to get the next issues delivered directly to your inbox. How are insurance firms using the cloud and artificial intelligence (AI) to understand customers' needs and create personalised experiences? Amy Stokes-Waters, new business sales executive, Identity Experts: People love to be independent and are increasingly turning to mobile apps for self-service across a range of products, insurance being no exception. Insurance firms are having to invest heavily in cloud technologies to keep up with consumer want for on-demand information and simplified communications in our mobile-first society.

Infosys has opened a digital innovation center in Dusseldorf, Germany, to use the local talent and shrink the IT skills gaps in Europe. The new innovation center will help Infosys to work closely with its clients in the region and support their digital transformation journey. The center will focus on cloud-based services, 5G, Artificial Intelligence, Machine Learning, Internet of Things, notes announcement. Infosys revealed that the innovation center would serve as a link between the businesses and some of the leading educational establishments in Germany to reduce the skills gap in the region. Executive Opinion Chief Operating Officer, Infosys, Pravin Rao, said, "This investment in Germany builds on Infosys' long-standing commitment to Europe, our investment in developing a highly-skilled workforce, and our focus on achieving breakthrough innovation for our clients. Dusseldorf is at the vanguard of technological innovation, with a highly skilled labor supply, productivity, social, legal, and regulatory credentials. The new center, along with our strategic academic partnerships, will help us build the next generation of technology talent."

In this episode, we hear from Brad Hayes, Assistant Professor of Computer Science at the University of Colorado Boulder, who directs the university's Collaborative AI and Robotics lab. The lab's work focuses on developing systems that can learn from and work with humans--from physical robots or machines, to software systems or decision support tools--so that together, the human and system can achieve more than each could achieve on their own. Our interviewer Audrow caught up with Dr. Hayes to discuss why collaboration may at times be preferable to full autonomy and automation, how human naration can be used to help robots learn from demonstration, and the challenges of developing collaborative systems, including the importance of shared models and safety to allow adoption of such technologies in future.

Last year Shell started using machine learning to better analyse and process that data.

The pharmaceutical industry is an example of Yin and Yang or Dark and Bright Duality. In fact, pharma is a paradox of conscience and corruption. The pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately, the business model of research-based pharmaceutical companies is under significant pressure. Their return on R&D investment has dropped to its lowest levels in decades, and their public reputation in U.S. and around the world (anti vaccine movement in Europe) is worse than ever.

The growing demand for ML/AI technologies, as well as for ML/AI talent, in the pharmaceutical industry is driving the formation of a new interdisciplinary field: data-driven drug discovery/healthcare. Consequently, there is a growing number of AI driven startups offering technology solutions for drug discovery/development. In drug development, preclinical phase (in vitro and in vivo), also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials, and during which important feasibility, iterative testing and drug safety data are collected. According to a detailed mind-map prepared by Pharma Division of Deep Knowledge Analytics (updated Q1 2019): the AI for Drug Discovery, Biomarker Development and Advanced R&D Industry Landscape counts so far 400 investors, 170 companies and 50 corporations. This article focuses only on the AI startups and the AI investors trying to overcome the above 4 challenges during design and execution of the preclinical phase.