Ask most people outside academia or Silicon Valley what comes to mind when they hear the term "machine learning" and you're likely to get a response that involves a movie like "The Matrix" or "Ex Machina." You're less likely to hear how it's a great tool for fraud detection or supply chain optimization, and that's too bad. Machine learning has a tremendous range of business applications, from optimizing data centers to predicting fine wine price changes to retail market basket analysis. With that in mind, I hope to cut through the science fiction clutter and misconceptions so you can consider how machine learning relates to your business. Many have heard about Andrew Ng's popular graduate level machine learning course at Stanford, now available on Coursera.

The applications of AI are moving beyond predictive analytics and machine learning, as more innovative use-cases for anticipating the needs of customers, employees and partners emerge. This growth is being accelerated by billion dollar investments that technology companies are making in order to commercialise applications of AI. Secondly, since it is still an evolving area, there is a shortage of the skills required to design, build and integrate systems with AI capabilities. These changes can naturally create uncertainty amongst the workforce, so AI technology implementations require far greater organisational change management than traditional IT projects.

Digital technologies are fuelling a seismic shift in the modern business landscape, driven by changing customer expectations and mounting competition from new and nimble'born digital' companies. This has given rise to a new breed of organisations; the 21st Century Enterprise, which are being forced to rethink traditional market approaches and become more service-centric in order to remain relevant. To make this transition successfully, businesses must have the ability to not only respond, but also pre-empt the needs of customers, partners and employees. That's a pretty big ask given that most of us lack any psychic abilities, but the growing maturity of Artificial Intelligence (AI) is starting to make this goal much more achievable. Machine learning and cognitive computing are paving the way for automation capabilities that are hugely impacting business technology and ushering in a new generation of intelligent, outcome-based services, which lie at the heart of the 21st Century Enterprise. As the hype around these technologies continues to intensify and organisations start to experiment and explore the potential of AI even further, we're likely to see widespread disruption across businesses and industries.

Text: Rahul Sachitanand, ET Bureau A clutch of ventures has made some early progress in the rapidly emerging field of artificial intelligence, but runaway commercial success may take some time.

Headlines on artificial intelligence typically belong to globally renowned tech giants, such as Google, Facebook, and Apple. Asia's AI and machine learning startups, on the other hand, rarely make it onto the world stage. "I want a world-competitive AI company coming from Asia," says Tak Lo, the Managing Director of Zeroth, a funding program for early-stage AI and machine learning startups in Asia. "[Part] of that is taking Asian startups and being able to support them from a global network of entrepreneurs." Zeroth, which launched on July 1st, is a three month funding program that offers mentorship and 20,000 USD in capital to early-stage startups.

From harnessing artificial intelligence to understanding our origins, a panel of distinguished scientists outlined the grand challenges for science in the 21st century. Held at Nanyang Technological University and moderated by independent writer and lecturer Tor Norretranders, the panel session comprised Sydney Brenner, Nobel laureate in Physiology or Medicine; W. Brian Arthur, external professor at the Santa Fe Institute; Astronomer Royal Martin Rees; Terrence Sejnowski, Francis Crick Professor at the Salk Institute for Biological Studies; and Eörs Szathmáry, director of the Parmenides Center for the Conceptual Foundations of Science. The scientific industry has seen a marked shift towards industrial science--or science that directly benefits the economy--and away from basic research, said Sydney Brenner, a senior fellow at the Agency for Science, Technology and Research in Singapore. He lamented that scientists today lack a crucial truth-seeking mentality, by accepting the results of published research without challenging assumptions. In science, where research is built upon research--which potentially leads to an accumulation of mistakes--the practice of critical evaluation is all the more pertinent, Brenner said.

Have you ever considered what real people actually do with the products of all of the innovation flowing from the IT industry? Sometimes, the answers are quite obvious. Shopify (NYSE:SHOP) essentially builds websites for mainly smaller businesses and facilitates credit card transactions. Silver Spring Networks (NYSE:SSNI) uses the technology of the IoT to facilitate the "intelligent city" and the "smart grid." Salesforce (NYSE:CRM) sells solutions that help companies manage both their sales people or agents and manage their customer interactions. Those and other solution sets are sold to rather obvious customers. But who actually buys and uses Big Data Appliances or Artificial Intelligence or advanced analytics. But in recent years, some financial services firms have tried to transform themselves from their roots as the providers of simple financial products used by consumers and business into what is known these days as Fintech. There are more than few such companies and perhaps at this point, there are companies with more aspirations as opposed to significant revenue. Equifax (NYSE:EFX) for many years has been known as one of the 3 major credit reporting agencies, the people who calculated your credit score using some arcane methodology. The business of selling credit scores to both business and consumers is hardly the most exciting in the world and has modest growth organic growth prospects in the US.

When Donald Trump opens his mouth, the output can seem like the work of a demented Markov chain, a poorly trained algorithm trying its hand at rhetoric. Key words--"great again," "let me tell you," "we don't win anymore"--end up strung together by exceptionally weak ligaments. His syntax seems generated on the fly, word to word, each stumbling straight into the next, bound by the barest loyalty to grammar. As only he could, Trump's brought the state of political speech down to the state of the art of machine speechwriting. This past winter, a graduate student at the Technical University of Denmark earned significant attention for the politicians he was crafting in Python.

If you'd like to read more about the awesome potential of machine learning, check out the article in the link below: The era of globalization has brought about both advantages and disadvantages. With the variety of cutting-edge technology available at the UA Lighthouse, the company wants to develop best practices for all of its products that are made domestically and abroad. To hear more about what the industry is saying about the UK's recent decision, check out the article in the link below: In the past few years, there's been a greater and greater focus on autonomous cars and commercial vehicles. At the recent Autonomous Ship Technology Symposium 2016 in Amsterdam, Rolls Royce released a white paper that details how autonomous freight ships are technically and economically feasible.

Each Sunday, we publish a list of top articles and other content related to manufacturing in areas like quality control, product development, supply chain management, sourcing, auditing and law. The world of manufacturing is constantly evolving. It never seems to stop. Just when one technology seems to take center stage, another one steps up. One big performer on the horizon is machine learning.