The hype machine for AI and machine learning has been going full throttle, and one can be forgiven for thinking that every organization from the mega-techs to the corner store is turning over processes or decisions to AI. If you're still stuck trying to figure out how AI and machine learning can fit into your operations, don't worry -- so is everyone else, actually. Companies may be increasing their investments in machine learning and machine learning development, but, for the most part, are still in the early learning stages. That's the major takeaway from a survey of 750 technology managers and professionals released by Algorithmia, which specializes in such things. Survey respondents represent companies that are actively engaged in building machine learning lifecycles.

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Machine learning is a form of narrow AI used to classify data and make predictions. Supervised machine learning classifies orthopaedic images comparably to humans. Neural networks identify successful exercise performance with 99.4% accuracy. Machine learning can predict successful performance of a single leg squat exercise. Unsupervised learning finds patterns in data without training; used in data mining.

Recent research from SAP and Oxford Economics demonstrated CFOs' strategic initiatives are taking a more active role in the direction of their businesses, rather than operating within a siloed financial function. The report showed that 88% respondents said CFO's are increasingly involved in the strategic decisions of their organisations.

It's been estimated that China's government has detained as many as a million members of the country's Muslim population in so-called "re-education camps," in part of a campaign that has alarmed human rights activists across the world. This week, drawing on 403 pages of leaked government documents, The New York Times published new details of how the ongoing crackdown took shape under Chinese President Xi Jinping and other leadership in the Communist Party of China, how government workers who resisted the plan were sidelined, and what officials were instructed to tell young people whose families had been detained. "They're in a training school set up by the government to undergo collective systematic training, study and instruction," the talking points read, adding, "You have nothing to worry about." The Chinese government's campaign against those it says have been exposed to extremism is centered on an autonomous region, Xinjiang, where nearly half of the 25 million residents are a Muslim people called the Uighurs. Earlier in November, a FRONTLINE documentary called In the Age of AI examined how, as part of its crackdown involving the Uighurs, China's government has made Xinjiang a test project for forms of extreme digital surveillance.

Discussions on the interplay of humans and Artificial Intelligence tend to pose the issue in the language of opposition. However, according to the thinking of evolutionary biologist Richard Dawkins, tools such as AI can be better thought of as part of our extended phenotype. A phenotype refers to the observable characteristic of an organism, and the idea of the extended phenotype is that this should not be limited to biological processes, but include all of the effects that the genes have upon their environment, both internally and externally. We are used to defining ourselves strictly by the space we occupy in the physical world. The numbers of non-human cells that occupy our own body outnumber the number of human cells and vast colonies of bacteria swarm within the interior of our digestive tract. Author Robert Svoboda compares the human to a minority government ruling a primarily non-human population.

A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone and many other places. They have recently undergone massive transformation and run on operating systems that are fuelled by artificial intelligence (AI). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using AI to make sense of the situation.

Researchers at Washington University in St. Louis are developing a new imaging technique that can reportedly provide accurate, real-time, computer-aided diagnosis of colorectal cancer. Using deep learning, a type of machine learning, the team used the technique on more than 26,000 individual frames of imaging data from colorectal tissue samples to determine the method's accuracy. Compared with pathology reports, they were able to identify tumors with 100% accuracy in this pilot study. This is the first report ("Real-time colorectal cancer diagnosis using PR-OCT with deep learning") using this type of imaging combined with machine learning to distinguish healthy colorectal tissue from precancerous polyps and cancerous tissue. Results appear in advance online publication in the journal Theranostics.

The speed of AI progress is accelerating at breakneck speed. This year, we saw some very cool industry breakthroughs with AI - and we're excited to share them with you. The objective of Artificial Intelligence is to enhance the ability of machines to process copious amounts of data and by doing so, automate a broad range of tasks. Despite this benign objective, AI also lends itself to nefarious ends, and in our increasingly digitising world, AI has the potential to cause an unprecedented degree of damage. In the same way that human intelligence can be used towards positive, benign or detrimental purposes, so can artificial intelligence.

The fine folks at Microsoft have put together an excellent Single Page Cheatsheet for Azure Machine Learning Algorithms. It is very helpful for Azure, but it is also helpful for understanding when and why to use a particular algorithm. Start in the large blue box, "What do you want to do?" Then follow the lines out to match what you would like to solve. For example, maybe you have some data and you want to predict whether a customer will purchase or not. You want to predict "Will Purchase" or "Will Not Purchase".