Today, Mark takes on that ongoing tension of artificial intelligence and jobs, looking at how things are looking for jobs in Government administration, healthcare, and education. Public sector jobs could be under threat by robots in the next 15 years, according to a new report by The Reform think-tank. As technology is advancing at an unprecedented rate, the report explains, there is a growing opportunity to improve efficiency to the tune of billions of pounds by engaging robots to undertake certain tasks. The tasks that these bots can undertake, however, may have ramifications for civil servants' jobs. The co-author of the report, Alexander Hitchcock, has put this caveat on The Reform's think-tank: "Such a rapid advance in the use of technology may seem controversial, and any job losses must be handled sensitively. But the result will be public services that are better, safer, smarter, and more affordable."

Artificial General Intelligence (AGI) is an emerging field aiming at the building of "thinking machines"; that is, general-purpose systems with intelligence comparable to that of the human mind. What is currently labeled'artificial intelligence' is largely narrow automated knowledge work, lacking the flexibility and adaptability seen in animal intelligence. The pursuit of AGI begins at a foundational level, asking fundamental questions about models of cognition, knowledge acquisition, making choices through reason, thinking and conceiving the world in adaptive and intuitive ways. You emphasize the importance and value of "artificial understanding" of human language. What are the current "natural language processing" systems (Siri, Alexa, chat-bots, etc.) doing and how does this differ from what AGI is striving for w/regards to working with language? None of the language understanding systems go beyond identifying words correctly in context; this is a major step forward, but not enough.

Something is wrong in the way ML is being taught to developers. Most ML teachers like to explain how different learning algorithms work and spend tons of time on that. For a beginner who wants to start using ML, being able to choose an algorithm and set parameters looks like the #1 barrier to entry, and knowing how the different techniques work seems to be a key requirement to remove that barrier. Many practitioners argue however that you only need one technique to get started: random forests. Other techniques may sometimes outperform them, but in general, random forests are the most likely to perform best on a variety of problems (see Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?), which makes them more than enough for a developer just getting started with ML.

Pity the 34 staff of Fukoku Mutual Life Insurance in Japan, diligently calculating insurance payouts and brutally replaced by an AI system. If you believe the reports from January, the AI revolution is here. In my opinion, the goings-on in Japan cannot possibly qualify as AI, but, in order to explain why, I have to explain what I think AI means. In one way, this attempt will be doomed to failure because there is no unified definition of AI. But I can, hopefully, provide a framework of understanding about the topic that may help.

Now, scientists have demonstrated an automated system that uses artificial intelligence and cutting-edge image processing to rapidly examine large numbers of individual Caenorhabditis elegans, a species of nematode widely used in biological research. Beyond replacing existing manual examination steps using microfluidics and automated hardware, the system's ability to detect subtle differences from worm-to-worm -- without human intervention -- can identify genetic mutations that might not have been detected otherwise. By allowing thousands of worms to be examined autonomously in a fraction of the time required for conventional manual screening, the technique could change the way that high throughput genetic screening is carried out using C. elegans. Details of the research were scheduled to be reported August 19th in the advance online publication of the journal Nature Methods. The research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Alfred P. Sloan Foundation.

This work, carried out by Ardian Jusufi, Robert Full and colleagues at the University of California, Berkeley, explains how large-tailed animals can turn themselves right side up while falling through the air. It could also help engineers to design air- or land-based robots with better stability. "It is not immediately obvious which mechanism an animal will use to accomplish aerial righting and recover from falling in an upside-down posture. Depending on body size, morphology and mass distribution there are multiple strategies for animals to execute this behavior," said Ardian Jusufi, lead author of the study. Lizards in their natural environment encounter various situations where they could fall.

But that could soon change: Researchers at MIT and the Georgia Institute of Technology have developed a way to automate the process of finding and recording information from neurons in the living brain. The researchers have shown that a robotic arm guided by a cell-detecting computer algorithm can identify and record from neurons in the living mouse brain with better accuracy and speed than a human experimenter. The new automated process eliminates the need for months of training and provides long-sought information about living cells' activities. Using this technique, scientists could classify the thousands of different types of cells in the brain, map how they connect to each other, and figure out how diseased cells differ from normal cells. The project is a collaboration between the labs of Ed Boyden, associate professor of biological engineering and brain and cognitive sciences at MIT, and Craig Forest, an assistant professor in the George W. Woodruff School of Mechanical Engineering at Georgia Tech.

Advances in technology could soon make dozens of middle class jobs obsolete, according to a groundbreaking study. Carl Frey, a director at Oxford University - who previously authored a study suggesting that 35% of jobs in the UK were in danger - has followed up his work by analysing which higher earning jobs are likely to disappear. Examining office jobs that pay more than £40,000, Frey compiled a risk list that showed which jobs were most in danger of becoming automated, and those most likely to remain. The revelations that dependable office jobs such as insurance workers and real estate agents have a more than 97% chance of becoming computerised could now spark fears among the middle class workforce. 'While low-skilled jobs are most exposed to automation over the forthcoming decades, a substantial number of middle-income jobs are equally at risk.' Frey told The Times. Other jobs that feature high on the'risk list' are credit analysts who have a 97% chance of losing their jobs to robots, postal service workers at 95% and lab technicians who have an 89% chance of seeing their role become automated.

Almost 250,000 public sector workers could lose their jobs to robots over the next 15 years, according to a new report which claims machines would be more efficient and save billions of pounds. Reform, a right-of-centre thinktank, says websites and artificial intelligence "chat bots" could replace up to 90% of Whitehall's administrators, as well as tens of thousands in the NHS and GPs' surgeries, by 2030 – saving as much as £4bn a year. Even nurses and doctors could fall victim to the march of the machines, which the report says can outperform humans at some diagnoses and routine surgical procedures, and are more efficient at collecting information. The report argues that public services should become more flexible by embracing a gig economy where workers support themselves through a variety of flexible jobs acquired through online platforms. In remarks that seem set to infuriate unions, a Reform press release says: "Public services can become the next Uber, using the gig economy to employ locum doctors and supply teachers."

Tim Cook had indicated a lot of developments from Apple in his quarterly earnings call last week. He indicated that the company's flagship smartphone, the iPhone still had much more technological evolution to go for. Here is the range of products that the Apple CEO talked about and based on how they are expected to develop in 2017. "I think the smartphone is still in the early innings of the game," Cook said about the iPhone. Could this be an indication of some new technological changes coming to the device so as to keep it in "the game?" Apple generally launches just one phone per year compared to its competitors such as Samsung and LG, which have devices at various price ranges and have round the year launches.