Artificial intelligence systems have been developed to predict whether someone will develop dementia within two years, with 92 per cent accuracy, its developers claim. Data from 15,300 patients in the US was used to train the AI by researchers from the University of Exeter, teaching it who would and wouldn't go on to develop dementia. The technique works by spotting patterns in the data and learning who is most at risk, with researchers hoping it could cut the number of people wrongly diagnosed. Dementia is an umbrella term used to describe a range of progressive neurological disorders (those affecting the brain) which impact memory, thinking and behaviour. There are many different types of dementia, of which Alzheimer's disease is the most common.

The draft text, presented today by the rapporteur, says that the public debate should shift towards a focus on the enormous potential of AI, which offers humankind the unique chance to improve almost every area of our lives. AI could help combat climate change, pandemics and global hunger, and enhance quality of life through personalised medicine. According to the draft document, AI can substantially increase productivity, innovation, growth and job creation. The EU should not regulate AI as a technology; instead, the type, intensity and timing of regulatory intervention should solely depend on the type of risk associated with a particular use of an AI system. The text warns that the EU is currently falling behind in the global tech race that will determine the future political and economic global power balance.

The automation of business processes was already in a state of increasing adoption long before the pandemic began. However, the rapid shift to remote work coupled with COVID-19's economic impact has kicked automation into high gear. Organizations are seeking to improve efficiency and reduce cost by reducing employees' time spent on manual tasks. Robotic Process Automation (RPA) platforms have traditionally driven this transformation, but with the increased demand for a variety of use cases, many organizations are finding RPA tools inadequate. And they're wanting to add more intelligence to automation.

Software development is around a half-a-trillion-dollar industry that is always on the rise, adapting to emerging technology. Less than a year ago, a new language AI tool called GPT-3 hit the stage which is considered to have a huge potential. GPT-3, built by Open AI, is capable of writing in different styles, answering complex questions and writing bits of code. In September, Microsoft purchased license to the underlying technology. And now, the company's Redmond branch has announced their first product based on the program.

Fujifilm India is eyeing double-digit growth every year from now, against the 9-10 per cent growth it has been clocking over the past few years, largely driven by an expansion of its medical products business. This would entail a shift in focus to offering medical solutions through Internet of Things (IoT)-enabled products. While the company has not invested in any manufacturing facility at the moment, it may consider setting up one here in the future for IoT- and artificial intelligence (AI)-enabled medical products, considering the huge potential in India, Haruto Iwata, Managing Director, Fujifilm India, told BusinessLine. Some of the company's IoT application software is already being developed in India, he added. Going forward, Fujifilm India's primary focus will be on offering healthcare solutions through AI and IoT, he said.

While it is difficult to predict the impact of automation and artificial intelligence (AI) on the economy and the labour market, they have huge potential in sectors such as healthcare, Bill Gates, co-chair of Bill & Melinda Gates Foundation, said. "Certainly we use AI to do drug discovery. These biological systems are very complicated and so the fact that we have vaccines for TB and HIV coming, that is partly enabled by this rich data, advance in biology and machine learning," he said. Gates was speaking at the inaugural'Mint Visionaries' event in New Delhi where he was in conversation with Rishad Premji, chairman of Wipro Ltd. Though machine learning can give us some miracle tools, its impact on jobs is an important issue, he noted.

Machine learning and deep learning are still in the experimental stage of development. However, machine learning capabilities are already routinely incorporated in software for both personal and business use. You can find good machine learning projects everywhere – from home and office automation tools through industrial equipment to mobile devices. Machine learning ideas drive mostly projects aimed at the development of smart algorithms like artificial intelligence. Even so, do not make the mistake of referring to a machine learning project as "artificial intelligence".

The United Nations reports that about 1/3 of the food produced globally each year is lost or wasted, and I'd reckon that number is not too surprising. Those of us in the United States see evidence of waste each time we go out to eat or do a weekly purge of jam-packed refrigerators. Outside the waste, however, there's a greater problem many of us don't realize. Just as the amount of food wasted globally is skyrocketing, the global demand for food is, ironically, set to rise. How will we manage to feed and sustain 9 billion humans estimated to populate planet earth by 2050?

Business-to-consumer (B2C) businesses have made it a priority to incorporate machine learning into customer-facing functions, integrating it into sales and marketing. For business-to-business (B2B) companies, however, translating data into actionable marketing strategies can be a more difficult proposition. Selling to organizations invariably requires embarking on a much longer and more complex journey, culminating in an order of much higher value than in the consumer realm. With hundreds of thousands, if not millions, of dollars at stake, a misguided marketing investment could lead to financial losses. "The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world," says Laura Beaudin, a partner at Bain & Co. "A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world."

Business-to-consumer (B2C) businesses have made it a priority to incorporate machine learning into customer-facing functions, integrating it into sales and marketing. For business-to-business (B2B) companies, however, translating data into actionable marketing strategies can be a more difficult proposition. Selling to organizations invariably requires embarking on a much longer and more complex journey, culminating in an order of much higher value than in the consumer realm. With hundreds of thousands, if not millions, of dollars at stake, a misguided marketing investment could lead to financial losses. "The availability of data and the importance of having the focus on the full customer journey is coming a little later to the B2B world," says Laura Beaudin, a partner at Bain & Co. "A lot of expectations in terms of customers manifested themselves in the consumer world before they brought those expectations to their business-purchasing world."