Ask anyone who regularly interviews people and they'll tell you that few things are stranger than when the tables turn and you're the one being interviewed. This is especially true when the person interviewing you has been dead for a year and a half. Virtually Parkinson is a new podcast in which celebrities are interviewed by an AI model trained to speak and act like the late Michael Parkinson. The announcement of the podcast last year prompted a flurry of vaguely apocalyptic reactions. It was sacrilegious, some said, tantamount to digging up and reanimating a national treasure against his will. It was pointless, others said – of all the transformative ways to use AI, you're blowing it on a podcast?

An ultra-sensitive robotic'finger' that could help detect breast cancer is being developed by scientists. Experts have created a device with a sophisticated sense of touch that can take patient pulses and check for abnormal lumps. The technology could make it easier for doctors to detect diseases such as breast cancer early on, when they are more treatable. And it may also help patients feel at ease during physical examinations that can seem uncomfortable and invasive, the researchers said. While rigid robotic fingers already exist, experts have raised concerns that these devices might not be up to the delicate tasks required in a doctor's office setting.

Thousands of British women will have their mammograms assessed by an AI'doctor' in a real-time clinical trial aimed at improving early breast cancer detection, Rishi Sunak will announce this week. Speaking ahead of a global summit on artificial intelligence in South Korea, the Prime Minister said the AI technology could help improve aspects of Britain's health service. But they added regulations needed to be brought in to ensure the technology worked for the benefit of mankind and not to its detriment. Alongside his South Korean counterpart, Mr Sunak hailed a collaboration between the NHS and Korean firm Lunit on using AI to improve the speed and accuracy of breast cancer diagnosis as an example of the positives of the new technology. 'AI is changing the world around us.

Intelligent interaction with the physical world requires perceptual abilities beyond vision and hearing; vibrant tactile sensing is essential for autonomous robots to dexterously manipulate unfamiliar objects or safely contact humans. Therefore, robotic manipulators need high-resolution touch sensors that are compact, robust, inexpensive, and efficient. The soft vision-based haptic sensor presented herein is a miniaturized and optimized version of the previously published sensor Insight. Minsight has the size and shape of a human fingertip and uses machine learning methods to output high-resolution maps of 3D contact force vectors at 60 Hz. Experiments confirm its excellent sensing performance, with a mean absolute force error of 0.07 N and contact location error of 0.6 mm across its surface area. Minsight's utility is shown in two robotic tasks on a 3-DoF manipulator. First, closed-loop force control enables the robot to track the movements of a human finger based only on tactile data. Second, the informative value of the sensor output is shown by detecting whether a hard lump is embedded within a soft elastomer with an accuracy of 98%. These findings indicate that Minsight can give robots the detailed fingertip touch sensing needed for dexterous manipulation and physical human-robot interaction.

A security guard has become one of the first patients in Scotland to have a tumour cut out of his throat by a robot. Peter Simpson was awake, talking and eating ice-cream just five hours after his tonsil and part of his tongue were removed. The 63-year-old was home within 24 hours of the pioneering surgery at the Queen Elizabeth University Hospital in his home city of Glasgow. "I'm feeling good and I'm really quite surprised," he told STV News after the procedure, which our cameras were allowed to film. "When you're told you're getting this kind of operation on your throat, you think'am I going to be able to talk or eat?', but I can do everything. "There is some discomfort when I swallow but it's much better than I expected." Peter was shaving while on holiday in Skye last August when he noticed a lump on his neck, which turned out to be cancerous. Tumours in such hard-to-reach areas would previously have involved a gruelling and invasive operation. But medics were instead able to guide robotic arms – already used in urology and lung surgery – into Peter's mouth. Ahead of the treatment, staff in the operating theatre told STV News it was a "big week" for them after six months of extensive Transoral Robotic surgery (TORS) training. A doctor from London's Royal Marsden hospital was there to advise as the first of five such ENT (ear, nose and throat) procedures planned in Scotland took place. Jenny Montgomery, consultant for head and neck surgery, guided the robotic arm and communicated by microphone with a surgical team working on Peter. The'arms' of the robot allowed the team to make tiny movements, while the hands can rotate 360 degrees. Enhanced precision helps reduce side effects and the length of time patients have to stay in hospital. "This patient will have probably a more effective identification of where their cancer originated from than they would have had with previous operations," said Ms Montgomery. "If it's a small cancer, there is a possibility they might not need radiotherapy.

Johan den Haan is CTO of Mendix, a Siemens business and leader in enterprise low-code, a model-driven approach for building apps 10x faster. Is AI the transformative technology destined to work wonders for humanity, from driverless cars to a cure for cancer? Or is it a genie in a bottle that, once released, could be used to manipulate or even rule humankind? With the tremendous advances in computing power, software capabilities and the cloud over the last decade, progress on AI is no longer linear -- it's exponential. That means it's time to pay attention and make some fundamental decisions.

We are going to do binary classification, so the value of y (true/target) is going to be either 0 or 1. For example, suppose we have a breast cancer dataset with X being the tumor size and y being whether the lump is malignant(cancerous) or benign(non-cancerous). Whenever a patient visits, your job is to tell him/her whether the lump is malignant(0) or benign(1) given the size of the tumor. There are only two classes in this case. So, y is going to be either 0 or 1. Let's use the following randomly generated data as a motivating example to understand Logistic Regression.

Indian health care startup Niramai Health Analytix is in talks with Asian hospitals for its noninvasive, artificial intelligence-enabled technology to screen for early signs of breast cancer. The startup has raised about $7 million from Japan's Dream Incubator and local investors Pi Ventures, Axilor and Flipkart co-founder Binny Bansal, among others. It is now preparing pretrials in South and Southeast Asian countries with a high incidence of breast cancer. According to the World Health Organization, Asia accounted for 43.6% of the 2.09 million breast cancer cases globally in 2018. The organization also noted that breast cancer is the most common cancer in women, both in developed and less developed nations.

An NHS-backed medical app has been branded a'public health danger' after failing to suggest a 66-year-old woman's breast lump could be cancer. After watching a video of the interaction, Twitter users flocked to criticise Bablyon Health's'flawed' symptom-checking system. Someone claiming to be an NHS consultant told the artificial intelligence a painless breast lump – a hallmark of cancer – was their only symptom. Despite the made-up patient being over 66 years old, so being 10 years older than when the average menopause ends, the'utterly absurd' bot still asked whether she was pregnant or breastfeeding. And, after asking numerous questions, the app's best guess was that the patient had osteoporosis, a condition which weakens people's bones as they get older.

New SFI research challenges a popular conception of how machine learning algorithms "think" about certain tasks. The conception goes something like this: because of their ability to discard useless information, a class of machine learning algorithms called deep neural networks can learn general concepts from raw data-- like identifying cats generally after encountering tens of thousands of images of different cats in different situations. This seemingly human ability is said to arise as a byproduct of the networks' layered architecture. Early layers encode the "cat" label along with all of the raw information needed for prediction. Irrelevant data, like the color of the cat's coat, or the saucer of milk beside it, is forgotten, leaving only general features behind.