AI Is Moving Beyond Chatbots. The DNA file had been gathering dust in Pietro Schirano's computer for years. Then, earlier this month, he gave it to Claude Code--an "agentic coding tool" developed by Anthropic--for analysis. "I'm attaching my raw DNA file from Ancestry DNA," he told the tool. The AI spawned copies of itself on Schirano's computer, each one simulating an expert in a different part of the genome--one expert on cardiovascular disease, another on aging, a third on autoimmune disease.

Kara Swisher has gotten to know a lot of tech-industry people over the years, and as she explains to producer Nayeema Raza in this episode of On With Kara Swisher, she knows "the difference between jerks and people who really actually do care about something bigger than themselves." Kara wholeheartedly believes LinkedIn co-founder Reid Hoffman falls into the latter camp, even if the two of them don't always agree about the benefits and harms of new technologies such as artificial intelligence. Hoffman is an AI evangelist who is knee-deep in that world (including, until he recently stepped down, being on the board of OpenAI, the nonprofit behind ChatGPT and GPT-4), while Kara looks at the current AI frenzy and sees storm clouds ahead. During her conversation with Hoffman, Kara asks the longtime tech entrepreneur and investor for his thoughts on a range of topics, from the collapse of Silicon Valley Bank to his political advocacy and ongoing fears about Donald Trump. She also grills Hoffman about his seemingly unflinching tech optimism; in the condensed segment below, she asks him to make his best case for several new AI-based technologies as well as explain what does, in fact, worry him about how AI could go wrong. Journalist Kara Swisher brings the news and newsmakers to you twice a week, on Mondays and Thursdays.

Insurance has been a fertile proving ground for analytics over many years. Underwriters have long used models to improve forecasting and predictions. More recently, insurers have started to use analytics to track and respond to client behaviour, to improve client-centricity, build lasting relationships and ensure positive outcomes for all parties involved. The proliferation of modern data analytics within the industry has had a profound effect on how we do business. It has given us insights that help us understand our clients better.

In the Indian 2011 census, the elderly population age 60 and above accounted for 8.6% of the total population (103 million). This is projected to rise to 19.5% (319 million) by 2050. The proportion of the people aged 75 and above is expected to increase by 340% between 2011 and 2050. The demographic/ epidemiological shift will further overburden our health care systems. The need of the hour is to promote'Healthy ageing' as per WHO (decade commitment 2021-2030) to decrease the burden of chronic health conditions and improve quality of life of the older persons.

Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We present a system for fine-grained location prediction (FGLP) of mobile users, based on GPS traces collected on the phones. FGLP has two components: a federated learning framework and a prediction model. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FGLP represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model merges Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. FGLP uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FGLP outperforms baseline models in terms of prediction accuracy. We also demonstrate that FGLP works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on several types of Android phones demonstrate FGLP's feasibility in real life.

You don't need to take in Disney's latest Star Wars instalments to see robots battling robots. Just pay a visit to the cyber desk of an energy company. Digital technologies are very democratic -- anyone can access them. All you need is a reasonably advanced smart phone from any of the big phone suppliers, an internet access (free in coffee shops and malls), and a free account on a cloud service. The apps are mostly free too, which tells you they're not costly to make.

Much of the focus on deep learning systems for vision has been in three areas: autonomous vehicles, facial recognition, and robotics. However, as with the many other areas of artificial intelligence (AI), vision will have a far wider impact on society than in those three areas. The logistics of relocation are heavily depending, no surprise, on what is being moved. Vision can be applied to that challenge in order to create more accurate estimates much faster than before. As a one news article points out, "about one in five Americans (23 percent) think that moving is more stressful than planning a wedding, according to new research. Twenty-seven percent think it's more stressful than a job interview, and more than one in 10 (13 percent) even go as far as to say it's more stressful than a week in jail."

AI "needs to span every battlefield system that we have, from our maneuver systems for our fire control systems to our sustainment systems to our soldier systems to our human resource systems and our enterprise systems." Gen. Matthew Easley, Director of the Army AI Task Force (AAITF), made these remarks at the 2019 AUSA Warriors Corner, which runs from October 14-16. "We need to be able to create an AI infrastructure for the Army" -- Brig. The US Army is looking to integrate AI into every facet of its operations. "We see AI as an enabling technology for all Army modernization priorities -- from future vertical lift to long range precision fires to soldier lethality," said Easley.

The News: Huawei Technologies has officially unleashed its artificial intelligence (AI) chip Ascend 910, which it says has a maximum power consumption of just 310W–lower than its originally planned specs of 350W. The chip is touted to have "more computing power than any other AI processor", delivering 256 teraflops at half-precision floating point (FP16) and 512 teraflops for integer precision calculations. The Chinese tech giant also announced the commercial availability of its MindSpore AI computing framework, which it said was designed to ease the development of AI applications and improve the efficiencies of such tools. Analyst Take: The news of the Huawei Ascend 910 chip, a powerful AI processor, is a clear sign that Huawei is moving forward with its plans to cut any dependency on the U.S. We saw a similar aggressive course of action from the company just a few weeks ago when it announced Harmony OS, and the intent to make it avaialble to replace Google's Android in the company's smart phones. This would reduce its dependence on U.S. based companies and at the very least could give Huawei smart phones a boost in China.

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.