MIT researchers have built a new benchmark tool that can accurately predict how long it takes given code to execute on a computer chip, which can help programmers tweak the code for better performance. MIT researchers have invented a machine-learning tool that predicts how fast computer chips will execute code from various applications. To get code to run as fast as possible, developers and compilers -- programs that translate programming language into machine-readable code -- typically use performance models that run the code through a simulation of given chip architectures. Compilers use that information to automatically optimize code, and developers use it to tackle performance bottlenecks on the microprocessors that will run it. But performance models for machine code are handwritten by a relatively small group of experts and are not properly validated.

BUSINESSES often think that machine learning (ML) models learn on their own and get better over time. If organizations want to use the technology effectively in 2020, they need to understand why and what to do about it. Business leaders have been told that they need a mountain of data to train any artificial intelligence (AI) or machine learning model. As a result, much of their efforts in the past year have been focused on acquiring data. However, once the models are deployed, they stop evolving and fail to account for changes that occur in variables.

It's hard to find a tech company that isn't attempting some sort of AI-related product, service, or initiative these days, but eBay went all-in by building its own AI platform, called Krylov. Sanjeev Katariya, eBay's VP and chief architect of AI and platforms, described Krylov in an interview with VentureBeat: "At the very highest level, Krylov is a machine learning platform that enables data scientists and machine learning engineers to ship all different kinds of models for all kinds of data quickly into production, which gets integrated into user experiences that eBay ships globally." It's a multi-tenant, cloud-based platform that involves technologies like computer vision and natural language processing (NLP), techniques including distributed training and hyper-parameter tuning, and tools germane to eBay's services, like merchandising recommendations, buyer personalization, seller price guidance, and shipping estimates. Even if they did, the hard costs -- however significant they may or may not be -- wouldn't fully capture what eBay has invested to build the platform over years of internal organizational efforts around the globe. And after all that, eBay is now open-sourcing Krylov.

Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to come to terms with potential limitations in what AI can realistically achieve. Meanwhile, a growing movement is grappling with its ethics and social implications, and widespread business adoption remains stubbornly low. As a result, companies and organizations are increasingly pushing tools that commoditize existing predictive and image recognition machine learning, making the tech easier to explain and use for non-coders. Emerging breakthroughs, like the ability to create synthetic data and open-source language processors that require less training than ever, are aiding these efforts. At the same time, the use of AI for nefarious ends like deepfakes and the mass-production of spam are still in their earliest theoretical stages, and troubling reports indicate such dystopia may become more real in 2020.

This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

Organisations are failing to truly harness the power of Conversational AI to deliver real business outcomes. Limited understanding of Artificial Intelligence (AI) has resulted in its hurried and ineffective implementation. The Customer Experience (CX) leaders of tomorrow can avoid this by following a framework that focuses on using AI and Machine Learning to deliver a tangible business case that enhances their overall customer experience. A recent CIO survey by Gartner found that by 2022, 20% of customer service will be handled by intelligent conversational agents and yet, 54% of organisations still have a very limited understanding of the technology. This has set the premise for the emergence of a plethora of vendors offering niche solutions using AI.

The Diplomat's Franz-Stefan Gady talks to Elsa B. Kania about the potential implications of artificial intelligence (AI) for the military and how the world's leading military powers -- the United States, China, and Russia -- are planning to develop and deploy AI-enabled technologies in future warfighting. Kania is an Adjunct Senior Fellow with the Technology and National Security Program at the Center for a New American Security (CNAS). Her research focuses on Chinese military innovation in emerging technologies. She is also a Research Fellow with the Center for Security and Emerging Technology at Georgetown University and a non-resident fellow with the Australian Strategic Policy Institute (ASPI). Currently, she is a Ph.D. student in Harvard University's Department of Government. Kania is the author of numerous articles and reports including Battlefield Singularity: Artificial Intelligence, Military Revolution, and China's Future Military Power and A New Sino-Russian High-Tech Partnership. Her most recent report is Securing Our 5G Future, and she also recently co-authored a policy brief AI Safety, Security, and Stability Among Great Powers. She can be followed @EBKania.

CHENNAI: Statistics show that about 40,000 women die in a year because of breast cancer, which is one in every 13 minutes a day. The importance of detecting the disease with the help of Artificial Intelligence is the need of the hour. Cautioning that breast cancer has become the most common disease among women both in the developed and the developing countries in the world in the past few years, Subash Kumar, a Computer Science Engineering graduate with around 12 years of experience in the field of data science, Artificial Intelligence machine learning- deep learning, from Chennai, claims that he has developed an easy way of detecting and curing the same. The application of Artificial Intelligence (AI) machine learning technology with deep learning algorithms to whole-slide pathology images can potentially improve the diagnostic accuracy of breast cancer at a very early stage. Some scholars have assessed the performance of automated deep learning algorithms at detecting metastases in the tissue of women with breast cancer and compared results with pathologists' diagnoses.

During a trial of self-driving buses in Oita City, also in southern Japan, one bus crashed into a curb, and officials realized that autonomous vehicles were not quite ready to cope with situations like traffic jams, jaywalkers or cars running red lights. For decades, Japan has been a leader in the use of robots. It is the world's largest maker of industrial robots, and once led the globe in the number of robots per employee, said Gee Hee Hong, an economist specializing in Japan at the International Monetary Fund. More recently, according to the International Federation of Robots, Singapore, South Korea and Germany have overtaken Japan in robots per worker. Unlike in the West, where employees often view automation as an existential threat, robots in Japan are generally portrayed as friendly forces.

Using machine learning to comb through exercise-related tweets, researchers identified regional and gender differences in exercise types and intensity levels, providing insights into possible interventions that target certain communities, according to the findings of a study published in BMJ Open Sport & Exercise Medicine. The machine-learning method also allowed researchers to see how different populations feel about different kinds of exercise. The findings revealed that walking was the most popular physical activity for both men and women across all regions. Men and women also mentioned performing gym-based activities at similar rates, with men mentioning such activities in approximately 4.68% of tweets, compared to 4.13% for women. Among these tweets, CrossFit was the most popular among men's tweets, showing up in approximately 14.91%.