Shamayita Acharya

By Lance Eliot, the AI Trends Insider Julius Caesar is famously known for his radical act in 49 BC of defying authority by marching his army across the Rubicon river. Unless you happen to be a historian, you might not be aware that the Roman Senate had explicitly ordered Caesar to disband his army, return to Rome, and not to bring his troops across the Rubicon. His doing so was an outright act of defiance. Not only was Caesar defiant, he was risking everything by taking such a bold and unimaginable act. The government of Rome and its laws were very clear cut that that any imperium (a person appointed with the right to command) that dared to cross the Rubicon would forfeit their imperium, meaning they would no longer hold the right to command troops. Furthermore, it was considered a capital offense that would cause the commander to become an outlaw. The commander would be condemned to death, and — just to give the commander some pause for thought, all of the troops that followed the

A little more than three-and-a-half years ago, Cory Kidd founded Catalia Health based on the work he did at the MIT Media Lab and Boston University Medical Center. Headquartered in San Francisco, the company’s overarching goal is to improve patient engagement and launch behavior change. But the way it goes about meeting that mission is unique. Through Catalia Health’s model, each patient is equipped with an interactive robot to put in their home. Named Mabu, the robot learns about each patient and their needs, including medications and treatment circumstances. Mabu can then have tailored conversations with a patient about their routine and how they’re feeling. The information from those talks securely goes back to the patient’s pharmacist or healthcare provider, giving them an update on the individual’s progress and alerting them if something goes wrong. Right now, the company is focused on bringing Mabu to patients with congestive heart failure. It is currently working with Kaise

Attention, foreign-policy makers. You will soon be working with, or competing against, a new type of robot with the potential to change the game of international politics forever. Diplomacy is similar to a strategic board game. A country makes a move, the other(s) respond. All want to win. Artificial intelligence is good at board games. To get the game started, the system analyses previous play, learns lessons from defeats or even repeatedly plays against itself to devise a strategy that can be never thought of before by humans. It has defeated world champions in chess and Go. More recently, it has won at no-limit Texas Hold’em poker, an “imperfect information game” in which a player does not have access to all information at all times, a situation familiar in the world of diplomatic affairs. Several prototypes of a diplomatic system using artificial intelligence are under development in China, according to researchers involved or familiar with the projects. One early-stage machin

The digital ecosystem is changing everything. Companies that don't adapt to this reality risk missing out on an exciting new way of doing business. The post How the Digital Ecosystem Will Revolutionize Business appeared first on Hortonworks .

Ever since the rise of big data analytics fundamentally reshaped how we do business, big data projects have been launching across the market. Many newcomers to the field of big data and information analytics are unfamiliar with the hurdles that make so many rookies trip and fall, however, and running a successful big data project takes a considerable amount of time and effort. If you follow a sound strategy, however, your big data project can be off to a roaring start in no time. These are the essential tips you should follow if you want don’t want your big data project to crash and burn. Assembling your team If you’re looking to run a really tremendous big data project, you’ll need a team of skilled professionals with the tech-savvy and business know-how to compete in today’s cutting-edge market. After all, big data projects aren’t exactly scarce in this day and age; current predictions expect the broader big data market to continue to soar in the near future, with more than $20

Technological innovations are helping healthcare providers advance and improve the medical field. Some technologies now permit physicians to diagnose illnesses with 100-percent accuracy. With science and technology, researchers are creating innovations that are expanding and transforming health care. Innovations such as artificial intelligence (AI) are aiding scientists in developing new technologies in remarkably short periods of time, and machine learning algorithms are helping researchers analyse information incredibly fast. The following excerpts reveal in more detail three more ways that technology is advancing modern health care. Advancement 1: Innovative Imaging Technologies Physicians use imaging so that they can view patients’ internal organs and accurately diagnose illnesses. The technology is one of the greatest achievements in medicine. It’s eliminated the need for costly, invasive and painful exploratory surgeries. As a result, Medicare spending on diagnostic imaging

By Lance Eliot, the AI Trends Insider Did you realize that apparently more than half of all United States medical doctors are suffering from burnout? You might at first glance not be overly surprised, since you’ve likely seen how harried most medical doctors are. Often, their patient load is at the max and they barely have time to say hello before they move onto the next patient. Many medical doctors bitterly complain that the nature of the healthcare system prevents them from spending quality time with their patients as they are under strict time guidelines and have little choice in the matter of how they use their time with patients. You might also be thinking that it doesn’t matter that medical doctors might be suffering burnout. The perception is that they are well-paid anyway, and so if they have to work long and hard hours, so be it. Some might think of them as whiners that don’t realize how good they really have it. This though would be a misunderstanding about the impacts o

Designing new molecules for pharmaceuticals is primarily a manual, time-consuming process that’s prone to error. But MIT researchers have now taken a step toward fully automating the design process, which could drastically speed things up — and produce better results. Drug discovery relies on lead optimization. In this process, chemists select a target (“lead”) molecule with known potential to interact with a specific biological target, then tweak its chemical properties for higher potency and other factors. Chemists use expert knowledge and conduct manual tweaking of the structure of molecules, adding and subtracting functional groups — groups of atoms and bonds with specific properties. Even when they use systems that predict optimal desired properties, chemists still need to do each modification step themselves. This can take a significant amount of time at each step and still not produce molecules with desired properties. Researchers from MIT’s Computer Science and Artificial I

The US Department of Veterans Affairs (VA) and IBM Watson Health have announced the extension of a public-private partnership to apply artificial intelligence (AI) to help interpret cancer data in the treatment of Veteran patients. First announced  two years ago  as part of the National Cancer Moonshot Initiative, VA oncologists have now used IBM Watson for Genomics technology to support precision oncology care for more than 2,700 Veterans with cancer. VA’s precision oncology program primarily supports stage 4 cancer patients who have exhausted other treatment options. The partnership extension with IBM will enable VA oncologists to continue using Watson for Genomics through at least June 2019. “Our mission with VA’s precision oncology program is to bring the most advanced treatment opportunities to Veterans, in hopes of giving our nation’s heroes better treatments through these breakthroughs,” said Acting VA Secretary Peter O’Rourke in a press release. “We look forward to continui

Illumina has announced the release of open source, novel artificial intelligence (AI) software that can distinguish between potential disease-causing mutations, and the millions of benign genetic variants in individuals. The new AI software for genome interpretation has been publicly released through Illumina’s BaseSpace Sequence Hub and GitHub. “The open source release of our software demonstrates Illumina’s commitment to not only being the world’s largest enabler of DNA sequencing data, but also making widely available the AI tools that will enable clinicians and researchers to keep up with the enormous depth and breadth of genomic data being generated,” said Mostafa Ronaghi, Chief Technology Officer at Illumina, in a press release. The Illumina team, along with collaborators at Stanford University, the University of Florida, the Broad Institute, and the University of Chicago, utilized state-of-the-art, deep neural networks to distinguish a handful of disease-causing mutations in

A few days ago, some of the world’s most renowned entrepreneurs, engineers and scientists in the technology industry warned for the dangers of artificial intelligence and that of lethal autonomous weapons. Over 150 companies and 2400 individuals from 90 countries signed the lethal autonomous weapon pledge at the 2018 International Joint Conference on Artificial Intelligence. I signed it as well, as I believe that we should do whatever it takes to stop lethal autonomous weapons as they can seriously destabilise the global society. Already, artificial intelligence is becoming increasingly advanced, and it is poised to play a major role in every industry; not only the military industry but every industry. With that, it will become a central component in our lives, affecting most of the decisions that we take on a daily basis. When that happens, it is vital that we remain in control of artificial intelligence, that biased data does not take over and that we prevent artificial intelligenc

After years of working on your PhD and being in academia, transitioning into a Data Science career can be a culture shock. It’s important to understand how the roles you want to apply to vary; what industry is it in? What is the main focus of the projects? Who will you be working alongside? What are the end goals the team would be working on achieving? Does your chosen industry have anything to do with your PhD and academic research? ‘Data Scientist’ is becoming an ever-ambiguous term for many facets of working within the Data Science industry. Not limited to technology, Data Scientists are found in many industries as companies seek professionals to wrangle their data. When entering the workforce following your PhD, recognise what sort of position you want to go for and, will it be linked to your PhD. Play on your strengths here and showcase exactly how your PhD can benefit the team and business as a whole, what impact will you and your work have? This is a must to include in you

What is the value of your data? Information Technology (IT) professionals have made a living off of data management, storage and reports. Analytics platforms: making sense of data can be a key skill for successful executives today. Comparing Analytics Platforms Do you remember when the Chief Information Officer (CIO) position was started? You might have guessed that the concept started to gain momentum with the IBM computers of the 1980s. The history of Chief Information Officers shows how data has become more valuable and crucial to modern business success. Originally, the title was not at the senior executive board level, but more at the level of the IT manager. In 1990s, there were only 10% of IT departments that used the CIO title, according to MIS Magazine. The primary skill of the CIO was number-crunching of electronic data. When data analysis was no longer simply ordered by individual managers, but the Chief Executive Officer (CEO), then the CIO position gained more prom

There is a growing demand for data scientists in every organization. For the growth of any business, there is a need to evaluate the data you collect and data scientists need both the right skill set and the right tools to help you deliver results with your data. In this article, we will talk about open source data science tools you or they can use to do just that. 1. Python Python is a popular high-level, general-purpose, dynamic programming language that is commonly referred to as the easiest language to read and to learn. It is emerging as the leading language for open data science because it combines rapid development with the ability to interface with high-performance algorithms written in C or Fortran. For the first time in three years, Python has edged out R as the most popular data science software: As a result, there is a rich suite of programming libraries that can be used as a foundation for data science. Python’s syntax allows programmers to express concepts cle

By Lance Eliot, the AI Trends Insider Apple’s self-driving car project is so cloaked in secrecy that most Apple employees know nothing about it. Lots of people all across the world would love to know what’s going on in the skunk works of the Apple AI self-driving car efforts. Will Apple be the one that surprises us all and gets to a true Level 5 self-driving car before anyone else does? Will they shock the world and pull a rabbit out of hat? Or are they really only working on the next iPod? Nobody really can say, other than those sworn to secrecy. Recently, a former Apple hardware engineer was busted for allegedly walking out the door with inner sanctum secrets about Apple’s AI self-driving car project. In a story line suitable for a movie, the engineer was arrested while trying to get on a flight to China at the San Jose, California airport. A criminal complaint was filed in federal court and indicated that he allegedly downloaded numerous technical materials about the Apple self-d