Artificial Intelligence Not Seen As A Job-Killer, Yet

Executives don’t see many job cuts ahead of a result of tasks being replaced by AI. Is this a realistic perception?

A recent survey of executives out of IFS tackled issues of AI perception, finding a few business leaders predict worker displacement by AI. Close to half, 46%, predict AI will actually increase headcounts over the coming decade, while 25% predict no changes at all to workforce sizes. Only 18% see AI as a tool for replacing workers.

There are many high hopes for AI — 61% see it boosting the productivity of their workforces. Another 48% also see AI as adding value to their products and services. While a majority anticipate productivity increases from AI, only 29% say such increased productivity will reduce headcounts over the next 10 years. “Respondents did not make the connection between increased productivity and reduced headcount,” the report’s authors suggest.

Cyber security enhanced with AI and ML: Improving data loss prevention

The vast and growing amounts of data being created, collected and used by the enterprise makes the deployment of data security solutions a business imperative. It is essential to implement cybersecurity solutions and practices to prevent data leaks and breaches, but how do businesses stay ahead of the growing sophistication of cyber-attacks?

Predictive technologies, such as artificial intelligence (AI) and machine learning (ML) can enhance traditional data loss prevention (DLP) solutions to greatly reduce the risk of breaches or leaks.

AI can provide critical analysis, and ML uses algorithms to learn from data—both provide a dynamic framework to predict and solve data security problems before they occur. The more data patterns ML analyses, the more processes and self-adjustments can operate based on those learned patterns. This continuous delivery of insights increases in value with the “intelligence” of the technology.

What Does Interoperability Mean for the Future of Machine Learning

Interoperability in action: Healthcare

Let’s use healthcare as an example of how interoperable machine learning technology can enhance our lives. Consider high-tech medical procedures like CT scans that automatically generate large volumes of sensor data for a single patient as opposed to health information your doctor manually enters into a proprietary database during a routine check-up. Without a way to quickly and automatically integrate these disparate data types for analysis, there is lost the potential for fast diagnosis of critical illnesses. This has created a demand for optimization across different information models. Current methods and legacy systems simply aren’t friendly in terms of interoperability — but recent developments in machine learning are opening the door for the possibility of stronger, faster translation between information platforms. The result could be vastly improved medical care and optimized research practices.

The role of neural networks

Modeled after the human brain, neural networks are comprised of a set of algorithms that are designed to recognize patterns. They interpret sensory data through a sort of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. According to a 2017 article in MIT News, neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Since that time, the approach has fallen in and out of favor, but today it’s making a serious comeback.

Artificial Intelligence, Machine Learning and Python

Ever since computers were invented, there has been an exponential growth in their ability and potential to perform various tasks. In order to use computers across diverse working domains, humans have developed computer systems while increasing their speed, and reducing size with respect to time.

Artificial Intelligence pursues the stream of developing computers or machines to be as intelligent as humans themselves. In this article, we will scrape the top layer about the concepts of artificial intelligence that will help understand related concepts like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms, etc. Along with this, we will also learn about its implementation in Python.

Putting Artificial Intelligence to Work

Artificial Intelligence (AI) is one of the hottest technology trends on the planet, but for the average small business owner, it can be terribly intimidating. It’s time to get over that.

While many small and midsized business (SMB) leaders say AI is critical for their business, only one in five are actually doing anything about it, according to a recent Capterra survey.

This should come as no surprise since we all know SMBs don’t tend to deploy new technology until the kinks have been worked out and it becomes more mature. Plus, they have more pressing priorities to deal with, like finding new customers and paying the bills. Right?

But here’s the thing: AI isn’t all that new, and it’s not some temperamental new technology that could come-and-go as quickly as Palm Pilots, Betamax video players and QR Codes. It’s here to stay, finding its way into everything from those personalized shopping suggestions we all get on social media sites to virtual assistants like Amazon’s Alexa and Apple’s Siri. Increasingly, it’s also seeping into SMB operations.

Artificial intelligence will make you smarter

The future won't be made by either humans or machines alone – but by both, working together. Technologies modeled on how human brains work are already augmenting people's abilities, and will only get more influential as society gets used to these increasingly capable machines.

Technology optimists have envisioned a world with rising human productivity and quality of life as artificial intelligence systems take over life's drudgery and administrivia, benefiting everyone. Pessimists, on the other hand, have warned that these advances could come at great cost in lost jobs and disrupted lives. And fearmongers worry that AI might eventually make human beings obsolete.

Artificial intelligence has the power to change the world – but it is double-edged sword

Artificial Intelligence (AI) is already reconfiguring the world inconspicuous ways. Data drives our global digital ecosystem, and AI technologies reveal patterns in data. Smartphones, smart homes, and smart cities influence how we live and interact, and AI systems are increasingly involved in recruitment decisions, medical diagnoses, and judicial verdicts. Whether this scenario is utopian or dystopian depends on your perspective.

The potential risks of AI are enumerated repeatedly. Killer robots and mass unemployment are common concerns, while some people even fear human extinction. More optimistic predictions claim that AI will add $15 trillion (£11.7 trillion) to the world economy by 2030, and eventually, lead us to some kind of social nirvana.

Artificial intelligence and the future of medicine

Artificial intelligence has the potential to transform medicine. Two Washington University experts, Philip Payne, Ph.D., and Thomas Maddox, MD, discuss how AI may change health care and what challenges need to be addressed before it can become part of routine care.

Washington University researchers are working to develop artificial intelligence (AI) systems for health care, which have the potential to transform the diagnosis and treatment of diseases, helping to ensure that patients get the right treatment at the right time.

In a new Viewpoint article published Dec. 10 in the Journal of the American Medical Association (JAMA), two AI experts at Washington University School of Medicine in St. Louis discuss the best uses for AI in health care and outline some of the challenges for implementing the technology in hospitals and clinics. Philip Payne, Ph.D., is the Robert J. Terry Professor and director of the Institute for Informatics, and Thomas M. Maddox, MD, is a professor of medicine and director of the Health Systems Innovation Lab, launched in 2017 as a partnership between the School of Medicine and BJC HealthCare. The lab is aimed at developing innovative ways to deliver care and improve people’s health.

The Future Artificial Intelligence in the Service Industry

Artificial intelligence has grown into a full-fledged ecosystem. With the automation, it brings to almost every aspect of our life, we see it creeping into our mobile phones, our devices connected over IoT and websites, Artificial Intelligence has become a way of living.

The major breakthroughs that our generation has witnessed are in the service industry where the Chat representatives have been replaced by chatbots. This is just the tip of the iceberg.

The customer service industry is very excited to put the advantages of AI powered solutions to use. Not only is it reducing the human effort, but it is also bringing in more accuracy and precision in services fueled by an ecosystem driven by innovative mobile app developers. A lot of customer service representative’s job is repetitive, mundane and has a pattern which makes it easy for the AI scientists to develop algorithms that fit in the space well.

Healthcare Natural Language Processing Expects Steady Growth

Natural language processing is rising in the healthcare industry as organizations seek solutions to process unstructured data.

Healthcare natural language processing (NLP) is expected to grow significantly over the next several years as organizations continue to adopt more advanced health IT infrastructure solutions, according to a recent report.

A Transparency Market the research report predicts the global healthcare NLP market will be worth $4.3 billion by 2024, growing significantly from the $936 million reported in 2015. Between 2016 and 2024, the market is projected to rise at a CAGR of 18.8 percent.

According to the report, meaningful comprehension of unstructured data is the main reason why healthcare organizations are interested in NLP. The overall adoption of more advanced health IT infrastructure technology is expected to significantly boost the market.