When I think back to my oldest memory of artificial intelligence, all I can think about is the 2001 movie A.I. Artificial Intelligence. It's both entertaining and hysterical to look back on that movie and think that's how I thought the future would look... and what I believed artificial intelligence would consist of. Fifteen years later, I think it's safe to say that we've come a long way from that 2001 vision of the "future." We can actually encounter and interact with intelligent machines daily. There is also a more defined path of where artificial intelligence is headed and how we really want to benefit from these intelligent machines.

Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by--but typically operate quite differently from--the ways people use their nervous systems and bodies to sense, learn, reason, and take action. While the rate of progress in AI has been patchy and unpredictable, there have been significant advances since the field's inception sixty years ago. Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and AI planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood. Deep learning, a form of machine learning based on layered representations of variables referred to as neural networks, has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition.

A few years ago, robotics, a branch of artificial intelligence (AI), was seen as an upcoming trend that figured all too often in predictions made by business leaders and was presented as a revolutionary technology that would invade all aspects of our society. By 2016, not only have these predictions come true for many aspects of our personal lives, but robotics and automation have also entered the business world. Banks, in particular, are starting to use robotics and automation tools to address new challenges created by their move into the digital age. Customer service management will be one of the most impacted areas of banking due to the evolution of robotics. Roy Morgan revealed recently that the average customer satisfaction ratings for the Big Four banks have dipped to the lowest level since mid-2013, after peaking in mid-2015.

The study "Artificial Intelligence and Life in 2030" looks at the impact of AI in 2030. According to Harvard, Artificial intelligence has already transformed our lives -- from autonomous cars and platforms like IBM Watson in the healthcare sector, to the robotic vacuums and smart thermostats. Over the next 15 years, AI technologies will continue to make inroads in nearly every aspect of our lives, from education to entertainment, healthcare to security. Technoloy is however only one part of the equation. A very important question is: are people ready for the coming transformation?

Autonomous cars need a new kind of horsepower to identify objects, avoid obstacles and change lanes. There's a good chance that will come from graphics processors in data centers or even the trunks of cars. With this scenario in mind, Nvidia has built two new GPUs--the Tesla P4 and P40--based on the Pascal architecture and designed for servers or computers that will help drive autonomous cars. In recent years, Tesla GPUs have been targeted at supercomputing, but they are now being tweaked for deep-learning systems that aid in correlation and classification of data. "Deep learning" typically refers to a class of algorithmic techniques based on highly connected neural networks--systems of nodes with weighted interconnections among them.

Chatbots were oversold and not that great when they first launched earlier this year, Facebook Messenger VP David A. Marcus told Techrunch's Disrupt conference. "The problem was that it got really overhyped very, very quickly," he said. "And the basic qualities we provided at that time weren't good enough to replace traditional apps." The problems, he added, are typical with the growing pains for any ecosystem. It didn't help that Facebook gave developers a very limited amount of time (just two weeks) to develop the first bots before they debuted at the F8 developer conference in April.

To understand the bot detection problem better, let's address the common website security measure taken by companies to prevent bot attacks and why they aren't effective against blocking bots. Code Level Security It's is a good practice to implement code level security in the initial development rather than worrying about it later. Code level security is effective when it comes to basic website security threats. But as online bots evolve to exploit new website vulnerabilities, it becomes impossible for websites to detect and block sophicated bot attacks. Advanced bot attacks that can almost perfectly mimic a human user make it difficult for code level security measures and in-house bot detection tools to detect sophisticated bot patterns. Such bots that mimic human behavior are programmed to interact directly with web pages, for example, to spam forms or throw password dictionaries at user login fields.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS Analytics Product Manager. "This software helps provide positive outcomes to increase profitability, better understand customer behavior and decrease the cost of doing business." SAS Viya SAS Visual Data Mining and Machine Learning is one of the initial analytics applications on the SAS Viya platform. SAS Viya is an innovative analytics environment designed for use in the cloud that provides the power of SAS Analytics through SAS interfaces as well as open APIs for Python, Lua, Java and REST. The new analytics offerings for SAS Viya are structured for a diverse range of users, while maintaining consistency and manageability. In addition to SAS Visual Data Mining and Machine Learning for data scientists, the Viya family will include SAS Visual Analytics for business analysts and SAS Visual Statistics, aimed at experienced statistical users. The breadth of SAS Viya applications will satisfy the appetites of all user types, while maintaining a consistent structure. The speed of the multithreaded parallel processing engine in SAS Viya will drive faster decisions. And the strength of analytics from the advanced analytics leader will produce trusted results. To better understand the need, applications and benefits of machine learning, please visit Machine Learning: what it is and why it matters. Today's announcement was made at the Analytics Experience conference in Las Vegas, a business technology conference presented by SAS that brings together more than 10,000 attendees on-site and online to share ideas on critical business issues. About SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 80,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW . SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. Other brand and product names are trademarks of their respective companies.

The relentless increase in computing power and the accumulation of big data over the years has sparked intense interest in machine learning and its associated techniques. The new SAS Visual Data Mining and Machine Learning software, available later this month, will feed this need for smarter analytics. Advanced analytics offer insight to businesses, but machine learning and deep learning algorithms take it deeper, revealing insights that were previously out of reach. For example, machine learning use can include facial recognition in security systems, speech recognition in customer service applications, accurate product recommendations in e-commerce, self-driving cars and medical diagnostics. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS Analytics Product Manager.

When it comes to detecting OS X malware, the future may not be rooted in machine learning algorithms, but patterns and heatmap visualization, a researcher posits. In an academic paper published by Virus Bulletin on Monday, Vincent Van Mieghem, a former student at the Delft University of Technology in the Netherlands, describes how a recurring pattern he observed in OS X system calls can be used to indicate the presence of malware. Van Mieghem wrote the paper, "Behavioral Detection and Prevention of Malware on OS X," (.PDF) while interning at Fox-IT but has since moved on to PricewaterhouseCoopers' cybersecurity division. By the numbers, the detection method Van Mieghem concocted is a success; it detected infections from 100 percent of malware samples found on OS X systems at the time. The method apparently leaves little room for error too; it resulted in a scant 0 percent to 20 percent false positive rate, depending on the user, according to the paper.