Do you remember ten years ago when phones used to be the prime point of contact for customer-related issues? Businesses could rely on call centre agents to respond to customers in a couple of days. It took a while, and the communication options were limited. Today the rise of e-commerce, multiple communication channels and the proliferation of mobile devices has meant that consumer behaviour has significantly shifted. We now demand access to immediate information and want our issues to be solved instantly at the click of a button. However, while advancements such as the move to mobile and e-commerce have undoubtedly propelled the customer service industry forward, it's the technology that is still evolving, like machine learning and AI, which will have a much larger impact on the future of businesses' customer relationships.

IBM Watson may be able to win Jeopardy!, but security experts are skeptical about the technology's ability to defeat today's cyberthreats. The IBM Watson for Cyber Security beta program launched this week with 40 partners around the world in an effort to help security analysts make better, faster decisions from vast amounts of data, but experts said this is the same promise offered by many other products. IBM said Watson for Cyber Security will feature natural language processing that can help it to "understand the unique language of security." "The truth is, a lot of security vendors today are attaching [artificial intelligence] or cognitive to a number of products that are really just advanced analytics or machine learning, which are also important elements that can help in the fight against cybercrime," Diana Kelley, executive security adviser for IBM Security, told SearchSecurity. "What Watson will bring to the equation that is unique is the ability to digest vast amounts of both structured data, as well as all of the intelligence that exists in natural language, like blogs, white papers and research reports. For example, there are around 10,000 security research papers published each year, and 60,000 security blog posts published every month."

Sure, you can already take steps to keep your Twitch chat friendly, but it's a lot of work if you don't have a team of moderators. Do you really want to watch conversations like a hawk in case someone gets around your meticulously crafted filters? You might not have to after today. Twitch is introducing an AutoMod feature that uses a mix of machine learning and natural language processing to keep "inappropriate content" out of your stream chats. It not only screens for offensive language, but can spot attempts to dodge your filters through clever uses of characters and emoji.

THIRUVANANTHAPURAM: It seems machines replacing humans is not science fiction anymore. Artificial Intelligence (AI) is the buzzword now in the IT field and many companies have started imparting training for employees in AI. Anoj Pillai, chief architect of UST Global's first, spoke at length about how AI was changing the world, at the company's developer's conference. Observing that AI would replace 80% of IT helpdesk, he said, "Labour-centric services will be wiped off eventually. For example, a person who attends a customer's call could be replaced by a machine. It is not going to change the employee. Only his profile will change," he said.

Artificial intelligence and machine learning have become key drivers of innovation. Machine-learning algorithms significantly improve detection time for modern threats, as they can analyze large amounts of data significantly faster than any human could. If trained to accurately detect various types of malware behavior, machine-learning algorithms can have a high detection rate, even on new or unknown samples. The merging of human ingenuity with the speed and relentless data analysis of machine learning significantly accelerates reactions against new malware, offering protection even from previously unknown samples – advanced persistent threats, zero-day attacks, and ransomware. Detecting ransomware, for example, requires several algorithms, each specialized in detecting specific families with individual behaviors.

Amid concerns that robots will replace human workers at an increasingly rapid pace thanks to artificial intelligence, Microsoft's new initiative aims to fund start-ups that build AI that has a positive impact on society. To qualify, the companies must be "designed to assist humanity; be transparent; maximize efficiency without destroying human dignity; provide intelligent privacy and accountability for the unexpected; and be guarded against biases," Microsoft said in a statement. The investment expands Microsoft's start-up investing, which had previously focused on cloud companies. It comes at a time when rivals like Google, Apple and Amazon have accelerated their efforts in artificial intelligence. Of course, not all aspects of artificial intelligence have been well received by tech leaders, many of whom signed an open letter calling for AI systems that are "robust and beneficial [to society]."

Netflix employs an algorithm that helps its users discover movie options, and now it's about to help discover new planetary systems. Researchers at the University of Toronto Scarborough have developed a new approach to identifying stable planetary systems based on the machine learning artificial intelligence Netflix uses. "Machine learning offers a powerful way to tackle a problem in astrophysics, and that's predicting whether planetary systems are stable," Dan Tamayo, lead author of the research and a postdoctoral fellow in the Center for Planetary Science at the University of Toronto Scarborough, said in a press release. Machine learning is a type of artificial intelligence that allows computers to learn new functions without being programmed. This is how Netflix can make scarily accurate predictions of what you're interested in watching without you telling it.

There is quite a lot of confusion about the differences between machine learning, cognitive computing and artificial intelligence. Is there an easy distinction? Josefin Rosén (JR): I think the easiest way of thinking about it is that machine learning is basically a subfield of artificial intelligence. Then you can think of cognitive computing as artificial intelligence plus elements of natural language processing. So cognitive computing understands input like text, voice and video, and it can reason and create outputs that can be used and consumed by humans, not just computers.

Artificial Intelligence is becoming a game for the big dogs. To succeed in genuine artificial intelligence efforts, beyond trivial prediction, you need three things. AI is emerging, so you need to be able to recruit and attract a sufficient core of people and create the culture that allows them to explore. You also need mechanisms to exploit their progress commercially. The second is training environments, which include training data but also systems which let you train and test your systems.

'Tis the season for the public relations exercise known as "here's what we think (or hope) will happen in the tech sector next year," flooding my inbox with predictions for 2017. No one knows what will happen tomorrow, let alone over the next 12 months, but the exercise yields interesting insights into what's hot (and what's not) in technology today. Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous "new big thing," big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market.