Articles about AI

New research from Deep Instinct finds that 78 percent of SecOps professionals are concerned that cyber adversaries will develop and deploy AI to cause a global cyber incident in the next 12 months.

The study of 600 IT and cybersecurity professionals finds more than half of respondents believe ransomware or zero-day attacks are the biggest threats to their organization.

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We know technology has had an impact on every area of our lives. We can manage almost our entire lives through our smartphones, from arranging appointments to paying bills. Technological innovations have also found a way into the workplace, completely revolutionizing the way we work.

As technology is embedded into workplaces, it can unsettle workers. With technologies including artificial intelligence and automation able to replicate elements of our jobs, it’s easy to see why. Half of UK workers believe they may be replaced by automation, AI, or robots in the next decade. A further 61 percent are concerned about AI.

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Artificial intelligence (AI) and machine learning (ML) have been two of the most disruptive technological advancements of the past several years.

Gartner predicts that by 2024, 75 percent of enterprises have shifted from AI pilot stage implementation to operationalization. Their effects have been wide-ranging and promise to continue into the foreseeable future. Businesses will gain a substantial competitive advantage by capitalizing on the benefits of AI and ML.

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The Federal Trade Commission (FTC) recently published a blog entitled "Aiming for truth, fairness, and equity in your company's use of AI" that should serve as a shot across the bow for the large number of companies regulated by the FTC. 

Signaling a stronger regulatory stance on deployed algorithms, the FTC highlights some of the issues with AI bias and unfair treatment and states that existing FTC regulations -- such as the Fair Credit Reporting Act, the Equal Credit Opportunity Act, and the FTC ACT -- all still apply and will be enforced with algorithmic decision-making.

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The debate about the energy greediness of large AI models is raging. Recently, an AI ethics researcher at Google was dismissed because she had pinpointed the upward spiral of exploding training data sets. The fact is that the numbers make one’s head swim. In 2018, the BERT model made the headlines by achieving best-in-class NLP performance with a training dataset of 3 billion words.

Two years later, AI researchers were not working with billions of parameters anymore, but with hundreds of billions: in 2020, OpenAI presented GPT-3 -- acclaimed as the largest AI model ever built, with a data set of 500 billion words!

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Can artificial intelligence be deployed to slow down global warming, or is AI one of the greatest climate sinners ever? That is the interesting debate that finds (not surprisingly) representatives from the AI industry and academia on opposite sides of the issue.

While PwC and Microsoft published a report concluding that using AI could reduce world-wide greenhouse gas emissions by 4 percent in 2030, researchers from the University of Amherst Massachusetts have calculated that  training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car. Who is right?

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Artificial Intelligence (AI) has quickly emerged as a game-changing technology, often helping researchers uncover insights much faster than they otherwise could.

Here are five impressive ways that researchers have already capitalized on AI this year.

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Every contact center needs to track its compliance with laws and regulations, yet so many make it tougher on themselves to do so. Reviewers screen call after call, checking off compliance steps and ensuring agents meet expectations. But with potentially millions of calls to screen, manual compliance tracking becomes an impossible task -- and workaround solutions can leave centers open to damaging compliance failures, potentially costing companies millions of dollars in fines.

The cost of human error is too high, but manual processes are too overwhelming, time-consuming and expensive. Automating compliance monitoring can remove much of the burden from compliance managers and officers and decrease their chances of human error. Technology like Interaction Analytics helps reduce the traditional pain-points and limitations in reviewing contact center compliance while also unlocking insights on larger trends and ways to improve contact center operations.

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When is a mouse not just a mouse? When it has a dedicated voice button, apparently. In collaboration with Baidu, Logitech is launching an all-new mouse which features such a button. Called "Voice M380 Wireless Mouse with Speech Input," Logitech's newest product is exclusive to China and is priced at 199 RMB -- that's equivalent to about $30 here in the USA.

It appears to have no thumb buttons, but it does have both vertical and horizontal scrolling -- a feature I adore for working with large (particularly wide) spreadsheets. Besides the off-white color seen above, it will also be available in graphite and rose.

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The global enterprise messaging industry accounts for 2 trillion messages annually. Messaging is a great medium for engagement -- it’s instant, and users tend to be much more responsive compared to other media. Also, users tend to utilize messaging apps dozens of times every day. Therefore, brands that do messaging right will see much higher frequency and intensity of engagement than with other media.

Most messages are delivered today using good old-fashioned SMS messaging. However, this sizable enterprise messaging industry is about to change in significant ways. Newer messaging channels are emerging with far greater capabilities -- namely Whatsapp, Rich Communication Service (RCS), Google Business Messages (GBM) and even a new messaging channel launched by Gupshup called Gupshup IP (GIP) Messaging. The common theme across all these channels is that they are all based on data messaging using the Internet Protocol (IP messaging).

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Artificial intelligence (AI) is rapidly gaining momentum as a vital business resource as organizations discover new use cases in their efforts to improve processes, increase efficiency and automate costly, manual tasks. Industries such as financial services are ideal for AI-driven applications and a related technology, machine learning (ML), because they can bolster customer service and leverage data to increase competitiveness.

AI includes software that’s designed to work in ways similar to the human brain, while machine learning encompasses programs that alter themselves based on data that’s fed into the programs in order to train them.

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When it comes to delivering an immersive, exciting, and balanced gaming experience for the player, AI is certainly one of the focus points for developers. From building realism in a virtual world to making sure a game adapts to skill level, it has an increasingly important role to play in the industry.

To take a quick temperature check on the importance of AI for gamers, just look at what can happen when things aren't up to scratch. It was impossible to miss the burning wreckage of Cyberpunk 2077 as it came crashing down last year, with a buggy and faulty AI on display for all to see (among all the other issues). The internet was quickly filled with clips of enemies spawning out of the ether and cars circling the same roundabout ten times before vanishing. The bugs were enough to ruin the game's launch for many, with underperforming sales the end result.

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Despite an inarguably terrible year and many concerning events happening around the world -- pandemic notwithstanding -- technology is still advancing at breakneck speeds. That looks to continue well into the coming year, with many fascinating and emergent solutions bubbling to the surface.

Here are 10 emerging technologies everyone should be on the lookout for in 2021:

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In an intensely competitive economic climate, midmarket customers are caught in an especially challenging position right now. Faced with many of the same problems we see large enterprise customers grappling with -- mushrooming data demands, unpredictable business growth etc. -- but with smaller budgets and fewer personnel to overcome them. 

How can these customers compete against the big IT-spenders? Automation and analytics tools integrated with Artificial Intelligence (AI) and Machine Learning (ML) capabilities hold the answer -- helping to manage ever-growing storage demands more efficiently with fewer personnel, fewer resources and less human intervention.  

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Language AI is one of the most challenging areas of artificial intelligence, one where mainstream AI is far from coming near human-level performance, because it needs world knowledge to be solved (AI complete).

The shortcomings of modern machine learning approaches can be explained by the low efficiency of artificial neural networks. Because natural evolution is mainly driven by efficiency, I developed a strong interest for biologically inspired natural language understanding, hence the following book recommendations.

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