Articles about Artificial Intelligence

As we move further into the digital age, technologies need to evolve quickly enough to support the constantly changing needs of the modern enterprise. While cloud computing has surged in popularity in recent years, most organizations must simultaneously continue to rely on their legacy systems for many of their core functions.

Despite the cloud’s ability to minimize IT infrastructure costs and adjust resources to meet fluctuating and unpredictable demand while getting applications up and running faster, 71 percent of the Fortune 500 and more than 90 percent of the world’s largest 100 banks, 10 largest insurance companies and 25 largest retailers in the U.S. all continue to depend on outdated systems to power their mission-critical applications.

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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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The popularity of enterprise AI continues to grow but practices and maturity are stagnant as organizations run into challenges implementing AI within their organizations.

The annual AI Adoption in the Enterprise survey from learning platform O'Reilly finds a lack of skilled people and difficulty hiring topped the list of challenges to adopting AI, cited by 19 percent of respondents. This compares to last year when 22 percent named company culture as the major barrier.

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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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Human error and poor security decisions are among the leading causes of data breaches, but it can be hard for security teams to know where to invest resources to address these risks and provide help to employees who need it most.

Tessian is introducing what it calls the Human Layer Risk Hub -- a solution that offers organizations full visibility into employees' risk levels and drivers on email, enabling security and risk management leaders to take a more tailored approach to employee security.

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With large numbers of new samples appearing every day the old signature-based methods of malware detection have become unwieldy.

AI can learn from millions of samples, but if it uses all samples for optimum detection that means slower learning and updates. The alternative is to use only select samples to keep up with the rate of change of malware, but this runs the risk of 'catastrophic forgetting ' of older patterns.

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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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Only 23 percent of knowledge workers say their organization is ahead of the curve in digital capabilities to support knowledge work according to a new survey.

The study from iManage shows 68 percent of knowledge workers believe 'information contained in digital documents and files' is vital to their business. Respondents rate contracts, emails, and spreadsheets as the three most important sources of digital information.

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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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In a new study, 82 percent of respondents cite too many redundant or routine tasks as their most critical IT challenge, but 91 percent agree that automating routine manual tasks by introducing Artificial Intelligence for IT Operations (AIOps) can provide significant benefits across the enterprise.

The Autonomous Enterprise survey from Digitate shows 65 percent say lacking a proactive ability to predict, identify, and detect system issues is a major challenge.

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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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