Articles about AI

There’s no denying that technology has been the key to evolution for almost every industry. From transport to gaming, sports to healthcare, improvements to processes and capabilities have been attributed to rapidly innovating technology. This is particularly prominent in the field of artificial intelligence (AI). What was once reserved for sci-fi movies is now actively a part of our everyday lives, and it’s set to pave the future -- perhaps autonomously!

In this article, we take a look at how AI is set to ignite several key industries.

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As more industries find themselves searching for viable solutions to common problems, they look towards automation. The concept of artificial intelligence (AI) has evolved over the years, and new applications for automation are emerging. It seems as though the world is moving towards automation rapidly, and industries will need to adopt this technology to stay ahead of the competition.

Continue reading to learn more about six autonomous technologies being developed that will change the way industries operate. They will contribute to higher levels of efficiency and productivity by complementing existing technologies.

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A third of financial institutions are accelerating their AI and machine learning adoption for anti-money laundering (AML) technology in response to COVID-19.

Meanwhile, another 39 percent of compliance professionals say their AI/ML adoption plans will continue, despite the pandemic's disruption, this is according to a new study by analytics company SAS, consultancy KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS).

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This seems like a great time to ask this question, as it might not matter soon. With the direction things are heading, we may soon arrive at an intersection where the blurring of identity reality and identity fiction is so extreme that we’ll simply stop asking what is authentic personhood. 

Case in point, a story out of Paris in July outlines not a dystopian future, but a troubling present reality. The piece recounts the story of computer-generated YouTube storytime videos. The genre is pretty much what it sounds like:

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According to a new study from the IBM Institute for Business Value, 71 percent of executives surveyed say mainframe-based applications are central to their business strategy.

Also, in three years, the percentage of organizations using mainframe assets in a hybrid cloud environment is expected to increase by more than two times.

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50 percent of respondents to a recent McKinsey survey reported that their companies adopted artificial intelligence (AI) in at least one business function in 2020. As interest and investment in AI and machine learning (ML) continue to grow across different business functions, is Procurement keeping pace with its business unit counterparts?

Procurement value generation is heavily dependent on fast access to accurate data; while other business functions are automating decisioning using AI, in many organizations today Procurement is still working manually just to collect and clean source data before even getting to the decisioning stage.

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Enterprises are relying on data more than ever before, but that can come at a cost in terms of the time spent on building and managing the infrastructure to handle it.

In order to streamline the integration and efficient scaling of these big data and AI workflows into hybrid cloud environments, IBM Research is launching CodeFlare.

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Recommendations based on AI are something we encounter all the time. From shopping sites, streaming services and social media we're constantly shown stuff that the AI thinks we'll like.

But how easy would it be for an attacker to manipulate these recommendations to promote conspiracy theories or spread disinformation?

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We've all seen the headlines suggesting that AI is racist and sexist. However, many people overlook one important fact -- that AI is simply a tool, incapable of being inherently biased. That’s not to say AI isn’t capable of producing biased outcomes -- as the headlines show, it certainly is. But it can only ever be as biased as the data upon which it relies.

So how can developers and marketers avoid deploying biased AI? Unfortunately, there is no magic one-size-fits-all solution. As with any successful technology deployment within a business, it requires a thorough understanding of the datasets you are working with, and the outcomes AI can produce with said data. The first step is knowing what to look for.

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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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