Articles about AI

These days when you contact a company online it can be hard to know if you're dealing with an actual person or with an AI bot.

Clearly AI has a role to play in automating repetitive tasks and answering straightforward queries. But can it really have a wider role to play in improving customer experience? We spoke to Derek Roberti, VP of technology at Cognigy, a global conversational AI platform provider, to find out.

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If you have a dog then you’ve likely been there with the 3AM trips outside regardless of weather -- rain, snow, bone-chilling cold. If the hound needs to go, you're going out -- after all, you don’t want the alternative. 

There are other fun times, teething for instance. My current black lab cost me two pillows, a house plant and a vacuum cleaner cord. There’s food that needs to be kept out of reach... a lab can easily reach a kitchen counter, I learned early on. 

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Responding to a user request from an AI model -- 'model serving' -- is a key part of making use of the technology. But as the number of models expands serving them all raises problems and can lead to many being rarely used or abandoned.

Which is why IBM is introducing ModelMesh, a model serving management layer for Watson products that is designed to cope with high-scale, high-density and frequently-changing model use cases. It intelligently loads and unloads AI models to and from memory to strike an optimized trade-off between responsiveness to users and computational footprint.

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As we enter the back half of 2021 there are two top cybersecurity headlines, and they’re both sobering. One, even large organizations now suffer cyberattacks as a near-daily fact of life -- not just mid-sized businesses with resource-strapped SOCs (Security Operations Centers), which historically felt the most pressure.

Two, prevention-forward defense strategies no longer inspire confidence. Malicious, innovative use of AI to find and exploit fruitful attack vectors sees to that. AI has rendered many old go-to defenses less effective, namely firewalls and SIEM (security information and event management) solutions. The third headline, however, is cause for optimism. AI works for cyber defense, too. In the current environment, if you are not leveraging AI to defend your organization, it isn’t optimally defended. Period. I see AI as our greatest ally to create a secure future.

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A recent IBM report showed that over 90 percent of organizations are planning to implement edge computing strategies within the next five years.

Now 5G security company Exium is collaborating with Big Blue to help clients adopt an edge computing strategy designed to enable them to run AI or IoT applications seamlessly across hybrid cloud environments.

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IBM is releasing details of its new Telum Processor, designed to bring deep learning to enterprise workloads and help address fraud in real-time.

Telum is IBM's first processor to contain on-chip acceleration for AI inferencing while a transaction is taking place. Three years in development, the breakthrough of this new on-chip hardware acceleration is designed to help customers achieve business insights across banking, finance, trading, insurance applications and customer interactions.

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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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