IBM's latest acquisition helps enterprises spot 'bad data' at source
As the volume of data continues to grow at an unprecedented rate, organizations often struggle to manage the health and quality of their data sets.
To deal with this IBM has announced that it's acquiring Israel-based Databand.ai, a provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality before it impacts their bottom-line.
Financial services leaders struggle to keep up with AI developments
Research released today shows that 78 percent of financial services enterprise leaders are finding it a challenge to keep up with the speed of AI model and data growth.
The UK research, based on survey of 125 financial services leaders, from SambaNova Systems reveals that the top challenges to deploying enterprise AI include finding or customizing the models/algorithms for their needs (67 percent), setting up infrastructure (33 percent) and preparing data (38 percent).
How web scraping has gone from niche to mainstream [Q&A]
Web scraping -- collecting data from websites -- has been around almost as long as the internet has existed. But recently it's gone from a little-known niche to a serious activity, using automation to collect large amounts of information.
We spoke to Julius Černiauskas, CEO of data acquisition company Oxylabs to find out more about web scraping and how it has evolved.
Instagram to use video selfies as one method of age verification
Like most social media platforms, Instagram has a minimum age for users -- in this instance, it is 13 years old. But verifying ages online -- particularly for non-adults -- has long proved difficult. Now Instagram thinks it has come up with a solution.
As part of measure to enforce age limits more strongly, Instagram is preparing to use a variety of techniques to confirm that younger users are the age they claim to be. One of the options that will be available to such users is uploading a video selfie which will be shared with age-checking agencies.
Microsoft to retire some facial recognition technology as it takes a more responsible approach to AI
Microsoft has publicly shared its Responsible AI Standard which includes its guidelines for building AI systems. The company says it is publishing the standard in order to "to share what we have learned, invite feedback from others, and contribute to the discussion about building better norms and practices around AI".
In addition to publishing the Responsible AI Standard, Microsoft has also announced that it is closing down some of the capabilities of its Azure Face facial recognition service. Features that are being retied include those that can be used to "infer emotional states and identity attributes such as gender, age, smile, facial hair, hair, and makeup".
Why do development projects fail?
Why do development projects fail? And perhaps more importantly what do senior management need to understand about why they fail? Those are the questions that a new study from AI platform vFunction sets out to answer.
Based on a survey by Wakefield Research of 250 US software developers and architects, at a senior level within enterprises of 5,000 or more staff, it looks at the differences in goals, challenges and reasons for failure between business leaders and architects.
Could democratization of AI help prevent the Great Resignation? [Q&A]
The Great Resignation has hit the IT industry harder than most, with recent figures from Gartner suggesting only 29 percent of global IT workers have a ‘high intent’ to stay in their current role.
AI is sometimes blamed for reducing the number of jobs, but could a democratization of AI in the workplace help retain staff by giving them the skills to be more involved in the flow of work?
Chatbots -- are they more artificial than intelligent? [Q&A]
When you contact a large organization it's increasingly likely that, in the first instance at least, you'll find yourself dealing with a chatbot rather than a real person.
Most of these are based on some form of AI, but are they really all that clever? Deon Nicholas, CEO and co-founder of Forethought, doesn't think so. We spoke to him to find out more and discuss whether there might be a better solution.
The importance of responsible AI
Artificial intelligence (AI) is growing and shows no signs of stopping -- almost. In 2020, IDC estimated global spending on the technology would more than double by 2024 to hit $110 billion. Investors feel the same enthusiasm. CB Insights reported venture capital for AI startups in Q3 2021 reached a record $17.9 billion. Yet, even in the bright light of such success, a shadow is being cast. Even as AI is exploding, trust has leveled out, and that could eventually stall its progress and acceptance if we are not careful.
Given how widely AI is being deployed, many organizations are content to look the other way; so long as there’s value, no need to ask questions. But what about transparency and responsibility? If a company can’t trust its own algorithm, why will consumers? Case in point is the Apple Card launch in 2019 in which a noticeable difference in credit lines offered to men vs. women was revealed. Turns out, a faulty AI design failed to have gender input. Further, Apple hadn’t been following the algorithm closely for bias. That’s how launches and reputations are undermined.
UK IT leaders struggling to keep up with AI due to talent shortage
The ongoing skills shortage is causing problems for IT leaders when it comes to implementing AI. New research from SambaNova Systems finds that for 80 percent of UK IT leaders, it's a challenge to keep up with the speed of model and data growth.
The top challenges when deploying enterprise AI include, finding or customising models and algorithms at 67 percent, setting up infrastructure (43 percent) and preparing data (38 percent).
APIs or custom AI? Everything businesses need to know before taking the leap
The call to implement Artificial Intelligence (AI) is becoming difficult for businesses to ignore. Offering the promise of increased organizational productivity, speed and accuracy, some applications can be greatly beneficial to firms across a wide variety of industries and sectors.
That said, companies will naturally have some difficulty deciding on how best to implement AI, and where to achieve the best return on investment in innovative technology. Given the inherent difficulties involved in building an AI solution, finding a solution that is the perfect fit can be a mammoth task, involving great resource and even greater costs. For some, the drawbacks might even outweigh the benefits; perhaps this is why less than 15 percent of firms have implemented AI in their operations.
How artificial intelligence and machine learning are changing the development landscape [Q&A]
It's an increasingly rare application these days that doesn’t claim to incorporate some form of artificial intelligence or machine learning capability.
But while this may be great from a marketing standpoint it does pose a challenge for developers. We spoke to Luis Ceze, CEO and co-founder of OctoML, to find out more.
What is needed to make digital transformation work? [Q&A]
Digital transformation is a topic that's been in the air for more than just a few years now, but the impact of the pandemic and the need for businesses to adapt has rapidly brought it back to the forefront.
It's also no longer just about IT. Digital transformation is an enterprise-wide endeavor, connecting and affecting all business units and requires a shift in mindset to take full advantage of the opportunities it offers.
Microsoft announces Project Volterra to breathe life into Windows on Arm
In addition to revealing that Windows 11 users can look forward to installing third-party widgets later this year, Microsoft also used Build 2022 to announce Project Volterra.
Project Volterra is a developer-focused device powered by a Snapdragon processor, which looks remarkably similar to a Mac Mini. Featuring an integrated neural processing unit, the developer device will provide, Microsoft devs, opportunities to explore various AI scenarios.
The challenge of shifting left: Why AIOps is essential
Development teams are being forced to 'shift left'-- under pressure from the business to move more and more work closer to the design and development phase, earlier in the process.
The idea is to catch bugs earlier, before they turn into costly production outages, and should improve efficiency while minimizing risk within the software development cycle. Yet this demand puts even more pressure and responsibility on developers.
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