Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
When it comes to enterprise data and analytics investments among enterprise IT buyers, recent GlobalData research points to a future dominated by all things cloudy.
But how are buyers prioritizing specific areas of investment such as workloads, including BI, data warehousing, and AI? We dive into a new survey of more than 3,000 IT practitioners to find out.
As an industry analyst, when a new survey arrives at your doorstep, you greet it as you would the arrival of an old friend who’s been oversees exploring other cultures and climes. After the customary hugs and hellos, it’s instantly down to business. Where did you go? What did you see and learn? And so it was with our newly returned 2018 Global IT Customer Insight survey. Now in its sixth iteration, this annual survey plumbs the international depths of the IT buyer landscape to discover current and future buying priorities across a wide array of investment areas. Continue reading “Enterprise Data and Analytics Cloud Migration Priorities: Part One”→
Walmart’s announcement that it would use Microsoft’s cloud platform and desktop apps across its entire business looks to be a direct shot across the bow of Amazon, a direct counterpoint to Amazon’s own ‘All In’ marketing mantra.
Instead, this partnership speaks more to the operationalization and unification of synergistic technologies – AI, IoT, big data – as a means of speeding time to market for Walmart’s numerous customer engagement projects.
During its AI developer conference, Baidu made several announcements that demonstrate how it is moving the Chinese AI market forward, but the release of its Kunlun chip stands out as a key move that repositions it in the not only the Chinese market, but also globally
With Kunlun, Baidu joins the ranks of a select few companies that not only offer an AI platform that helps enterprises deploy AI-infused solutions, but that have also developed their own hardware to maximize AI processing.
Baidu is hot on the heels of the likes of Microsoft and Google. Although already known as an ambitious player in the AI realm, primarily in China, the search engine provider hasn’t managed to establish itself as a major force in the space, until now. Earlier this month, Baidu announced that it is bringing to market an AI-optimized chip, called Kunlun. With the move, Baidu joins the ranks of a select few companies that not only offer an AI platform that helps enterprises deploy AI-infused solutions, but have also developed their own hardware to maximize AI processing. Continue reading “Release of AI-optimized Kunlun Chip a Game Changer for Baidu”→
Successful AI projects take a village; project teams that include members from groups across the company are more likely to uncover the ‘what-if’ and ‘then what’ questions that are best addressed early.
GlobalData’s 2018 survey found that close to 40% of businesses include all affected parties in decisions related to big data and analytics solutions.
We’ve all heard that not only are machine learning (ML) algorithms time-consuming to develop and train, but that they also need access to vast data lakes and specialized data scientists. With these requirements, it’s no wonder that businesses tend to focus on identifying the skilled IT-centric resources required for undertaking an AI deployment. But AI isn’t just the playground of data specialists, successful outcomes take a village. Project teams that include members from different organizations across the company are more likely to uncover the ‘what-if’ and ‘then what’ questions that are best addressed early on. HR, legal, finance, customer service, operations, and other business units have much to contribute to a successful AI deployment. Continue reading “With AI Decisions, It Takes a Village”→
At Google I/O this week, Sundar Pichai walked attendees through a number of impressive implementations of AI, one of which showed how Google Assistant could book a haircut and make a dinner reservation via an unnervingly convincing conversation between human and machine.
What happens, then, if that assistant eventually learns how to pass itself off as you?
You know it’s spring when the cherry blossoms appear in force, the birds start singing in unison, and Google CEO Sundar Pichai takes the stage at Google I/O and nonchalantly demonstrates some new bit of technology that simultaneously manages to amaze and terrify. I’m talking about Google Duplex, an interesting blend of natural language understanding (NLU), deep learning (DL), and text-to-speech technology designed to do one thing: use AI to emulate at least one half of an actual human conversation. Continue reading “Google I/O 2018: Did Google AI Just Pass the Turing Test?”→
• Huawei showcased its Video Cloud Platform at its recent analyst event, touting its application for public safety.
• The company pointed to widespread adoption and success in China, but can it find a market for its solution overseas?
During Huawei’s Analyst Summit in Shenzhen, China, executive keynotes emphasized the role of artificial intelligence (AI) in the company’s vision to create a more connected, more intelligent world. The company’s vision is to use AI to improve people’s daily lives and to benefit society as a whole. Unlike some competitors, who often showcase the application of AI to improve the customer experience, or point to use cases that incorporate natural language processing or natural language generation, Huawei was keen to highlight its video strategy. The company has roughly 5,450 members of its staff involved in developing video solutions and eight research and development centers that focus on video technology (three in China, as well as sites in the US, France, Ireland, Russia, and Japan). Huawei envisions several use cases for the application of AI and video, including identification of abandoned objects, intrusion detection, crowd density monitoring, facial control/admission processing, and vehicle, facial, and physical attribute identification. Continue reading “Is Public Safety China’s New Export?”→
There are many AI-savvy chipsets on the market right now, each fine-tuned to support specific AI workloads, development frameworks, or vendor platforms.
But, what if developers could flexibly combine AI-specific hardware resource pools on the fly, on-premises as well as online?
There’s certainly enough buzz in the industry right now about artificial intelligence (AI). If you look beyond the doomsday predictions of a machine uprising, the prevailing view is that AI is a literal Swiss Army knife of circumstance, able to cut through any and all problems, ready to assemble opportunity out of nothing more than data. It seems that every vendor has one or two machine learning (ML) and deep learning (DL) frameworks lying about. It’s no wonder. There’s TensorFlow, Caffe, Theano, Torch, and many, many more to choose from, most of which open source and are quite accessible to the broader developer community. Continue reading “It’s Time to Orchestrate AI Hardware for Maximum Effect”→
• There’s a race right now in high tech to build the first general purpose quantum computer, with industry leaders IBM, Google, D-Wave Technologies, and Intel each building out very different implementations of a single, revolutionary idea — the use of qubits instead of plain old bits.
• But unlike most races, this one has no clear finish line as we’re still figuring out the best approach to quantum computing or to building software for them. Enter IT services powerhouse Atos, which is backing a pure but as yet simulated idea of quantum computing in an effort to garner what matters most, namely the hearts and minds of future quantum developers.
There’s an awful lot of noise in the technology industry right now regarding the promise of quantum computing. A sizable number of dissimilar technology and platform players, ranging from Intel to Google to Atom Computing (a 2018 startup) are all busy building increasingly capable computers that push and pull qubits rather than bits. And as you might expect from such a diverse cast, there are a lot of differing views on how to build such a beast and how to best put it to use. Continue reading “Atos Has a Secret Weapon, and It Rhymes with Awesome Computing”→
• During IBM Think, IBM made several AI-related announcements, some designed for enterprises with complex requirements, and others geared towards helping businesses deploy their first AI solution.
• Although IBM’s new capabilities and tools in support of deep learning are impressive, and position IBM as a thought leader, it’s the steps IBM is taking to help companies just getting started with AI that truly move the market forward.
IBM Think was promoted as an event that would bring together the greatest minds in AI. It featured technologies such as virtual assistants, machine learning (ML), and deep learning (DL), and also touched on hot button issues such as ethics and AI. During her keynote, CEO Ginni Rometty discussed the transformational role that AI will have on the IT market going forward, and she introduced Watson’s Law, describing it as a follow on to Moore’s Law and Metcalfe’s Law. Continue reading “IBM Think 2018: Big Blue Looks to Help Companies Adopt Their First AI Project”→
• When it comes to swapping ones and zeros, quantum computing promises to outpace traditional processors in pure scale.
• Yet its true promise will play out when we learn how to invoke quantum phenomena in order to speed up artificial intelligence (AI).
At last week’s IBM Think conference in Las Vegas, Big Blue and AI chip manufacturer NVIDIA talked up the importance of hardware in resolving AI performance bottlenecks. As it turns out, building a smart AI system demands not only copious amounts of data but also the ability to rapidly run machine learning (ML) and deep learning (DL) algorithms against that data. The trouble is that quite often hardware gets in the way. Continue reading “This is Your Brain on Quantum Computing”→