Profile of James KobielusLead Analyst, SiliconANGLE Wikibon
News & Commentary Posts: 35
Jim is Wikibon's Lead Analyst for Data Science, Deep Learning, and Application Development. Previously, Jim was IBM's data science evangelist. He managed IBM's thought leadership, social and influencer marketing programs targeted at developers of big data analytics, machine learning, and cognitive computing applications. Prior to his 5-year stint at IBM, Jim was an analyst at Forrester Research, Current Analysis, and the Burton Group. He is also a prolific blogger, a popular speaker, and a familiar face from his many appearances as an expert on theCUBE and at industry events.
Articles by James Kobielus
Understanding how AI models and AI-based applications work, what they can and cannot do, and where there is potential for ethical issues is core to putting AI to work for your organization.
As enterprises embark on AI and machine learning strategies, chip makers like NVIDIA, Intel and AMD are battling to become the standard hardware providers.
If this approach gains traction, it could have a disruptive impact on the standard practice of data science.
AI-based test automation will be a key feature of DevOps in enterprise multi-cloud environments in the 2020s.
As more individuals, governments and companies see artificial intelligence as evil, it becomes clear that we need metrics to ensure that AI is a good citizen.
While artificial intelligence and machine learning hold promise in cybersecurity initiatives, there are some gaps that enterprises need to consider.
If some forms of artificial intelligence may put lives at risk, what are some of the options for safer deployment of the technology.
From issues such as privacy and data bias regulation to model training and self-service AI, you can expect a broad range of key advances in the artificial intelligence space.
Recent computer industry developments set the stage for demonstrations of how blockchain can help to turn Internet of Things networks into trusted environments.
More applications utilizing facial recognition are inevitable in the public and private sectors, and those will generate plenty of new rules and regulations.
The next phase in our move to the rent-don't-buy economy shapes up with "device as a service".
The end-to-end cloud ecosystem must mature rapidly to support enterprise deployment of AI and machine learning applications.
There's lots of talk about where blockchain will be used. Next up: The cloud.
The way you walk, the way radio waves reflect off your body, and your body's thermal signature all play into the ability for AI to identify you without the use of a camera.
Eliminating bias in the data and algorithms that drive artificial intelligence and machine learning initiatives requires constant vigilance on the part of not only data scientists but up and down the corporate ranks.
Research by neuroscientists promises to make attentional mechanisms a core feature of artificial general intelligence, and thus help to make the IoT so much more than commonly thought.
Next up for developing artificial intelligence systems is automated neural-net architecture search.
Those digital devices that get you through the day will get less chatty and more focused on working for you.
There are plenty of concerns about the safety of artificial intelligence, and it's up to humans to set the standards for safe uses of the technology.
Spark is the shiny new thing in big data, but how will it stand out? Here's a look at "fog computing," cloud computing, and streaming data-analysis scenarios.
Software-defined storage and hybrid deployment approaches may keep disk drives and other 'obsolete' technologies around longer than you'd expect.
Some believe a new C-level data scientist will inevitably control the purse strings on data-related projects. Here's why that's a bad idea.
The acquisition will advance graph analysis against clickstreams and social media messages. Competitors will respond on the "No SQL" and social network analytics fronts.
Technical differentiation continues to narrow among leading vendors. The next Forrester Wave will see a push into cloud-based data warehousing.
Forrester's plans for covering self-service, pervasive, social, scalable, cloud and real-time analytics in the coming year.
Most people in communities are essentially there for the ride, contributing little while benefiting from whatever resources the more generous among them have chosen to share... That's fine, as long as you keep encouraging actively engaged individuals -- whom Forrester refers to as CRM Highly Empowered & Resourceful Operatives (HEROes) -- to keep the useful content coming.
Rome was not reinvented in a day. Your enterprise business processes won't turn around overnight either. You'll need to re-engineer processes while you continue to run a business -- albeit one with many buried layers, some splendid ruins, and many construction projects that cause never-ending traffic snarls.
Is there a real difference between these two terms, or are you seeing double? My initial impetus for a podcast on leveraging the power of social media and social networks to manage your brand was to spell out the chief distinctions between these two terms...
When IT professionals speak of "agile development," they could be referring to any of countless overlapping schools of thought. It's best to tread lightly and keep an agile mind to find some hybrid or innovative approach that specifically meets your needs.
The latest Forrester Wave on Customer Relationship Management (CRM) Customer Service Solutions includes 19 vendors evaluated against 196 criteria. As you can well imagine, it took time to compile the research and double-check the facts before we scored these sophisticated product suites.
Hadoop is riding the hype wave right now. You'll find many IT professionals who know just enough about Hadoop to be dangerous in a cocktail party setting, but not enough for their own comfort to respond to grilling from the chief technology officer or the geekier business executives.
Do you prefer the broad or the narrow definition of BI, and how should we differentiate BI and analytics? Read on for my "extended jam" on these and other FAQs about business intelligence.
What do you need for a full-featured process analytics platform?... Here's a little peek ahead at what I plan to share on this topic at the May 26-28 Forrester Information Technology Forum in Las Vegas...
Approximately one in three companies implementing traditional BI also uses advanced analytics. In other words, if, say, 3 percent of employees in BI-implementing firms use traditional BI, that would correspond to 1 percent of those firms' employees using advanced analytics...
Social networks have their foundations in the space-time continuum -- you know, the funky coordinate system that Einstein was so keen about... Down deep, I consider social network analysis an important new branch of decision support systems as a discipline.