Machine learning has joined artificial intelligence (AI) as the hottest technology topics of 2018. We asked our expert influencers to share their thoughts on the state of the industry: where it's going, and how and why companies should be adopting machine learning and AI.
Big Data and Analytics Hub spoke with IBM Distinguished Engineer John Thomas (@johnjaithomas) about some of the importance of tuning information architecture to make algorithms meet enterprise needs, as well as how machine learning can most effectively be applied in hybrid scenarios in 2018.
What will happen to companies who don't embrace data? What do the next five years hold? What's the difference between AI and machine learning? Steve Ardire and Adam Gabriel tackle these questions and more during this special post-event Facebook Live session.
There’s no doubt data science and machine learning are main areas of focus for enterprises to better their business. However, talking about data science and machine learning isn’t the same as making it a reality.
Data already is the new currency and is at the heart of everything digital. I like to repeat the adage, “Data becomes Information, becomes Knowledge, becomes Wisdom”. And “It’s all about the data”. So why do we send up probes, sensors or satellites — for the data?
One of the hallmarks of the cognitive era of business is that companies can can be positioned to unlock insights from unprecedented volumes of data. Advancements in cognitive computing and artificial intelligence (AI) might hold the most significant opportunity where companies can win with data-
As a business technology professional, you need to manage your company’s information resources 24x7 while juggling concurrent projects and staying up to speed on changes in the technology and in your chosen field. You’re stretched thin but continue to seek out professional learning opportunities
The reality is that AI is still heavily-reliant upon smart, willing and trained humans in order for AI to behave in a manner that we would expect. Humans are needed to scope the problems, identify relevant examples and verify the results. Without humans as a guide, current AI is no more capable
Seth Dobrin is vice president and CDO, IBM Analytics, platform development, at IBM. In this podcast, Dobrin shares experiences using Apache Spark for data science transformation and some thoughts on a larger vision for data science transformation at scale.
The financial industry faces a wide range of priorities including customer experience, instant fulfillment, cyber security, risk management and compliance, and expenses. A modern financial services platform is needed to strengthen financial businesses as they progress into the future. And this
Are you looking for a book to help you navigate through the extraordinary changes that technology is bringing to our world? Change is a constant in life. Take a quick skim through these four books that help you better understand how artificial intelligence, blockchain, cognitive computing and
Now that we’re into the swing of 2017, the time is ripe for the first CrowdChat of 2017 to explore the goals, challenges and strategies that CDOs and CIOs are focused on for their organizations. Get involved and share your thoughts in this kick-off IMB Big Data CrowdChat.
One thing that a recent event in Beijing, China confirmed is there’s no shortage of interest in machine learning for developers in that region. Take a look at snapshots of event highlights featuring rich content on artificial intelligence, cognitive capabilities, machine learning and more presented
Cognitive technologies such as advanced analytics and artificial intelligence are emerging as vital tools for achieving digital transformation outcomes. Learn more about the role of cognitive technologies from early adopters in the recent IBM Cognitive Advantage report and how they can be used to