Digging into customer analytics can improve sales opportunities — but how does an organization balance that against data privacy concerns? Get insights from data professionals.
Gaining a reputation as a viable technology in niche applications like X-ray scans, fingerprint matching and robotics, computer vision looks to mainstream, commodified apps.
New tools and cutting-edge projects show how machine learning and advanced analytics may soon revolutionize how software is designed, tested, and deployed.
For most organizations, DIY artificial intelligence is out of reach. Here’s how to cut through the hype and create business value with off-the-shelf AI.
Deciding whether to buy off-the-shelf AI or build your own AI-based business solutions is a complex equation based on available talent, business needs, desired outcomes, lock-in comfort and a rapidly evolving market.
In the age of GDPR and privacy regulations, special attention must be paid to user privacy. Data management tools that employ AI as part of analytics can help achieve that balance.
Data scientists can choose from a growing list of commercial and open source platforms that ease data access, analytics, model building and management in a collaborative way.
From customer service to risk management, artificial intelligence is ushering in the next financial revolution — as long as compliance issues can be addressed.
Pairing artificial intelligence or machine learning with traditional fuzzing techniques creates a powerful tool to find application or system vulnerabilities — for both researchers and cyber criminals.
As enterprises further develop artificial intelligence projects, they are finding that some roles are essential to success. But the right talent can be hard to find.
The emergence of AI-as-a-service tools is helping more enterprises access the benefits of AI, not just the leading-edge tech companies that pioneered the technology.
The race to attain artificial general intelligence is on. Ranging from predictions of 10 to 200 years away, the one thing experts can agree on is that common sense AI is the next step in the journey.
Early adopters are beginning to reap real business results from artificial intelligence implementations. But rolling out an AI initiative isn’t without its challenges.
The days of simple, linear regressions powering machine learning are on their way out, as newer, more powerful deep learning techniques find a range of enterprise use cases.
Workplace AI: Emerging technologies, ethical questions