Deep learning and neural networks are picking up steam in applications like self-driving cars, radiology image processing, supply chain monitoring and cybersecurity threat detection.
To reach full analytics potential, machine learning platforms powered by AI must provide scalability, handle multiple models, integrate with data sources and be cloud-friendly.
IT executives say pricing models, agility and auditability are some of the biggest challenges they have faced in managing today’s increasingly complex data pipelines.
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.
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.
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.
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.