Machine learning techniques continue to evolve with increased efficiency for recognition problems. But, they still lack the critical element of intelligence, so we remain a long way from attaining AGI.
How do you put together a solid data science team when it comes to developing data-driven products? A variety of roles are available to consider, so which ones do you need and which are most crucial?
What happened at Zillow? An important real-world lesson in... just because you have a cool AI tool, doesn't mean that alone becomes your business model.
As a result of the efforts outlined in this article, we confirmed that clustering through crowdsourcing is indeed possible and works impressively well.
The hiring run for data scientists continues along at a strong clip around the world. But, there are other emerging roles that are demonstrating key value to organizations that you should consider based on your existing or desired skill sets.
Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.
PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.
Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.
Building a solid data team
A $9B AI Failure, Examined
Why Machine Learning Engineers are Replacing Data Scientists
Where NLP is heading