Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 3 intelligent process automation use cases and how they work

  Enterprises are pursuing intelligent process automation to take their digital transformation and RPA applications to the next level. Here's a look at three use cases. Continue Reading

• Neural network applications in business run wide, fast and deep

  Neural network uses are starting to emerge in the enterprise. This handbook examines the growing number of businesses reporting gains from implementing this technology. Continue Reading

• Deep learning and neural networks gain commercial footing

  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. Continue Reading

• Ethical concerns of AI call growing adoption into question

  AI tools are getting easier to use every day, putting powerful tools into the hands of potentially malicious users. The time to think about the ethics of AI advances is now. Continue Reading

• Human-AI collaboration produces top results

  Humans and machines have different -- and often complementary -- strengths and weaknesses. That's why we're not seeing automation leading to mass job losses, at least for now. Continue Reading

• How automated machine learning tools pave the way to AI

  Every enterprise is trying to get to machine learning and, ultimately, AI, but not every business has the level of skill in-house to make it happen. Is automated machine learning the answer for them?Continue Reading

• Is artificial general intelligence possible in our lifetime?

  Artificial general intelligence aims to create a wide-reaching, common-sense AI that behaves in a human fashion, but researchers and experts are questioning its plausibility.Continue Reading

• AI gig economy sets workers and bots on collision course

  The future of work has shifted toward a gig economy, with high-value, short-term workers on demand for organizations. The fast turnover and high volume demand AI to reduce friction.Continue Reading

• Clashes between AI and data privacy affect model training

  Enterprises' lax data rules reveal weaknesses around AI and model training -- particularly machine learning's reliance on unrestrained big data collection.Continue Reading

• 5 types of machine learning algorithms you should know

  Machine learning can assist enterprises by quickly modeling large data sets. Choosing the right algorithm depends on the desired outcome and the makeup of your data science team.Continue Reading

• Full benefits of voice assistant tech yet to be realized

  Voice assistant technology is advancing rapidly, thanks to substantial vendor investment. But a new benchmark report reveals the most popular assistants still leave much to be desired.Continue Reading

• How to choose the right autoML platform for your enterprise

  Before autoML can improve model building and deployment, enterprises need to choose a platform. Here, we evaluate autoML platforms by category, key features and accessibility.Continue Reading

• Enterprises work toward AI trust and transparency

  If ignored, a lack of trust in AI algorithms could diminish user adoption. To remedy this risk, enterprises are working to make their applications more transparent and explainable.Continue Reading

• Choosing the right chip foundation for AI-optimized hardware

  Every enterprise is trying to implement AI and machine learning. But, before AI, before clean data and before platform comparison, enterprises need to find the best hardware to support AI.Continue Reading

• 3 GAN use cases that showcase their positive potential

  GANs' ability to create realistic images and deepfakes have caused industry concern. But, if you dig beyond fear, GANs have practical applications that are overwhelmingly good.Continue Reading