Evaluate

Evaluate

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

• How to build a chatbot with personality and not alienate users

  Adding personality to a chatbot can push it toward the uncanny valley and raises ethical questions. But enterprises can make their bots more engaging, while avoiding these hurdles. Continue Reading

• How Getty Images reduces bias in AI algorithms to avoid harm

  In applications from internal job recruiting to law enforcement technology, AI bias is a widespread issue. Here's what enterprises can do to reduce bias in training and deployment. Continue Reading

• Data visualization process yields 360 AI-driven analytics view

  Data visualization tools find increasing uses as part of AI processes to explore data in the initial stages of model development and make outputs easier to digest. Continue Reading

• Data visualization in machine learning boosts data scientist analytics

  Data scientists offer practical insights into the role of visualization tools in building, exploring, deploying and monitoring their machine learning models. Continue Reading

• Building a better conversational AI assistant requires emotion

  Industry after industry is seeing benefits from chatbot implementation, but customers and developers are looking toward a future of more connected, intelligent conversational agents. Continue Reading

• How the top open source AI software drives innovation

  In the world of AI, open source software is driving most of the innovation. But with vendor tools largely sidelined, what does this mean for things like security and technical support?Continue Reading

• 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