Eric Reiss started working with user experience (UX) long before the term was even known. Over the past 40 years, he has encountered many issues that have disturbed him – from creating purposely addictive programs, sites, and apps, to the current zeitgeist for various design trends at the expense of basic usability.
The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.
Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.
The future is bright for AI. Whether data science can change the world or not, that remains to be seen. One thing is sure though: developers should collaborate more efficiently with data scientists & engineers. We talked to David Wyatt, Vice President EMEA at Databricks about the challenges when approaching AI initiatives, the role of Spark in this field and the next step for data engineering and data science teams.
What does the future of tech look like? We asked four experts about their predictions for the next year. Expect to hear lots about DevOps, artificial intelligence, cloud technologies, security, and more.