TensorFlow 2.1.0 has been released, following two release candidates. The final version of the machine learning platform includes new features and breaking changes. Meanwhile, Python 2.7 has reached its end of life and is no longer supported by TensorFlow. Let’s take a look at what else has changed.
Manifold, Uber’s model-agnostic visual debugging tool for machine learning, is now open source and available as a demo version and a GitHub repository. Manifold is built with TensorFlow.js, React, and Redux and is part of the Michelangelo machine learning platform. The open source version includes a few new features that will make for an easier user experience.
Take a look into the crystal ball. What does Gartner predict for 2020? Here are ten strategic trending technologies that tech leaders should have on their radar in the coming years. Augmented and virtual reality might train employees in the future, voting might become based on a blockchain, and AI security might become the most important factor in risk management.
Manual efforts to gather such a huge amount of information could eat up a lot of time. Hence, AI is leveraged to automate the stage and deliver flawless results while saving a lot of time and resources. Embedded AI and ML can help security testing teams in delivering greater value through automation of audit processes that are more secure and reliable.
Saving lives with deep learning, creating smarter chatbots and more: 10 takeaways from ML Conference 2019
ML Conference 2019 had lots of exciting talks and insights to offer. How can we make chatbots smarter and provide machines with abilities such as ethical values or emotional intelligence—and how can deep learning help save lives? We’ve collected 10 takeaways to share some highlights of our Berlin conference.
At ML Conference in Berlin, we caught up with keynote speaker Dr. Janina Loh. Watch the video to learn about the ethical issues surrounding robots and autonomous cars—and why she believes universal guidelines for robots ethics can and should not be established.
Both machine learning and the use of cloud-native environments built on containers are becoming more commonplace in the enterprise. Luckily, Kubernetes and containers are a perfect match for ML. The cloud-native model has many advantages that can be brought over to machine learning and other forms of artificial intelligence for more effective, practical business strategies.
How can we detect deepfakes that have been created with deep learning methods such as GANs? Facebook, AWS and Microsoft joined forces to launch the Deepfake Detection Challenge (DFDC) that should encourage developers to research this issue. Winners can receive up to $500,000 USD.
We interviewed ML Conference speaker Christoph Henkelmann in Berlin. The natural language processing expert shared some insights on Google’s model BERT, OpenAI’s recently fully released model GPT-2, and what the future may hold for NLP.
Generative Adversarial Networks (GANs) have recently sparked an increasing amount of interest, as they can generate images of faces that look convincingly real. What else are they capable of, what risks could they pose in the long run, and what do they have in common with the emerging internet in the 1990’s? We interviewed ML Conference speaker Xander Steenbrugge.
What is the current state of machine learning and data science in enterprises? For the second year in a row, Algorithmia have published their State of Enterprise Machine Learning report. It shows the top machine learning use cases in enterprises and what trends to look out for in the future.
ML Conference and Voice Conference 2019 have started – “Actions are never morally neutral, nor are the products of human actions”
The first day of ML Conference and Voice Conference 2019 started out with an exciting keynote and you can read all about it here. The speaker, Dr. Janina Loh, raised questions about the moral and ethical implications of robots in the digital age.
Human / AI Interaction Loop Training as a new approach for interactive Learning – Livestream of the MLCon keynote
In the final keynote of ML Conference 2019 in Berlin, Dr. Neda Navidi dives into the topic of human-AI interaction, looking at a new approach for interactive learning. Here’s the livestream of the keynote if you couldn’t make it to Berlin this year.
In the second keynote at ML Conference 2019 in Berlin, Dr. Yonit Hoffman dives into the topic of data science: How can data science and machine learning help prevent accidents at sea that cost lives, money and environmental destruction? Here’s the livestream of the keynote if you couldn’t make it to Berlin this year.