He is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. Pedro is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas; a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Pedro has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. Our speaker helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.

If you missed the live event, you can watch the complimentary recording and download the slides by filling out the form. Jorge Torres, CEO of MindsDB, has co-hosted the webinar with Borne to demonstrate how organizations can reduce machine learning complexity by bringing models directly to the data source. Using standard SQL queries and a new type of database table called AI Tables, users can apply machine learning models straight in the database to automatically generate predictions as simple tables.

Ahead of this year's AI Summit London, part of London Tech Week, we invited three Enterprise AI experts to join us for the London Tech Week Digital Series, to discuss responsible AI for business. This webinar is moderated by Aditya Kaul, Research Director at Tractica, who has 12 years' experience in technology market research with a primary focus on artificial intelligence & robotics. Joining Aditya is Ivana Bartoletti, Founder of the Women Leading in AI Network, and Udai Chilamkurthi, Lead Architect for Retail & Logistics at one of the UK's largest supermarket chains, Sainsbury's. In this on-demand webinar, you'll learn how your business can build an ethical framework for responsible AI & unlock the full potential of AI & Machine Learning to transform your enterprise. By accessing this free on-demand webinar by the AI Summit, you'll automatically receive a 20% discount to the upcoming AI Summit San Francisco (Palace of Fine Arts, 25 - 26 September 2019).

Automated Machine Learning (AutoML) has received significant interest recently. We believe that the right automation would bring significant value and dramatically shorten time-to-value for data science teams. Databricks is automating the Data Science and Machine Learning process through a combination of product offerings, partnerships, and custom solutions. This talk will focus on how Databricks can help automate hyperparameter tuning. For both traditional Machine Learning and modern Deep Learning, tuning hyperparameters can dramatically increase model performance and improve training times.

Artificial Intelligence and Machine Learning (ML) can turn massive amounts of data into deep insights that drive revenue and decrease costs. But ML's not an island – in fact, it's carried out most successfully when paired with advanced analytics. To facilitate the best analytics work, enterprises need the right platforms and tools to load data, prepare it, ensure high-quality and integrate with corporate data governance processes. How can you get all that working harmoniously, especially in the cloud? It takes the right tools, strategy and workflow, but it can be done. Watch this 1-hour on-demand webinar, from GigaOm Research, to find out how.

On July 9th, our team hosted a live webinar--Scalable End-to-End Deep Learning using TensorFlow and Databricks--with Brooke Wenig, Data Science Solutions Consultant at Databricks and Sid Murching, Software Engineer at Databricks. In this webinar, we walked you through how to use TensorFlow and Horovod (an open-source library from Uber to simplify distributed model training) on the Databricks Unified Analytics Platform to build a more effective recommendation system at scale. If you missed the webinar, you can view it now as well download the slides here. If you'd like free access Databricks Unified Analytics Platform and try our notebooks on it, you can access a free trial here. Toward the end, we held a Q&A, and below are all the questions and their answers.

When we think of artificial intelligence (AI), we think of robots -- machines that mimic human behavior or thought. This is partly the influence of comics, novels, movies and other pop culture tidbits, but the boundaries of AI have progressed far beyond this basic personification. Originally defined by Alan Turing, AI initially referred to any machine that could approximate human responses under certain conditions. However, AI is now a multifaceted subject. From a technology perspective, recent advancements in machine learning, deep learning and cognitive computing have spurred significant interest in the potential of AI.

According to a recent Smart Building report from Aberdeen Group, modern Integrated Workplace Management Systems (IWMS) – driven by IoT, analytics, and cognitive computing – are central to the rise of the smart building. Buildings and workplaces are massive generators (and consumers) of data. The capture and analysis of data enable organizations to gain deeper insights into operational effectiveness, accelerate their ability to react to change, and increase returns from real estate-related decisions. As cognitive computing services continue to gain momentum, many organizations are starting to explore different ways artificial intelligence can help to optimize occupancy experiences. Data captured by buildings can be augmented by cognitive capabilities for use in IWMS such as IBM TRIRIGA – to help make decisions, alert management on issues, in addition to providing buildings with virtual concierge services.

When we think of artificial intelligence (AI), we think of robots -- machines that mimic human behavior or thought. This is partly the influence of comics, novels, movies and other pop culture tidbits, but the boundaries of AI have progressed far beyond this basic personification. Originally defined by Alan Turing, AI initially referred to any machine that could approximate human responses under certain conditions. However, AI is now a multifaceted subject. From a technology perspective, recent advancements in machine learning, deep learning and cognitive computing have spurred significant interest in the potential of AI.