Databricks just launched a new web series: Data Brew and this is the first episode.

For this first season, we will be focusing on lakehouses – combining the key features of data warehouses, such as ACID transactions, with the scalability of data lakes, directly against low-cost object stores.

In our inaugural episode, we’d like to welcome data warehouse luminaries Barry Devlin, Susan O’Connell, and Donald Farmer to discuss the evolution of data warehouses, data lakes, and lakehouses.Join us for the debut of Data Brew — a new video / podcast series where we explore and debate the evolution of Data + AI. No hype, no spin, just a straight shot of strong opinions from some really smart people.

74% of IoT deployments slow down or stall completely as users grapple with the complexity of provisioning and managing their edge hardware at scale.

Learn how the integration of ZEDEDA’s IoT Edge orchestration solution with Azure IoT makes it possible to fast track and scale your entire project.

Features include one-click bulk provisioning, full lifecycle management of hardware, the Azure IoT Edge runtime, Azure IoT modules and any other installed apps, security enhancements like distributed firewall and concurrent support for legacy apps deployed in Windows VMs.

Check out Zededa Edge Quick Connect on the Azure Marketplace at https://aka.ms/iotshow/ZededaEdgeQuickConnect

If you’ve been paying to attention to my writing over the past 10 months or so, you know that I have seen the light on quantum computing.

Recently, the Microsoft MVP program highlighted two MVPs leading the way in this cutting edge space. My co-host and brother from another mother Andy Leonard, was called out for his work in this space.

Governments and private investors all around the world are pouring billions of dollars into quantum research and development, leading commentators to believe we may now be on the brink of a second quantum revolution as humans attempt to harness even more of the power of the quantum world.

Superconducting materials can do amazing things that appear to defy the laws of physics, but their major drawback is that superconducting properties don’t appear unless a material is cooled to near absolute zero.

Superconductors that would work at (or near)  room temperatures would, without exaggeration, would change the world and would have massive implications for quantum computing.   

Liv Boeree shares this exclusive behind-the-scenes interview with the scientists who just unearthed one of the holy grails of physics: a room-temperature superconductor!

Their discovered material — carbonaceous sulfur hydride — shows superconductivity at 15 degrees Celsius, a temperature FAR above all previous records. It takes us a huge step closer to the long-sought goal of creating electrical systems with perfect efficiency, which would transform the world’s energy grids, computation and transportation systems entirely.

 

Database backups are an essential part of any business continuity and disaster recovery strategy because they protect your data from corruption or deletion.

In this episode of Data Exposed with Uros Milanovic, he’ll share the two types of backups for restoring databases for Azure SQL customers, why database backups are important, how backups are kept, what options customers have, and much more.

Related Resources:

In 2018, Microsoft bought Lobe, a San Francisco-based startup that made a platform for building, training and shipping custom deep-learning models.

I had seen a demo of this last November, but was forbidden from speaking about it publicly. 🙂

This week, Microsoft made some of Lobe’s technology publicly available and I encourage you to check it out.

On October 26, available a public preview of a Lobe app for training machine-learning models. Available for both Windows and Mac, the Lobe app is free and designed to enable people with no data science experience to import images into the app and label them to create a machine learning dataset.

Daniel Bourke reviews the the State of AI 2020 report in the following video.

For the last three years, the State of AI Report has been published as a snapshot of what’s happened in the field of artificial intelligence over the past 12 months. This is my review/walkthrough of the 2020 version.

Read the full State of AI Report 2020 here: https://www.stateof.ai

Index:

  • 0:00 – Intro and hello
  • 1:31 – AI report review start
  • 2:12 – AI definitions
  • 3:00 – SECTION 1: Research
  • 3:37 – Transformers taking over NLP
  • 5:50 – Universities starting AI-based degrees
  • 6:35 – Only 15% of papers published share their code/data
  • 8:20 – PyTorch outpacing TensorFlow in research papers
  • 11:12 – Bigger models, datasets and compute budgets drive performance
  • 15:40 – Increased performance costing more for incremental improvement
  • 18:13 – Deep learning is getting more efficient
  • 21:11 – AI for conversations is getting better
  • 23:02 – Machine translation for code (Python to C++)
  • 25:32 – Many algorithms starting to beat human baseline for NLP on GLUE test
  • 27:04 – Using Transformers for computer vision
  • 30:11 – AI performs incredibly well on mammography tasks across two regions (US and UK)
  • 31:30 – Causal inference in ML
  • 34:30 – ML for synthesizing new molecules
  • 36:26 – AI starts to read DNA-encoded molecules
  • 39:39 – AI generates tennis matches between any tennis players you want
  • 40:46 – Transformers being used for object detection
  • 41:46 – AI which learns from its dreams
  • 44:26 – Really efficient on-device computer vision models
  • 45:04 – Evolving machine learning algorithms from scratch (AutoML Zero)
  • 46:20 – Federated learning is now booming
  • 47:16 – Privacy-preserving ML
  • 48:35 – Using Gaussian Processes for estimating model uncertainty
  • 50:11 – SECTION 2: Talent
  • 50:12 – Many top companies stealing AI professors
  • 52:02 – Abu Dhabi opens the world’s first AI university
  • 54:03 – Many Chinese AI PhD’s depart China for other countries
  • 54:52 – US based companies and institutions dominate NeurIPS and ICML (ML conferences)
  • 56:13 – Three times more AI job postings then views for AI roles
  • 56:58 – TensorFlow and Keras have more job postings on LinkedIn than PyTorch
  • 57:18 – SECTION 3: Industry
  • 59:06 – INTERMISSION
  • 59:44 – AI predicting metabolic response to food
  • 1:00:45 – FDA acknowledge lack of policy for AI-driven systems
  • 1:01:54 – Less than 1% of AI-based medical imaging studies are high-quality
  • 1:02:48 – First reimbursement approval for deep learning based medical imagining
  • 1:05:16 – Self-driving mileage in California still well above autonomous driving mileage
  • 1:10:36 – Supervised ML improvements seem to follow an S-curve
  • 1:12:13 – A new kind of approach to self-driving cars
  • 1:17:17 – New AI-first chips (competition for NVIDIA)
  • 1:19:41 – The rise of MLOps
  • 1:21:52 – Computer vision for auto insurance claims
  • 1:23:45 – Using NLP to detect money laundering and terrorist schemes on the web
  • 1:25:17 – Robots in stock factories are picking millions of items per month
  • 1:26:23 – HuggingFace’s open-source NLP work is driving NLP’s explosion
  • 1:28:09 – SECTION 4: Politics
  • 1:29:59 – Creating a search engine for faces
  • 1:33:29 – GPT3 outputs bias predictions like GPT2
  • 1:33:49 – US military adopting deep reinforcement learning techniques
  • 1:35:52 – Fighter pilots vs AI pilots
  • 1:38:28 – Google’s People and AI guidebook talks about fairness, interpretability, privacy
  • 1:40:46 – China fronts big cash for chip manufacturing
  • 1:45:00 – A call to tackle climate change, food waste, generating new battery technologies and more with ML
  • 1:45:45 – SECTION 5: Predictions
  • 1:49:22 – A special guest appears