WHY IT MATTERS: Digital Transformation

This Constellation ShortList™ presents Artificial Intelligence and Machine Learning Best-of-Breed Platforms relevant to early adopters pursuing digital transformation. Offerings included in this document meet the threshold criteria for this category as determined by Constellation Research.

GPT-3 is the most powerful language model ever. Its predecessor, GPT-2, released last year, was already able to spit out convincing streams of text in a range of different styles when prompted with an opening sentence. But GPT-3 is a big leap forward. The model has 175 billion parameters (the values that a neural network tries to optimize during training), compared with GPT-2’s already vast 1.5 billion. And with language models, size really does matter.

There is great potential business value in a variety of retail use cases, many of which work in tandem to achieve a seamless, multi-channel multi-device customer experience, including:

• Personalized suggestions and recommendations
• Trend prediction
• Customer churn prevention
• Targeted discounts and advertising
• Stock optimization
• Consumer session tracking
• and more

It’s where forward-thinking managers should be focusing.

1. First, catalogue your business processes. 
2. Second, focus on simple problems. 
3. Third, don’t use machine learning where standard business logic will suffice.
4. Fourth, if a process is complicated, use machine learning to create decision support systems.

Artificial intelligence has not only become an international arms race, competition has now heated up as companies look to adopt machine learning/deep learning at an unprecedented pace. But the conversation about AI has largely focused on pitting humanity against AI, instead of what happens when we bring together humans *with* AI. In this talk by a16z operating partner Frank Chen, given at the annual a16z Summit, Chen goes beyond the hype to look forward at how AI and automation will augment, enhance, create, and yes, replace humans…. but also highlighting what it is that makes us human to begin with.

The child AI is really only capable of a specific task – image recognition. Using its AutoML AI, Google’s AI-building AI created its child AI using a technique called reinforcement learning. This works just like machine learning, except it’s entirely automated where AutoML acts as the neural network for its task-driven AI child.

In May, J.P. Morgan’s quantitative investing and derivatives strategy team, issued the most comprehensive report ever on big data and machine learning in financial services.
Titled, ‘Big Data and AI Strategies’ and subheaded, ‘Machine Learning and Alternative Data Approach to Investing’, the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don’t they’ll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

Far from being incomprehensible, the principles behind neural networks are surprisingly simple. Here's a gentle walk through how to use deep learning to categorize images from a very simple camera.

Andrew Ng, chief scientist at Baidu and professor at Stanford, said during his widely popular NIPS 2016 tutorial that transfer learning will be -- after supervised learning -- the next driver of ML commercial success.

The AI Index is an open, not-for-profit project to track activity and progress in AI. It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists and others to rapidly develop intuitions about the complex field of AI.
AI is the new electricity, and is transforming multiple industries. The AI Index will help current generations track and navigate this societal transformations. It will also help future generations look back and understand the AI's rise.

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

This technology is called face recognition. Facebook’s algorithms are able to recognize your friends’ faces after they have been tagged only a few times. It’s pretty amazing technology — Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!

What AI can — and cannot — do for your organization.

MouseLight is the most detailed map of the mouse brain yet. Scientists hope this brain map, and others like it, can help unravel the brain's mysteries.

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

Teachable Machine, an experiment that makes it easier for anyone to explore machine learning. Teach a machine using your camera – live in the browser, no coding required.

Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. AI investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.

What is artificial intelligence? Why is it important? Why is everyone talking about it all of a sudden? If you skim online headlines, you’ll likely read about how AI is powering Amazon and Google’s virtual assistants, or how it’s taking all the jobs (debatable), but not a good explanation of what it is (or whether the robots are going to take over). We’re here to help with this living document, a plain-English guide to AI that will be updated and refined as the field evolves and important concepts emerge.

In October 2016, in a big milestone for artificial intelligence, Microsoft unveiled a system that can transcribe the contents of a phone call as well or better than human professionals.

But while Microsoft's system had fewer transcription errors than the average human transcriptionist, it still couldn't best a team of trained humans. So, the world of academia fired back with a new challenge: Lower the error rate to below what human teams can do. 

Now Microsoft has done just that.

Intensive efforts to re-create human cognition will transform the way we work, learn, and play

Researchers from the Facebook Artificial Intelligence Research lab (FAIR) recently made an unexpected discovery while trying to improve chatbots. The bots — known as “dialog agents” — were creating their own language — well, kinda. Using machine learning algorithms, dialog agents were left to converse freely in an attempt to strengthen their conversational skills.

Google quietly released an academic paper that could provide a blueprint for the future of machine learning. Called “One Model to Learn Them All,” it lays out a template for how to create a single machine learning model that can address multiple tasks well.

80 private companies in cybersecurity that are using AI and categorized them into 9 areas of operation.