O'Reilly on Data

Closer to AGI?

O'Reilly on Data

DeepMind’s new model, Gato, has sparked a debate on whether artificial general intelligence (AGI) is nearer–almost at hand–just a matter of scale. Gato is a model that can solve multiple unrelated problems: it can play a large number of different games, label images, chat, operate a robot, and more.

On Technique

O'Reilly on Data

In a previous article , I wrote about how models like DALL-E and Imagen disassociate ideas from technique. In the past, if you had a good idea in any field, you could only realize that idea if you had the craftsmanship and technique to back it up. With DALL-E, that’s no longer true.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications.

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Scaling False Peaks

O'Reilly on Data

Humans are notoriously poor at judging distances. There’s a tendency to underestimate, whether it’s the distance along a straight road with a clear run to the horizon or the distance across a valley. When ascending toward a summit, estimation is further confounded by false summits.

Supply Chain Strategy (And Why Yours Isn't Working)

Speaker: Michelle Meyer, Founder and CEO of MatterProviders

Michelle Meyer is here to walk you through the future of supply chain strategy, and why your current approach is probably not working. In this exclusive webinar, she will explore ways to develop and perfect your new supply chain strategy in this post-pandemic era of uncertainty.

The Next Generation of AI

O'Reilly on Data

Programs like AlphaZero and GPT-3 are massive accomplishments: they represent years of sustained work solving a difficult problem. But these problems are squarely within the domain of traditional AI. Playing Chess and Go or building ever-better language models have been AI projects for decades.

2021 Data/AI Salary Survey

O'Reilly on Data

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation.

Where Programming, Ops, AI, and the Cloud are Headed in 2021

O'Reilly on Data

In this report, we look at the data generated by the O’Reilly online learning platform to discern trends in the technology industry—trends technology leaders need to follow. But what are “trends”? All too often, trends degenerate into horse races over languages and platforms.

The Quality of Auto-Generated Code

O'Reilly on Data

Kevlin Henney and I were riffing on some ideas about GitHub Copilot , the tool for automatically generating code base on GPT-3’s language model, trained on the body of code that’s in GitHub.

AI Powered Misinformation and Manipulation at Scale #GPT-3

O'Reilly on Data

OpenAI’s text generating system GPT-3 has captured mainstream attention. GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet.

Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science

O'Reilly on Data

So you need to redesign your company’s data infrastructure. Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? Do you engage many small startups, each focused on one part of the problem? A little of both? We see trends shifting towards focused best-of-breed platforms.

The Top 5 Business Outcomes Companies Can Achieve From Monitoring Consolidation

In this eBook, learn what the top five business outcomes are that organizations see when leveraging Datadog's end-to-end monitoring tool.

Practical Skills for The AI Product Manager

O'Reilly on Data

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager.

What to Do When AI Fails

O'Reilly on Data

These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world.

Bringing an AI Product to Market

O'Reilly on Data

The Core Responsibilities of the AI Product Manager. Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones.

Intelligence and Comprehension

O'Reilly on Data

I haven’t written much about AI recently.

Communal Computing’s Many Problems

O'Reilly on Data

In the first article of this series, we discussed communal computing devices and the problems they create–or, more precisely, the problems that arise because we don’t really understand what “communal” means. Communal devices are intended to be used by groups of people in homes and offices.

5 Powerful Prescriptive Analytics Examples in Supply Chain

Prescriptive analytics is a type of advanced analytics that optimizes decision-making by providing a recommended action. Supply chain, with its complex planning questions, is typically an area where optimization technology is required. Read about 5 use cases.

InfoTribes, Reality Brokers

O'Reilly on Data

It seems harder than ever to agree with others on basic facts, let alone to develop shared values and goals: we even claim to live in a post-truth era.

What’s ahead for AI, VR, NFTs, and more?

O'Reilly on Data

Every year starts with a round of predictions for the new year, most of which end up being wrong. But why fight against tradition? Here are my predictions for 2022. The safest predictions are all around AI. We’ll see more “AI as a service” (AIaaS) products.

The unreasonable importance of data preparation

O'Reilly on Data

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them.

Pair Programming with AI

O'Reilly on Data

In a conversation with Kevlin Henney, we started talking about the kinds of user interfaces that might work for AI-assisted programming. This is a significant problem: neither of us were aware of any significant work on user interfaces that support collaboration.

Gain a Competitive Edge: The Importance of a Supplier Diversity Program

Speaker: Rod Robinson, SVP of the Supplier Diversity Practice, Insight Sourcing Group

In this exclusive webinar, Rod Robinson, SVP of the Supplier Diversity Practice Lead & Center of Excellence at Insight Sourcing Group, dives into the key benefits corporations are seeing emerge from their supplier diversity programs, and how you can gain unique competitive advantages with a supplier diversity program of your own.

How to Set AI Goals

O'Reilly on Data

AI Benefits and Stakeholders. AI is a field where value, in the form of outcomes and their resulting benefits, is created by machines exhibiting the ability to learn and “understand,” and to use the knowledge learned to carry out tasks or achieve goals.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

Algorithms tell stories about who people are. The first story an algorithm told about me was that my life was in danger. It was 7:53 pm on a clear Monday evening in September of 1981, at the Columbia Hospital for Women in Washington DC. I was exactly one minute old.

5 things on our data and AI radar for 2021

O'Reilly on Data

Here are some of the most significant themes we see as we look toward 2021. Some of these are emerging topics and others are developments on existing concepts, but all of them will inform our thinking in the coming year. MLOps FTW.

Our Favorite Questions

O'Reilly on Data

“ On peut interroger n’importe qui, dans n’importe quel état; ce sont rarement les réponses qui apportent la vérité, mais l’enchaînement des questions. “ “ You can interrogate anyone, no matter what their state of being.

Getting Started With Scenario Modeling in Supply Chain Network Design

To build your supply chain’s agility and responsiveness, you need to look at scenarios more frequently instead of relying on a single plan. Let’s explore how you can apply scenario modeling in supply chain network design.

6 trends framing the state of AI and ML

O'Reilly on Data

O’Reilly online learning is a trove of information about the trends, topics, and issues tech leaders need to know about to do their jobs.

Reinforcement learning for the real world

O'Reilly on Data

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL).

What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML).

The road to Software 2.0

O'Reilly on Data

Roughly a year ago, we wrote “ What machine learning means for software development.” In that article, we talked about Andrej Karpathy’s concept of Software 2.0. Karpathy argues that we’re at the beginning of a profound change in the way software is developed.

Modernizing Workloads with the Cloud: How to Improve Performance & Reduce Costs

In this eBook, you’ll learn how to migrate workloads to Azure and optimize performance for your serverless and containerized applications in Azure.

The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. Those suspicions were confirmed when we quickly received more than 1,900 responses to our mid-November survey request.

Product Management for AI

O'Reilly on Data

A couple of years ago, Pete Skomoroch, Roger Magoulas, and I talked about the problems of being a product manager for an AI product. We decided that would be a good topic for an article, and possibly more.

5 key areas for tech leaders to watch in 2020

O'Reilly on Data

O’Reilly online learning contains information about the trends, topics, and issues tech leaders need to watch and explore. It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1].

AI adoption in the enterprise 2020

O'Reilly on Data

Last year, when we felt interest in artificial intelligence (AI) was approaching a fever pitch, we created a survey to ask about AI adoption.

Secure Your Supply Chain: How to Build Resiliency for the Future

Speaker: Bart Huthwaite, Principal, RSM, Operations and Supply Chain

In this webinar, Bart Huthwaite will explore best practices in building and improving supply chain resilience for a successful and productive future.

COVID-19 and Complex Systems

O'Reilly on Data

In various mailing lists about the COVID-19 pandemic, I’ve seen several discussions of “complex systems theory” as, possibly, a way to understand how the pandemic is playing out in different locations.

AI Product Management After Deployment

O'Reilly on Data

The field of AI product management continues to gain momentum. As the AI product management role advances in maturity, more and more information and advice has become available.

Communal Computing

O'Reilly on Data

Home assistants and smart displays are being sold in record numbers, but they are built wrong. They are designed with one person in mind: the owner. These technologies need to fit into the communal spaces where they are placed, like homes and offices. If they don’t fit, they will be unplugged and put away due to lack of trust. The problems are subtle at first. Your Spotify playlist starts to have recommendations for songs you don’t like.

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Why a data scientist is not a data engineer

O'Reilly on Data

Or, why science and engineering are still different disciplines. "A A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.". Gordon Lindsay Glegg, The Design of Design (1969). A few months ago, I wrote about the differences between data engineers and data scientists. I talked about their skills and common starting points.

Best Practices to Model Carbon Costs in Supply Chain Network Design

How to model carbon costs in your supply chain design? Make conscious choices by comparing scenarios indicating at which points in the supply chain carbon emissions occur and achieve the right balance between sustainability and cost.