Key Trends in Data Lakes

Data lakes have become a key tool for mining competitive insight from large repositories of data.

The term data lake has been with us for many years. It’s origin is attributed to James Dixon who coined the term while writing, “If you think of a data mart as a store of bottled water – cleansed, packaged, and structured for easy consumption – the data lake is a large body of water in a more natural state.”

Many a subsequent writer has questioned whether organizations were creating data lakes with business value or data swamps with limited or no value. Given this, Marco Iansiti and Karim Lakhani have suggested that the data lake, data in it is original source, is part of a data platform with “data flowing from bottom to top…And the data platform aggregates, cleans, refines, and processes data” captured in the data lake.

Given this more refined view, the question is: where is the data lake within its hype cycle? To answer this question, I asked CIOs and industry experts for their opinions. Read More

#data-lake

Meet GPT-3. It Has Learned to Code (and Blog and Argue).

The latest natural-language system generates tweets, pens poetry, summarizes emails, answers trivia questions, translates languages and even writes its own computer programs.

This summer, an artificial intelligence lab in San Francisco called OpenAI unveiled a technology several months in the making. This new system, GPT-3, had spent those months learning the ins and outs of natural language by analyzing thousands of digital books, the length and breadth of Wikipedia, and nearly a trillion words posted to blogs, social media and the rest of the internet.

Mckay Wrigley, a 23-year-old computer programmer from Salt Lake City, was one of the few invited to tinker with the system, which uses everything it has learned from that vast sea of digital text to generate new language on its own. Mr. Wrigley wondered if it could imitate public figures — write like them, perhaps even chat like them. Read More

#nlp

Ethical AI isn’t the same as trustworthy AI, and that matters

Artificial intelligence (AI) solutions are facing increased scrutiny due to their aptitude for amplifying both good and bad decisions. More specifically, for their propensity to expose and heighten existing societal biases and inequalities. It is only right, then, that discussions of ethics are taking center stage as AI adoption increases.

In lockstep with ethics comes the topic of trust. Ethics are the guiding rules for the decisions we make and actions we take. These rules of conduct reflect our core beliefs about what is right and fair. Trust, on the other hand, reflects our belief that another person — or company — is reliable, has integrity and will behave in the manner we expect. Ethics and trust are discrete, but often mutually reinforcing, concepts.

So is an ethical AI solution inherently trustworthy? Read More

#ethics, #trust

Teachable Machine From Google Makes It Easy To Train And Deploy ML Models

Teachable Machine is an experiment from Google to bring a no-code and low-code approach to training AI models. Anyone with a modern browser and webcam can quickly train a model with no prior knowledge or experience with AI. Read More

#big7, #transfer-learning