What do we mean by life? The question has been asked a million times before, but with the development of artificial intelligence (AI) and potent computers, it is getting harder and harder to answer. Swedish-American physicist, cosmologist, and machine learning researcher Max Tegmark tackles this massive question in his book Life 3.0: Being Human in the Age of Artificial Intelligence. A professor at MIT and the president of the Future of Life Institute, Tegmark demonstrates that the question "How do we define life?" is actually wrong on its face since there is more than one type of life. He embarks on a journey to explain three stages of life that make up the universe: Life 1.0, Life 2.0, and Life 3.0.

Roboticists have designed all sorts of jumping robots over the years, and many of them have been inspired by biology. But, as diverse as the natural world is, evolution hasn't cracked every option.

With artificial intelligence becoming more advanced every year, a number of high-ranking experts have begun to sound the alarm. The Onion asked several CEOs what they most feared about AI, and this is what they said.

The past couple of years has seen an unprecedented surge in the adoption of robots by a variety of sectors, including major employers like manufacturers and warehouse operators such as Amazon. The trend was already ramping up when the pandemic hit, but labor shortfalls and supply chain pressures sent it into overdrive, and there's no going back. What does that mean for humans? And given the longstanding fears of the impact of robots on employees, why hasn't a bigger deal been made of this? Veo, which provides sensing and intelligence to four of the biggest robot companies in the world (FANUC, Yaskawa, ABB, and Kuka), came up with part of the answer when it surveyed 500 manufacturers across the US, UK, and Japan in Q2.

When Jared Bauman was asked to look at nine dog pictures and identify which ones were smiling as part of a captcha test to log in to a website a few weeks ago, he was stumped. "To be honest, I had a bit of a moment," the founder of a creative marketing agency in San Diego, California, says. Most of the dogs looked neither happy nor sad--some were grimacing, or simply had their mouths open. No one is sure whether dogs can actually smile, meaning that correctly identifying smiling dogs in a captcha is a near-impossible task. This kind of conundrum is becoming a bigger issue as captchas--tests designed to weed out robot web surfers from humans on websites--have grown increasingly cryptic.

For decades, engineers seeking to build tunnels underground have relied on huge tube-like machines armed with a frightening array of cutting wheels at one end--blades that eat dirt for breakfast. These behemoths, called tunnel-boring machines, or TBMs, are expensive and often custom-built for each project, as were the TBMs used to excavate a path for London's recently opened Elizabeth Line railway. The machines deployed on that project weighed over 1,000 tons each and cut tunnels over 7 meters in diameter beneath the UK capital. But British startup hyperTunnel has other ideas. The firm proposes a future in which much smaller, roughly 3-meter-long robots shaped like half-cylinders zoom about underground via predrilled pipes.

TL;DR: As of August 11, you can get the Advanced Python Masterclass and Automation Training Bundle(opens in a new tab) when you pay what you want (see below for details) instead of its retail value of $2800. Not every coding language is equal. That's not to say some are outright better than others, but some do have more diverse applications. Python, for example, can be used to build desktop apps or as an automation tool. If you want to start learning advanced skills with Python, Java, Django, OOP, and more, then you may want to try out the Advanced Python Masterclass & Automation Training Bundle.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Feature stores began in the world of Big Data, with Spark being the feature engineering platform for Michelangelo (the first feature store) and Hopsworks (the first open-source feature store). Nowadays, the modern data stack has assumed the role of Spark for feature stores - feature engineering code can be written that seamlessly scales to large data volumes in Snowflake, BigQuery, or Redshift. However, Python developers know that feature engineering is so much more than the aggregations and data validation you can do in SQL and DBT. Dimensionality reduction, whether using PCA or Embeddings, and transformations are fundamental steps in feature engineering that are not available in SQL, even with UDFs (user-defined functions), today. Over the last few years, we have had an increasing number of customers who prefer working with Python for feature engineering.

Jon Crowcroft has been the Marconi Professor of Communications Systems in the Computer Laboratory since October 2001. He has worked in the area of Internet support for multimedia communications for over 30 years. Three main topics of interest have been scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. Current active research areas are Opportunistic Communications, Social Networks, Privacy Preserving Analytics, and techniques and algorithms to scale infrastructure-free mobile systems. He leans towards a "build and learn" paradigm for research.