• I’ve taken many his AI courses in deeplearning.ai and was a mentor in one of his courses in Coursera. His courses really strengthen me a lot about the deep learning knowledge.
• This time, I would like to share an article that I’ve read recently, from IEEE Spectrum Magazine in April 2022, namely “Andrew Ng, AI Minimalist: The Machine-Learning Pioneer Says Small is the New Big”
• IEEE Spectrum Magazine is a monthly magazine, which talks about technology of all kinds. It has impact factor of 3.578.
• In this article, Andrew Ng has shared a lot of his valuable visions and broad views about AI, e.g.: NLP, CV, semiconductor manufacturers, and his company, even from his first NeurIPS workshop paper, to recent NeurIPS data centric AI workshop! Read More


#strategy

If you’re on twitter these days you have likely seen a wave of videos that utilize things like Stable Diffusion for fun and novel product concepts/demos. This is happening now that designers have had the requisite time to mess around in DreamStudio or Dall-E, talk to engineers to understand what is possible, and spin up concept art.

This reminds me a lot of prior concept-heavy phases of AR and VR including the launch of Google Glass (we all remember this video), Magic Leap, Spectacles, and more. These concepts were fun the first time you watched them and lost luster over the subsequent viewings as we all litigated how much we really would use a given use-case (all while ignoring the UX of using the actual hardware).

Unfortunately, the steady flow of twitter retweets and likes led the AR/VR community to get too focused on fun use-cases versus high-value utility that people repeatedly wanted.¹

Now that we have composability of AI models, there is a new wave of AI-first products/features making their way into the world. These effective demos will likely lead to a barrage of AI-first products that see high short-term usage followed by material churn, driven by the collective curiosity of a variety of industries. Read More

#strategy

We’ve been long working on diverse set of ML projects, and we see the same decisions taken and same mistakes made again and again. But ML is commoditizing, and there is no way to escape it.

… As the industry gains more experience within the area, a couple of common open-source tools are created. They might implement only 90% of SaaS/in-house solutions feature set, but it’s more than enough for 90% of the industry. Welcome to the commoditized world, where you can do ML without writing Python code. Read More

The year is 2052. The world has averted the climate crisis thanks to finally adopting nuclear power for the majority of power generation. Conventional wisdom is now that nuclear power plants are a problem of complexity; Three Mile Island is now a punchline rather than a disaster. Fears around nuclear waste and plant blowups have been alleviated primarily through better software automation. What we didn’t know is that the software for all nuclear power plants, made by a few different vendors around the world, all share the same bias. After two decades of flawless operation, several unrelated plants all fail in the same year. The council of nuclear power CEOs has realized that everyone who knows how to operate Class IV nuclear power plants is either dead or retired. We now have to choose between modernity and unacceptable risk. Read More

#strategy

A brand-new area of computer science was initially referred to as “artificial intelligence” in 1955. Many daily jobs are being replaced by artificial intelligence, requiring less human involvement. But what precisely is this new AI technology? AI refers to the process of teaching a computer system to function and think like a human brain. This is often accomplished through reinforcement learning, in which the computer learns from past errors and observed patterns. For instance, a trained model defeated the world champion in the difficult-to-learn and won a game of AlphaGO by self-training itself many times and learning from its mistakes each time it lost a game.

Applications of AI technology are rapidly gaining ground in every aspect of our daily life, just as AI is quickly transforming several sectors. People may easily carry out tasks like phoning a buddy, determining the best route to their destinations, and turning on or off electrical equipment by giving voice instructions to their virtual assistant. AI technology is also finding applications in the automotive, e-commerce, AI farming, healthcare, and several other industries.

It won’t be long before artificial intelligence (AI) technology has advanced to the point where people rely on their virtual assistants to wake us up, driverless cars to get us to work, and perhaps AI-powered robots to assist us in making decisions at work by forecasting, analyzing, and providing valuable insights. It all seems somewhat fictional when we read this, don’t you think? Thoughts of video calling were commonplace, but today everyone has access to it through their cellphones. Read More

Jeremy Howard is and artificial intelligence researcher and the co-founder of fast.ai, a platform for non-experts to learn artificial intelligence and machine learning. Prior to starting fast.ai, he founded multiple companies — including FastMail and Enlitic, a pioneer in applying deep learning to the medical field — and was president and chief scientist of machine-learning competition platform Kaggle.

In this interview, Howard discusses what it means for different industries and even global regions now that people without PhDs from specialized research labs can build and work with deep learning models. Among other topics under this broad umbrella, he shares his thoughts on how to best keep up with state-of-the-art techniques, prompt engineering as a new skill set, and the pros and cons of code-generation systems like Codex. Read More

Marc Andreessen arrived in Silicon Valley 28 years ago, fresh from the University of Illinois, where he and a colleague developed NCSA Mosaic, the graphic web browser that opened the world’s eyes to the potential of the internet. As an entrepreneur, Andreessen launched Netscape, whose IPO was the bellwether event of the first internet boom, and Opsware, an early cloud and software-as-a-service (SaaS) company. He then cofounded Andreessen Horowitz with Ben Horowitz, building it into one of the world’s premiere venture capital firms.

Andreessen’s experience gives him a unique perspective on how new technologies develop, disrupt, and create opportunities for business. It’s a perspective that is of particular interest at a time like this, when so much is unclear about the future of technology. Andreessen recently joined McKinsey senior partner Tracy Francis and the Quarterly editorial director Rick Tetzeli for a wide-ranging discussion. An edited version of the conversation follows. Read More