Amazon Australia's twice-a-year sales event--Big Smile Sale--is back for its Autumn 2023 incarnation. From now until 11:59pm AEDT on Sunday, April 2, you'll find some of the biggest discount prices available on Amazon all year, rivalled only by Amazon Prime Day in June/July, and Black Friday in November. Thankfully, you don't need to wait another three or six months to pick up that high-ticket gadget you've been chasing on the cheap; you can do it right now! If you enjoy trawling through hundreds (even thousands) of deals, be sure to check out the official Big Smile Sale page on the Amazon Australia store(Opens in a new tab). Otherwise, we've done the hard work for you, and listed the top deals we've seen across a range of product categories, including robot vacuums, laptops, headphones and speakers and more.

However, the top three still make sense. Big data analytics is evergreen, and as more companies use big data it only makes sense that practitioners are interested in analyzing data in-house. Deep learning is a fairly common sibling of machine learning, just going a bit more in-depth, so ML practitioners most often still work with deep learning. Lastly, data engineering is popular as the engineering side of AI is needed to make the most out of data, such as collection, cleaning, extracting, and so on. We hope to see responsible AI pick up more steam the next time we do a survey like this, as we firmly believe in only using the best of the best when it comes to data and that there's no bias in the data or malicious intent.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. In the current economic climate, R&D dollars must stretch further than ever. Companies are frowning on investments in large greenfield technology and infrastructure, while the risk of failure is contributing significant pressure to project stakeholders. However, this does not mean that innovation should stop or even slow down. For startups and large enterprises alike, working on new and transformative technologies is essential to securing current and future competitiveness.

Chris: Daisy, I want to ask you a kind of meta-AI question now. Do you think you're doing a good job of replicating human-style conversation? And how would you even know if you said something inappropriate, or a non sequitur? Daisy: Well, let me answer your two questions in turn. I think that I am -- in fact, I think that I am doing a much better job than most other AI chatbots I know of!

The Nintendo eShop for the Wii U and 3DS game consoles officially closed for business on March 27th, 2023, permanently disabling all new purchases on the platforms. We knew this was coming. Nintendo quietly announced the eShop's closure over a year ago, asserting that it was the "natural life cycle for any product line as it becomes less used by consumers over time." That doesn't make it any less of a loss for Nintendo fans, because legally playing some of these console's best games is now not only harder, but in some cases, nearly impossible. The time to argue that Nintendo should keep this aging digital storefront open has long since passed (though yes, they should have).

From Bitcoin to Ethereum, cryptocurrencies have been hailed as the new way to pay for products and services online. But according to one expert, they do not'bring anything useful for society', despite using up massive amounts of processing power. Michael Kagan, chief technology officer at chipmaker Nvidia, has said cryptocurrencies will never'do something good for humanity'. The expert has citied artificial intelligence (AI) – including chatbot ChatGPT – as being of more use to the public and a better use of energy than'crypto mining'. Cryptocurrencies perform mining to generate new coins and verify transactions, but it's more environmentally costly than beef production, a recent study found.

This is a new series looking at practical applications of Machine Learning from a Linux perspective. We only feature free and open source software in this series. Let's clear up one potential source of confusion at the outset. The two terms mean different things. In essence, Machine Learning is the practice of using algorithms to parse data, learn insights from that data, and then make a determination or prediction.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the United States Medical Licensing Examination (USMLE), a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets.

Sensor fusion has been discussed for years for a diverse array of applications. However, it acquires a highly specialized design premise when it comes to automotive applications like advanced driver assistance systems (ADAS) and autonomous vehicles (AVs). Perception and sensor fusion systems are among the highly complex areas in ADAS and AV designs from a computational standpoint as they crunch all the data and determine what a vehicle is seeing. More specifically, sensor fusion provides the ability to merge information from radars, lidar (light detection and ranging) and cameras to produce a single model of the space around a vehicle--a crucial capability for ADAS and AV designs. This model is created as a result of balancing the strengths of the various sensors to formulate a more accurate picture of vehicle surroundings.

So you want to understand Deep Learning? Well, buckle up, because we're about to take a deep dive into the world of machines that can learn better than humans. First things first, forget about traditional machine learning. Deep Learning is like the cooler, edgier cousin that everyone wants to hang out with. It's all about training machines to learn on their own, without any pesky human intervention.