Do you collect facts or nurture knowledge?
From SpaceX & Tesla, to Neuralink, OpenAI and The Boring Company, Elon Musk has demonstrated the ability to optimise the learning process, and it all comes down to two simple rules.
So I put together a quick guide so you can learn to optimise for success as well.
From the Terminator to Spiderman’s suit, self-repairing robots and devices abound in sci-fi movies. In reality, though, wear and tear reduce the effectiveness of electronic devices until they need to be replaced. What is the cracked screen of your mobile phone healing itself overnight, or the solar panels providing energy to satellites continually repairing the damage caused by micro-meteorites?
HB1 is an automated wall-climbing robot that was designed to streamline home construction projects.
No matter the size, location, style, or chosen building material–when it comes to constructing houses, it can be a dangerous job. Even with bulky construction vehicles, building homes requires a lot of finesse and attention. As our technological worlds evolve, so do our tools and that includes those used for home construction. Home-building robotics company Hausbots developed an automated, climbing construction robot called HB1 to help get home projects done.
The research study of Spanish clinical neuropsychologist Gabriel G. De la Torre, Does artificial intelligence dream of non-terrestrial techno-signatures?, suggests that one of the “potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general.”
“Our form of life and intelligence,” observed Silvano P. Colombano at NASA’s Ames Research Center who was not involved in the study, “may just be a tiny first step in a continuing evolution that may well produce forms of intelligence that are far superior to ours and no longer based on carbon ” machinery.”
Automating repetitive tasks with loops and functions.
Many R users get into R programming from a statistics background rather than a programming/software engineering background, having previously used software such as SPSS, Excel etc. As such they may not have an understanding of some of the programming techniques that can be leveraged to improve code. This can include making the code more modular which in turn makes it easier to find and resolve bugs, but also can be used to automate repetitive tasks, such as producing tables and plots etc.
This short post in c ludes some of the basic programming techniques that can be used to improve the quality and maintainability of R scripts. This will also save you a whole lot of time if you are carrying out repetitive tasks that are only marginally different. We assume that you have a basic understanding of writing simple scripts in R.
Let’s start with a simple example. Let’s say we have some data from several different groups. In this case 3 animals (tigers, swans and badgers) and we have collected some data on relating to this (a score and value of some kind).
Militaries around the world are making fast advances in AI weaponry. Learn why many see this as a threat to human values.
Let there be darkness.
That is the potential catchphrase for those that are concerned about nighttime light pollution.
More formerly known as Artificial Light At Night (ALAN), there is an ongoing bruhaha that our modern way of living is generating way too much light during the evening darkness. It is an ongoing issue and the amount of such pollution is likely to keep on increasing due to further industrialization and expansion of societies into additional geographical areas.
In short, you can expect more light to be emitted in existing populated areas, along with nighttime light being unleashed in regions that had so far not been especially well lit due to insufficient means or lack of a light-producing populace. When you start adding more office buildings, more homes, more cars, more lampposts, and the like, this all translates into a tsunami of unbridled light at night.
You might be puzzled as to why the mere shepherding of artificial light is considered a pollution monstrosity.
One obvious facet is that you cannot see the stars at nighttime, or they are otherwise generally blotted out of view by the abundance of overwhelming artificial light.
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Tang Jie, the Tsinghua University professor leading the Wu Dao project, said in a recent interview that the group built an even bigger, 100 trillion-parameter model in June, though it has not trained it to “convergence,” the point at which the model stops improving. “We just wanted to prove that we have the ability to do that,” Tang said.
Ironically, China is a competitor that the United States abetted. It’s well known that the U.S. consumer market fed China’s export engine, itself outfitted with U.S. machines, and led to the fastest-growing economy in the world since the 1980s. What’s less well-known is how a handful of technology companies transferred the know-how and trained the experts now giving the United States a run for its money in AI.
Blame Bill Gates, for one. In 1992, Gates led Microsoft into China’s fledgling software market. Six years later, he established Microsoft Research Asia, the company’s largest basic and applied computer-research institute outside the United States. People from that organization have gone on to found or lead many of China’s top technology institutions.
China is a competitor that the United States abetted. A handful of U.S. tech companies transferred their know-how and trained some of China’s top AI experts.
Top futurologist Marian Salzman tells us her predictions for 2022 with robots stealing our jobs, political upheaval, hybrid schooling, angst and electronic pets.
What does 2022 have in store for AI in the enterprise? Will it be a robust year of world-altering developments and implementation, or will organizations struggle to gain appreciable value from an exceedingly complex technology?
In all likelihood, it will be a little of both. So as you chart a strategy for the coming year, keep an eye on what is really happening with AI right now and what remains on the drawing board.
If we look at Gartner’s AI Hype Cycle for 2021, it’s clear that the company has placed the majority of AI developments on the up-slope of the Innovation Trigger curve and at the Peak of Inflated Expectations. This includes everything from AI-driven automation and orchestration platforms to neural networks, deep learning, and machine learning. This isn’t to say that these applications are destined to crash and burn, just that they’re still more hype than reality at the moment – and Gartner expects it will be two to five years before they become productive assets in the enterprise.
