Meta, the recently adopted company name of what was once known as Facebook,
has unveiled their "Next-gen A.I. Supercomputer" that will be censoring
the broadly-defined "harmful content" like never before in anticipation of the metaverse.
Hello and welcome back to A.I. science! In today's episode, we will be talking about
the terrifying prospects of Meta owning this latest Supercomputer!
Make sure to subscribe if you are new to our channel. So without any further delay,
let's begin!
The latest high-end machine will be a massive GPU-driven supercomputer
to run its artificial intelligence and machine learning-driven workloads that will
be the key to the metaverse development. Meta is constructing a new supercomputer to train massive
machine learning algorithms. Despite being just half completed, the artificial
intelligence research supercluster already ranks among the world's most powerful machines.
When completed, the corporation formerly known as Facebook claims it will be the fastest A.I.
supercomputer on the planet. Meta thinks that by training computers to detect hazardous material,
RSC will be able to enhance their goods, but analysts and whistleblowers
are uncovering facebook's true intentions behind reaching A.I. supremacy ahead of its competitor's
further advancements according to the business might enable real-time language translation
between tens of thousands of people online as well as multitasking algorithms that can
learn from and generalize across diverse inputs such as text photos and video.
All of this will assist real-world progress applications such as robots
and provide the groundwork for the as-yet primeval metaverse.
According to the press release, when explaining why Meta needs such a powerful A.I. supercomputer,
the trope of a "critical" use case was that of "harmful content," according to the press release.
"To fully realize the benefits of advanced A.I., various domains,
whether vision, speech, language, will require training increasingly large and complex models,
especially for critical use cases like identifying harmful content. In early 2020,
we decided the best way to accelerate progress was to design a new computing infrastructure — RSC."
In early December, Meta released a blog post about how "Harmful content
can evolve quickly" and how the benefits of enhanced A.I. can learn the nuances
of what Meta would consider harmful content that their traditional A.I. might overlook.
Meta is determined to "keep people safe" on their platforms, especially the forthcoming metaverse,
boasting of how this new Supercomputer will be
able to determine whether anything done on their platform is "harmful or benign."
Whether RSC will truly be the fastest such machine in the world or not,
it's impossible to deny that it's equipped with mind-boggling power and potential.
According to Meta, the company set out in 2020 with the goal of creating a research
computer capable of learning and training models with more than a trillion parameters.
RSC is said to one day be able to work on data sets as large as an exabyte. Most of us have
never even heard of exabytes, and no wonder — one exabyte adds up to one billion gigabytes of data,
which Meta says is the equivalent of 36,000 years of high-quality video. The best SSDs,
with 2 T.B. of storage, suddenly seem tinier than a grain of sand.
The Meta and Nvidia supercomputer is powered by 760 Nvidia DGX A100 systems which house
6,080 Nvidia A100 graphics cards. According to Nvidia, this supercluster of GPUs is
capable of delivering 1,895 petaflops of TensorFloat-32 (TF32) performance.
The computer is not quite ready yet, but it's getting there. Nvidia has
announced that during the course of this year, almost 10,000 GPUs will be added,
for a total of 16,000, which may result in up to 5 exaflops of A.I. performance.
RSC sounds like a true monster of a computer, but what does Meta really need it for?
All of this, according to the business will assist progress in real-world applications such as robots
and of the course provides the groundwork for the as yet primeval metaverse in the metaverse,
it's always a 3d multi-sensorial experience, and you have to construct artificial intelligence
agents in that environment that are meaningful to you meta's V.P. of artificial intelligence told
the wall street journal whatever the final use the investment demonstrates that the
industry's major players from meta to alphabet and Microsoft see cutting edge A.I. as more important
the announcement is part of a trend toward ever-larger machine learning algorithms that
need more processing power and larger data sets in 2020. OpenAI's natural language algorithm GPT-3
demonstrated that by increasing the number of internal connections and algorithms known
as parameters and the quantity of training data pumped through them, significant increases might
be obtained GPT3 was 17 times larger than its predecessor GPT-2 with 175 billion parameters.
Encouraged by the success of GPT-3, Microsoft introduced Megatron A.I. last year,
a three times larger algorithm than GPT-3, while Google and Chinese researchers have
developed algorithms with over a trillion parameters in preparation for the next stage.
Meta stated that they want to employ RSC to train algorithms with billions of parameters;
these enormous algorithms are increasingly necessitating the employment of supercomputers,
the room-sized machines that scientists use to model physical systems
ranging from fundamental particles to earth's climate to the whole cosmos.
Keeping this in mind, it is becoming increasingly vital for human professionals working with
advanced ai systems, for example, in the military or policy-making teams, to create a suitable
conceptual model of the A.I. system's cognitive skills concerning human cognition. This issue
will become increasingly important as A.I. systems improve and are deployed with greater autonomy.
As a result, this study aims to give more clarity and understanding of the essential qualities,
distinctions and peculiarities of human biological and artificial digital bits of intelligence.
The third portion introduces a worldwide framework for developing instructional content on
intelligence awareness. This may be used to create education and training programs for humans who
will be required to utilize or collaborate with powerful ai systems in the near and far future.
The new system, according to the businesses, was among the top five fastest supercomputers
in the world at the time. Though meta does not provide figures on RSC's current peak speed
looks to be equivalent in raw processing power to the Perlmutter supercomputer, which is ranked
sixth-fastest in the world RSC is now running on 6800 Nvidia a100 graphics processing units,
a specialized processor that was originally dedicated to gaming, but is now utilized more
extensively particularly in A.I. already. The machine is 20 times better at processing computer
vision operations and three times faster at processing complex language models such as GPT-3.
The faster a corporation can train models, the more it can finish and enhance in a given year.
RSC will provide meta with the opportunity to train algorithms on its vast treasure of user data
in addition to pure speed, the business previously trained A.I. on public open-source data sets,
but RSC will utilize real-world user-generated data from meta's production servers. According
to a blog post, given the various privacy and security concerns, meta has encountered
in recent years, this fact may make some people cringe. The business made efforts
in the statement to emphasize that the data will be carefully anonymized and secured end to end.
They also stated that RSC would not have a direct link to the greater internet later this year. The
installation will be expanded to incorporate sixteen thousand GPUs in an exabyte of storage
equivalent to 36 000 years of high-quality video to accommodate meta's massive training data sets
and substantially boost training performance meta claims that once completed, RSC will
supply training data at a rate of 16 gigabytes per second and operate at a peak speed of 5 exa flops
if built today RSC would be the world's fastest AI Supercomputer. Still, it's worth delving into
what it entails. For a second, the construction of supercomputers varies both central processing
units CPUs and graphics processing units; GPUs are often used in combinations. However,
the chip manufacturers vary, as does the infrastructure that connects them.
They also created a much smaller 7 billion parameter model known as retro,
given the opportunity to reference an external library of instances to influence its predictions.
Retro outperformed algorithms 25 times its size by matching or outperforming them. Deep mind claims
that the algorithm's thinking is also easier to track, making it more transparent and perhaps
easier to eliminate bias; however, no matter how clever autonomous A.I. agents develop in certain
ways, they will most likely remain unconscious robots or special-purpose gadgets that assist
humans in complicated specialized jobs for the foreseeable future as digital devices they have
a fundamentally different operating system digital versus biological and as a result cognitive
capabilities and capacities than biological entities such as humans and other animals.
So while creating massive algorithms on supercomputers is impressive,
retro demonstrates that innovation in how those models are produced
is also vital both sides of the spectrum will very certainly continue to be researched with
the expectation that one will feed into and improve the other. So what do you think about
meta's new research project? do you think that brute-forcing actually intelligent
artificial intelligence models is the way to go or are they just wasting their money?
Tell us your opinions in the comment section. If you liked this video,
give us a thumbs up and don't forget to share and subscribe. See you around and take care!