Google would have created the first generalized artificial intelligence, “competing” with the human mind
rael-science
Google would have created the first generalized artificial intelligence, “competing” with the human mind
⇧ [VIDÉO] You might also like this partner content (after ad)
DeepMind, a company (belonging to Google) specialized in
artificial intelligence, has just presented its new artificial
intelligence named “Gato”. Unlike “classic” AIs, which specialize in a
specific task, Gato is able to perform more than 600 tasks, often much
better than humans. Controversy is launched as to whether this is
really the first “generalized artificial intelligence” (GAI). Experts
remain skeptical of DeepMind’s announcement.
Artificial intelligence has positively changed many disciplines.
Incredible specialized neural networks are now able to produce results
far beyond human capabilities in many areas.
One of the great challenges in the field of AI is the realization of a
system integrating generalized artificial intelligence (GAI), or strong
artificial intelligence. Such a system must be able to understand and
master any task of which a human being would be capable. She would
therefore be able to compete with human intelligence, and even develop a
certain degree of consciousness. Earlier this year, Google unveiled a
type of AI capable of coding like an average programmer. Recently, in
this race for AI, DeepMind announced the creation of Gato, an artificial
intelligence presented as the first AGI in the world. The results are
published in arXiv.
An unprecedented generalist agent model
A single AI system capable of solving many tasks is not something
new. For example, Google recently started using a system for its search
engine called the “unified multitasking model,” or MUM, that can handle
text, images, and video to perform tasks, from research to
cross-linguistic variations. in the spelling of a word, and the
association of search queries with relevant images.
Incidentally, Senior Vice President Prabhakar Raghavan provided an
impressive example of MUM in action, using the mock search query: I hiked Mount Adams and now want to hike Mount Fuji next fall, what should I do differently to prepare? “. MUM enabled Google Search to show the differences and similarities
between Mount Adams and Mount Fuji. He also brought up articles dealing
with the equipment needed to ascend the latter. Nothing too impressive
you would say, but concretely with Gato, what is innovative is the
diversity of the tasks that are approached and the method of training,
of a single and unique system.
Gato’s guiding design principle is to train on the widest variety of
relevant data possible, including various applications such as images,
text, proprioception, joint torques, button presses and others. discrete
and continuous observations and actions.
To enable processing of this multimodal data, scientists encode it
into a flat sequence of “tokens”. These tokens serve to represent data
in a way that Gato can understand, allowing the system, for example, to
figure out which combination of words in a sentence makes grammatical
sense. These sequences are grouped together and processed by a
transformative neural network, typically used in language processing.
The same network, with the same weights, is used for the different
tasks, unlike traditional neural networks. Indeed, in the latter, each
neuron is assigned a particular weight and therefore a different
importance. In simple terms, the weight determines what information
enters the network and calculates an output data.
In this representation, Gato can be trained and sampled from a
standard large-scale language model, on a large number of datasets
including agents’ experience in simulated and real environments, in
addition to a variety of natural language datasets and images. When
operating, Gato uses context to assemble these sampled tokens to
determine the form and content of its responses.
The results are quite heterogeneous. When it comes to dialog, Gato
falls far short of rivaling the prowess of GPT-3, Open AI’s text
generation model. He can give wrong answers during conversations. For
example he answers that Marseille is the capital of France. The authors
point out that this could probably be improved with further scaling.
Nevertheless, he still proved to be extremely capable in other areas.
Its designers claim that, half the time, Gato performs better than
human experts in 450 of the 604 tasks listed in the research paper.
Examples of the tasks performed by Gato, as sequences of tokens. © S. Reed et al., 2022. ” The Game is Over “, Actually ?
Some AI researchers see the AGI as an existential catastrophe for
humans: a “super intelligent” system that surpasses human intelligence
would replace humanity on Earth, under the worst-case scenario. Other
experts believe that it will not be possible in our lifetime to see the
emergence of these AGIs. This is the pessimistic opinion that Tristan
Greene argued in his editorial on the site TheNextWeb. He
explains that it’s easy to mistake Gato for a real IAG. The difference,
however, is that a general intelligence could learn to do new things
without prior training.
The response to this article was not long in coming. On TwitterNando
de Freitas, researcher at DeepMind and professor of machine learning at
the University of Oxford, said the game was over (“ The Game is Over ”) in the long quest for generalized artificial intelligence. He adds : ” It’s
about making these models bigger, safer, more computationally
efficient, faster to sample, with smarter memory, more modalities,
innovative data, online/offline… It is by solving these challenges that we will obtain the IAG “.
Nevertheless, the authors warn against the development of these AGIs: “ Although
generalist agents are still an emerging field of research, their
potential impact on society calls for a thorough interdisciplinary
analysis of their risks and benefits. […] Generalist agent harm
mitigation tools are relatively underdeveloped and require further
research before these agents are deployed “.
Moreover, generalist agents, capable of performing actions in the
physical world, pose new challenges requiring new mitigation strategies.
For example, physical embodiment could lead users to anthropomorphize
the agent, leading to misplaced trust in the case of a faulty system.
In addition to these risks of seeing the AGI tip into a harmful
operation for humanity, no data currently demonstrates the ability to
produce solid results in a consistent manner. This is particularly due
to the fact that human problems are often difficult, not always having a
single solution, and for which no prior training is possible.
Tristant Greene, despite Nando de Fraitas’ response, maintains his opinion just as harshly, on TheNextWeb : “ It’s
nothing short of miraculous to see a machine pull off feats of
diversion and conjuring a la Copperfield, especially when you realize
that said machine is no smarter than a toaster (and obviously dumber
than the dumbest mouse) “.
Whether or not we agree with these statements, or whether we are more
optimistic about the development of AGIs, it nevertheless seems that
the scaling up of such intelligences, competing with our human minds, is
still far from complete. be completed, and controversies appeased.