back to index9 New Gemini Leaks, Code Llama and A Major AI Consciousness Paper
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Like buses, AI news can sometimes be slow and sometimes arrive all at once. 00:00:06.020 |
In the last few days we have had dramatic new leaked insights into the sheer breadth 00:00:11.680 |
of Google's Gemini. Just today we've had the release of Meta's Code Llama and earlier their 00:00:18.080 |
impressive multilingual seamless M4T model. And last but definitely not least, this 88 page 00:00:26.240 |
AI consciousness report. And yes, I read it all, it's juicy so I'm saving that for the end. 00:00:32.640 |
But let's start with two major paywalled articles, one from the Information and one from the New 00:00:38.620 |
York Times, about Google's Gemini model. From both of them I counted a total of 9 new revelations, 00:00:45.000 |
so let's get straight to it. To give you a sense of timeline by the way, Google's newly merged AI 00:00:50.360 |
SWOT team they call it, is preparing for a big fall or autumn launch. The takeaway 00:00:56.040 |
for me from both articles is that Gemini is going to be the everything model. Did you 00:01:01.360 |
know it's going to be the rival to Midjourney and Stable Diffusion? Midjourney only has 00:01:07.240 |
11 full time staff so it is more than plausible that Google's Gemini could outperform Midjourney 00:01:13.960 |
version 5. Next we may be able to create graphics with just text descriptions and control software 00:01:20.940 |
using only text or voice commands. These next two are speculation so I'm not even 00:01:25.840 |
counting them in the list of leaks. But I've already covered in a previous video that Gemini 00:01:30.660 |
has been trained on YouTube video transcripts. And the speculation is that by integrating video 00:01:36.780 |
and audio into Gemini, it could perhaps help a mechanic diagnose a problem with a car repair 00:01:42.880 |
based on a video. Or be a rival to Runway ML by generating advanced text to video based on 00:01:49.340 |
descriptions of what a user wants to see. You can start to see why I'm beginning to think of it as 00:01:55.640 |
Another leak is that one of the co-founders of Google, Sergey Brin is working on the front 00:02:00.520 |
lines of Google Gemini. And lastly from this article I found it really interesting that 00:02:05.860 |
Google's lawyers have been closely evaluating the training and they made researchers remove 00:02:11.640 |
training data that had come from textbooks. Even though those textbooks helped the model 00:02:17.040 |
answer questions about subjects like astronomy or biology. And I do wonder if they privately 00:02:22.360 |
benchmarked Gemini before removing that crucial data. 00:02:25.440 |
But if that's not enough, prepare to also receive life advice. My theory here is that Google wants to compete 00:02:33.000 |
directly for market share with Inflection's pie. What if you want scientific, creative or professional 00:02:39.780 |
writing? Yep, they're working on that too. In fact, we already know that Google has software 00:02:44.500 |
named Genesis that they're pitching to the New York Times, which can generate news articles, 00:02:49.460 |
rewrite them, suggest headlines, etc. But some people will be more interested in this feature that 00:02:55.240 |
Google DeepMind is working on. The ability to draft critiques of an argument and generate quizzes, 00:03:00.780 |
word and number puzzles. It's almost easier at this point to ask what might Google Gemini not 00:03:06.880 |
be able to do. And yes, this is not Gemini, but Google DeepMind is also using AI to design the 00:03:13.200 |
next generation of semiconductors. But if the fall seems far away, how about today when we got 00:03:19.260 |
Code Llama from Meta? I spent much of the last two hours reading most of the 47 page 00:03:25.040 |
paper and you can see Code Llama in action on screen. Some highlights include that the Code Llama 00:03:31.040 |
models provide stable generations with up to 100,000 tokens of context. Obviously, that could 00:03:37.020 |
be used for generating longer programs or providing the model with more context from your code base 00:03:42.240 |
to make the generations more relevant. It comes in three versions, Code Llama, Code Llama Instruct, 00:03:47.580 |
which can better understand natural language instructions, and Code Llama Python, 00:03:51.660 |
better, of course, at Python. It's available for commercial 00:03:54.840 |
use. And as you can see, some of the versions rival GPT 3.5 on human eval. That top score of 00:04:01.880 |
53.7% on Passat1 puts it in the same ballpark as Phi1. I've actually done a full video on Phi1, 00:04:09.340 |
so do check that out. But that got 50.6%. But it is about 25 times smaller at 1.3 billion parameters. 00:04:16.860 |
Interestingly, the Code Llama paper, which also came out about two hours ago, 00:04:20.980 |
mentions Phi1 directly, saying that it follows in a similar, 00:04:24.800 |
similar spirit, but the difference is that Phi1 is closed source. 00:04:28.680 |
Anyway, a couple more interesting things before we move on from Code Llama. And the first one 00:04:34.140 |
is the self-instruct method that they use. Let me know if you also find this fascinating, 00:04:39.080 |
because step one was to generate 62,000 interview-style programming questions by 00:04:45.020 |
prompting Llama2, the 70 billion parameter model. Then they removed duplicates in step two. 00:04:50.320 |
But here's where it gets interesting. For each of those questions, they first generated a unit test, 00:04:54.760 |
by prompting Code Llama 7 billion parameters. Then they generated 10 Python solutions by prompting 00:05:01.260 |
Code Llama. Finally, they ran unit tests on those 10 solutions, and they added the first solution 00:05:06.800 |
that passes those tests, along with the corresponding question and test, to the self-instruct 00:05:11.600 |
dataset. If that sounded a bit complicated, let me try to distill it a bit. They asked the big brother 00:05:16.900 |
Llama2 model to generate questions, then got the little brother Code Llama to generate tests for 00:05:23.680 |
those questions. Then they added the little brother Code Llama to generate tests for those questions. 00:05:24.720 |
Then got the model to generate solutions to its own tests, found the good solutions that don't 00:05:30.140 |
forget it produced, and then used those to further train the model. To be honest, synthetic data and 00:05:35.860 |
self-instruct seem to be the future of feedback. One final interesting quote from the paper on 00:05:42.360 |
safety, and that was an argument advanced by one of their red teamers. They made the point that 00:05:47.600 |
various scripts and code is readily available on mainstream public websites, hacking forums, or the 00:05:54.680 |
web. And the advanced malware development is beyond the current capabilities of available LLMs. And even 00:06:00.640 |
an advanced LLM paired with an expert malware developer is not particularly useful at the moment, 00:06:06.400 |
as the barrier is not typically writing the malware code itself. Let me know what you think in the 00:06:12.100 |
comments. But we must move on to Seamless M4T released a couple of days ago from Meta, which 00:06:18.980 |
frankly seems amazing for multilingual translation. That speech to text, speech to text, speech to text 00:06:24.640 |
to speech, text to text, and more. It has speech recognition for nearly 100 languages and can output in 36 00:06:32.640 |
languages. But there's one feature I find particularly cool. 00:06:35.840 |
Now, let's talk about code switching. Code switching happens when a multilingual speaker 00:06:43.340 |
switches between languages while they're speaking. Our model Seamless M4T automatically 00:06:48.840 |
recognizes and translates more than one language when mixed in the same sentence. As a multilingual 00:06:54.600 |
speaker, this is a very exciting capability for me. I often switch from Hindi to Telugu when I speak 00:07:00.640 |
with my dad. Notice in the following example when I change languages. 00:07:05.100 |
I can speak Hindi, Telugu, and English. Sometimes, I can use all three languages in one sentence. 00:07:17.940 |
I can speak Hindi, Telugu, and English. Sometimes, I use English. Sometimes, I use English. Sometimes, I 00:07:27.860 |
Speaking of cool though, we had this epic story out yesterday. AI gave a paralyzed woman her voice back. 00:07:35.260 |
In a moment, you're going to see her being plugged in to the model. There we go. And the short version 00:07:41.900 |
is that this woman suffered a stroke that left her unable to speak. But now, for the first time, 00:07:48.260 |
her speech and facial expressions can be synthesized from her brain signals. Decoding these 00:07:54.520 |
signals into text at nearly 80 words per minute, up from 14 words per minute. 00:07:59.860 |
But let's now end on this, an 88-page report on consciousness in artificial intelligence, which counts as one of its 00:08:06.720 |
co-authors, Yoshua Bengio, the Turing Award winner. It was dense and quite technical, but well worth the read. 00:08:16.680 |
"Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to build 00:08:28.020 |
These are the indicators and each one gets a few pages in the report. And the reason that they're split up is because each one rests on a certain theory of consciousness. 00:08:39.020 |
Obviously, the key problem is that we don't have a consensus theory on what consciousness is or how it comes about. 00:08:46.640 |
So in a way, to hedge their bets, they group in different theories and look at the kind of indicators that would satisfy each one. 00:08:54.440 |
Let's say that list seems so theoretical, why not just test the model or even ask the model? 00:08:59.680 |
For more on that approach, see my theory of mind video. 00:09:02.940 |
But the problem is, as they say on page four, the main alternative to a theory heavy approach is to use behavioral tests for consciousness. 00:09:12.440 |
But as I talked about in the other video, that method is unreliable because AI systems can be trained, of course they are, to mimic human behaviors, 00:09:23.340 |
Essentially, LLMs are the most efficient way to manipulate human behavior. 00:09:24.400 |
They can be used to manipulate the behavior of other people. 00:09:25.940 |
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They can be used to manipulate other people's behavior. 00:09:43.240 |
They can be used to manipulate other people's behavior. 00:09:45.240 |
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They can be used to manipulate other people's behavior. 00:10:56.280 |
They can be used to manipulate other people's behavior. 00:10:58.280 |
They can be used to manipulate other people's behavior. 00:11:00.280 |
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They can be used to manipulate other people's behavior. 00:11:06.280 |
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They can be used to manipulate other people's behavior. 00:11:10.280 |
They can be used to manipulate other people's behavior. 00:11:12.280 |
They can be used to manipulate other people's behavior. 00:11:14.280 |
They can be used to manipulate other people's behavior. 00:11:16.280 |
They can be used to manipulate other people's behavior. 00:11:18.280 |
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They can be used to manipulate other people's behavior. 00:11:28.240 |
They can be used to manipulate other people's behavior. 00:11:30.240 |
They can be used to manipulate other people's behavior. 00:11:32.240 |
They can be used to manipulate other people's behavior. 00:11:34.240 |
They can be used to manipulate other people's behavior. 00:11:36.240 |
They can be used to manipulate other people's behavior. 00:11:38.240 |
They can be used to manipulate other people's behavior. 00:11:40.240 |
They can be used to manipulate other people's behavior. 00:11:42.240 |
They can be used to manipulate other people's behavior. 00:11:44.240 |
They can be used to manipulate other people's behavior. 00:11:46.240 |
They can be used to manipulate other people's behavior. 00:11:48.240 |
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They can be used to manipulate other people's behavior. 00:11:52.240 |
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They can be used to manipulate other people's behavior. 00:11:58.200 |
They can be used to manipulate other people's behavior. 00:12:00.200 |
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They can be used to manipulate other people's behavior. 00:12:04.200 |
They can be used to manipulate other people's behavior. 00:12:06.200 |
They can be used to manipulate other people's behavior. 00:12:08.200 |
They can be used to manipulate other people's behavior. 00:12:10.200 |
They can be used to manipulate other people's behavior. 00:12:12.200 |
They can be used to manipulate other people's behavior. 00:12:14.200 |
They can be used to manipulate other people's behavior. 00:12:16.200 |
They can be used to manipulate other people's behavior. 00:12:18.200 |
They can be used to manipulate other people's behavior. 00:12:20.200 |
They can be used to manipulate other people's behavior. 00:12:22.200 |
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They can be used to manipulate other people's behavior. 00:12:26.160 |
They can be used to manipulate other people's behavior. 00:12:28.160 |
They can be used to manipulate other people's behavior. 00:12:30.160 |
They can be used to manipulate other people's behavior. 00:12:32.160 |
They can be used to manipulate other people's behavior. 00:12:34.160 |
They can be used to manipulate other people's behavior. 00:12:36.160 |
They can be used to manipulate other people's behavior. 00:12:38.160 |
They can be used to manipulate other people's behavior. 00:12:40.160 |
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They can be used to manipulate other people's behavior. 00:12:46.160 |
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They can be used to manipulate other people's behavior. 00:12:56.120 |
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They can be used to manipulate other people's behavior. 00:13:16.120 |
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They can be used to manipulate other people's behavior. 00:13:28.080 |
They can be used to manipulate other people's behavior. 00:13:30.080 |
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They can be used to manipulate other people's behavior. 00:13:56.040 |
They can be used to manipulate other people's behavior. 00:13:58.040 |
They can be used to manipulate other people's behavior. 00:14:00.040 |
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They can be used to manipulate other people's behavior. 00:14:06.040 |
They can be used to manipulate other people's behavior. 00:14:08.040 |
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They can be used to manipulate other people's behavior. 00:14:12.040 |
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They can be used to manipulate other people's behavior. 00:14:24.000 |
They can be used to manipulate other people's behavior. 00:14:26.000 |
They can be used to manipulate other people's behavior. 00:14:28.000 |
They can be used to manipulate other people's behavior. 00:14:30.000 |
They can be used to manipulate other people's behavior. 00:14:32.000 |
They can be used to manipulate other people's behavior. 00:14:34.000 |
They can be used to manipulate other people's behavior. 00:14:36.000 |
They can be used to manipulate other people's behavior. 00:14:38.000 |
They can be used to manipulate other people's behavior. 00:14:40.000 |
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They can be used to manipulate other people's behavior. 00:15:55.880 |
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They can be used to manipulate other people's behavior. 00:16:15.880 |
They can be used to manipulate other people's behavior. 00:16:17.880 |
They can be used to manipulate other people's behavior. 00:16:19.880 |
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They can be used to manipulate other people's behavior. 00:16:27.840 |
They can be used to manipulate other people's behavior. 00:16:29.840 |
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They can be used to manipulate other people's behavior. 00:16:33.840 |
They can be used to manipulate other people's behavior. 00:16:35.840 |
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They can be used to manipulate other people's behavior. 00:16:39.840 |
They can be used to manipulate other people's behavior. 00:16:41.840 |
They can be used to manipulate other people's behavior. 00:16:43.840 |
They can be used to manipulate other people's behavior. 00:16:45.840 |
They can be used to manipulate other people's behavior. 00:16:47.840 |
They can be used to manipulate other people's behavior. 00:16:49.840 |
They can be used to manipulate other people's behavior. 00:16:51.840 |
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They can be used to manipulate other people's behavior. 00:16:55.800 |
They can be used to manipulate other people's behavior. 00:16:57.800 |
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They can be used to manipulate other people's behavior.