<story><title>Can Demis Hassabis save Google?</title><url>https://www.bigtechnology.com/p/can-demis-hassabis-save-google</url></story><parent_chain><item><author>dbmikus</author><text>I think part of the problem for LLMs is that they don&amp;#x27;t operate in an easily scoreable &amp;quot;game&amp;quot;. We can make them work well for optimizing next token(s) log likelihood, but how do we judge quality of the output for the task it was made for?&lt;p&gt;Then after that, there are other challenges, such as do LLMs have a world model that lets them think how to attain a reward?&lt;p&gt;Wherever you can have an automated feedback mechanism, you can start to address the first problem and allow for the AI to explore more of the &amp;quot;tree&amp;quot;. These are situations like coding (you can run the code and evaluate the output), or situations where you can let the LLM crowd-source user feedback.&lt;p&gt;For models that have some actual world model, who can reason across modalities, and who can plan, LeCunn talks about this often. The videos&amp;#x2F;slides here[1] were good content.&lt;p&gt;[1]: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.ece.uw.edu&amp;#x2F;news-events&amp;#x2F;lytle-lecture-series&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.ece.uw.edu&amp;#x2F;news-events&amp;#x2F;lytle-lecture-series&amp;#x2F;&lt;/a&gt;</text></item><item><author>andy_xor_andrew</author><text>Given his experience with AlphaGo&amp;#x2F;Star&amp;#x2F;Zero&amp;#x2F;etc, I&amp;#x27;m sure if there&amp;#x27;s any way to apply tree search, or policy networks, to LLMs, Demis will find it.&lt;p&gt;I&amp;#x27;ve always found it strange that a LLM is basically an action&amp;#x2F;value model that scores all next possible actions (tokens), and yet we DON&amp;#x27;T traverse it like a tree. Well, &amp;quot;beam search&amp;quot; exists, but that&amp;#x27;s kind of the most naive approach.&lt;p&gt;Then again, I&amp;#x27;m not smart, and lots of very smart people are thinking nonstop about LLMs, and no type of tree search has become widely used, so maybe there&amp;#x27;s really nothing there.</text></item></parent_chain><comment><author>roenxi</author><text>Qualitatively, the answer seems to be to train for novelty. Which happens to also be how humans accomplish much of the complex stuff.&lt;p&gt;Eg, One interpretation of art is we have 8 billion or so humans with a finely trained neural net for recognising stuff. The artist is looking for novel ways of triggering those nets, and if they find something inspired then it is art. A great artist generally isn&amp;#x27;t trying to reproduce something that is known, they&amp;#x27;re trying to explore novel areas of a medium.&lt;p&gt;So the real trick to building the world model is coming up with a good novelty metric. Still hard, but easier than developing a reward function. That gets the part of the training done that establishes a world model, &lt;i&gt;then&lt;/i&gt; I&amp;#x27;d assume it is possible to train that model to do a task by rewarding specific outcomes that it already knows how to achieve.</text></comment>
<story><title>Can Demis Hassabis save Google?</title><url>https://www.bigtechnology.com/p/can-demis-hassabis-save-google</url></story><parent_chain><item><author>dbmikus</author><text>I think part of the problem for LLMs is that they don&amp;#x27;t operate in an easily scoreable &amp;quot;game&amp;quot;. We can make them work well for optimizing next token(s) log likelihood, but how do we judge quality of the output for the task it was made for?&lt;p&gt;Then after that, there are other challenges, such as do LLMs have a world model that lets them think how to attain a reward?&lt;p&gt;Wherever you can have an automated feedback mechanism, you can start to address the first problem and allow for the AI to explore more of the &amp;quot;tree&amp;quot;. These are situations like coding (you can run the code and evaluate the output), or situations where you can let the LLM crowd-source user feedback.&lt;p&gt;For models that have some actual world model, who can reason across modalities, and who can plan, LeCunn talks about this often. The videos&amp;#x2F;slides here[1] were good content.&lt;p&gt;[1]: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.ece.uw.edu&amp;#x2F;news-events&amp;#x2F;lytle-lecture-series&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.ece.uw.edu&amp;#x2F;news-events&amp;#x2F;lytle-lecture-series&amp;#x2F;&lt;/a&gt;</text></item><item><author>andy_xor_andrew</author><text>Given his experience with AlphaGo&amp;#x2F;Star&amp;#x2F;Zero&amp;#x2F;etc, I&amp;#x27;m sure if there&amp;#x27;s any way to apply tree search, or policy networks, to LLMs, Demis will find it.&lt;p&gt;I&amp;#x27;ve always found it strange that a LLM is basically an action&amp;#x2F;value model that scores all next possible actions (tokens), and yet we DON&amp;#x27;T traverse it like a tree. Well, &amp;quot;beam search&amp;quot; exists, but that&amp;#x27;s kind of the most naive approach.&lt;p&gt;Then again, I&amp;#x27;m not smart, and lots of very smart people are thinking nonstop about LLMs, and no type of tree search has become widely used, so maybe there&amp;#x27;s really nothing there.</text></item></parent_chain><comment><author>Zambyte</author><text>&amp;gt; I think part of the problem for LLMs is that they don&amp;#x27;t operate in an easily scoreable &amp;quot;game&amp;quot;.&lt;p&gt;Unfortunately &amp;quot;social media&amp;quot; is the gamified environment for language.</text></comment>