<story><title>AI is mostly about curve fitting (2018)</title><url>https://diginomica.com/ai-curve-fitting-not-intelligence</url></story><parent_chain><item><author>ipsa</author><text>&amp;quot;Machine learning&amp;quot; used to be a safe haven. You could flee there to escape the Terminators and brain-on-a-chip graphics. Business PR deliberately killed that. They wanted their ML algorithms to be refered to as AI, so they could fully ride the hype train.&lt;p&gt;AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant with &amp;quot;learning&amp;quot;, &amp;quot;intelligence&amp;quot;, &amp;quot;to perceive&amp;quot; in the context of AI. Nowadays, it is almost irresponsible to do this, not because you&amp;#x27;ll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.&lt;p&gt;Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and &amp;quot;out there&amp;quot;. Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with &amp;quot;an AI taught itself to X and you won&amp;#x27;t believe what happened next&amp;quot;.&lt;p&gt;The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they&amp;#x27;ve solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.&lt;p&gt;All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.</text></item></parent_chain><comment><author>erikpukinskis</author><text>What’s wrong with anthropomorphizing?&lt;p&gt;I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own.&lt;p&gt;But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionalism are actually extremely rare. The main source of our uniqueness is that we amalgamate other species, not that we have transcended them.&lt;p&gt;My theory is that we are terrified that we might be simpler than we think, because socially we behave as if we are so singular. If we are simple, and animals and machines are like us, then maybe we should be treating them with more reverence.&lt;p&gt;But being afraid of that is OK for a random person. For a machine learning researcher I would hope they are more careful about what we have evidence for (the similarities between us) and what we don’t (that there is some ineffable magic about humans).</text></comment>
<story><title>AI is mostly about curve fitting (2018)</title><url>https://diginomica.com/ai-curve-fitting-not-intelligence</url></story><parent_chain><item><author>ipsa</author><text>&amp;quot;Machine learning&amp;quot; used to be a safe haven. You could flee there to escape the Terminators and brain-on-a-chip graphics. Business PR deliberately killed that. They wanted their ML algorithms to be refered to as AI, so they could fully ride the hype train.&lt;p&gt;AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant with &amp;quot;learning&amp;quot;, &amp;quot;intelligence&amp;quot;, &amp;quot;to perceive&amp;quot; in the context of AI. Nowadays, it is almost irresponsible to do this, not because you&amp;#x27;ll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.&lt;p&gt;Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and &amp;quot;out there&amp;quot;. Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with &amp;quot;an AI taught itself to X and you won&amp;#x27;t believe what happened next&amp;quot;.&lt;p&gt;The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they&amp;#x27;ve solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.&lt;p&gt;All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.</text></item></parent_chain><comment><author>unityByFreedom</author><text>&amp;gt; The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general.&lt;p&gt;I&amp;#x27;ve come to terms with the hype. There are still researchers doing the hard theoretical work, and they will still be toiling away after the next economic downturn. We can all choose every day whether to find fulfillment through seeking attention from other people, money, or satisfying our curiosity to solve problems.&lt;p&gt;&amp;gt; Nobody from the research community ever claimed that [AGI], nobody came forward to claim they&amp;#x27;ve solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.&lt;p&gt;Hear hear! That said, this is a good article by a respected researcher. Here&amp;#x27;s what LeCun had to say about it,&lt;p&gt;&amp;gt; ...In general, I think a lot of people who see the field from the outside criticize the current state of affair without knowing that people in the field actively work on fixing the very aspects they criticize.&lt;p&gt;&amp;gt; That includes causality, learning from unlabeled data, reasoning, memory, etc. [1]&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.facebook.com&amp;#x2F;yann.lecun&amp;#x2F;posts&amp;#x2F;10156387222842143&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.facebook.com&amp;#x2F;yann.lecun&amp;#x2F;posts&amp;#x2F;10156387222842143&lt;/a&gt;</text></comment>