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> This works well for IBM generally (the products are shit) but especially well for Watson because it's extremely easy to sell AI without getting bogged down in

by cirgue 8y ago

> This works well for IBM generally (the products are shit) but especially well for Watson because it's extremely easy to sell AI without getting bogged down in details. The cynic in me says that every use of the term AI in any capacity is to sell experience and not functionality. When was the last time you used a product billed as 'AI' and thought 'wow, this is a huge game changer'? Siri is cool, but it's ultimately not super useful. Google translate is incredible, but it can only do what it can do because of the absolutely mind-boggling amount of training data that google can access. Most disciplines have the problem of not enough data, despite what 'big-data' folks say. In contrast, humans can extrapolate and make reliable predictions about the future based on really small sample sizes. We can pick up a new skill or recognize a new pattern with a high degree of accuracy really effing fast compared to a computer. This gives humans an enormous advantage. If IBM and anyone else in this space were really focused on delivering excellent real-world results, step 0 is building out world-class data integration and search tools (which we still actually suck at, weirdly.)

NicoJuicy 8y ago
Many people mention even a worser search experience with Google and AI
- davidf18 8y ago
  Could you please elaborate? Does that include DeepMind?
mlthoughts2018 8y ago
In fact, I would say apart from maybe self-driving cars, almost all of the biggest gains from machine learning are in unsexy, hidden backend problems, like automatically rectifying disparate data, optimizing resource utilization, flagging difficult-to-articulate events or triggers in a stream of data too large for human evaluation, machine translation, and other “unsexy” things. Product interfaces usually offer simple features to users and the value proposition is easy to see. Effective use of machine learning is well hidden upstream in a bunch of unsexy preprocessing or heavy lifting to get to the interface. Not something you’d ever need to emphasize in marketing, except maybe at tech meetups or in recruiting materials, but not to the end consumer. It just makes pop references to AI-powered products more egregious.
nostrademons 8y ago
I use & depend upon plenty of products that are built upon AI - GMail spam filtering & categorized inbox, Google image search, YouTube & Netflix recommendations, cheque OCR at my ATM, predictive keyboards on my phone, Amazon's "people also buy with this product" feature, Google translate, computer opponents in games that I play, and all of the signals that feed into Google Search. The irony is that not one of these bills itself as AI. It's just "a product that works", and the company that produces it is happy to keep the details secret and let users enjoy the product. So you may be right that the term "AI" itself is pure salesmanship. When it starts to work it ceases to be AI. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect Also - humans only look like we're fast at picking up new domains because we apply a helluva lot of transfer learning, and most "new" domains aren't actually that different from our previous experiences. Drop a human in an environment where their sensory input is truly novel - say, a sensory deprivation tank where all visual & auditory stimulation is random noise - and they will literally go insane. I've got a 5-month-old and a project where I'm attempting to use AI to parse webpages, and I will bet you that I can teach my computer to read the web before I can teach my kid to do so.
- cirgue 8y ago
  None of the things you mentioned are even close to AI. They’re applied statistics, and they mostly use techniques we’ve known about for decades but have only now found a use case because computing and storage is cheap enough to make them viable.
  - nostrademons 8y ago
    The recommendation, translation, & image classification algorithms are all done with deep-learning; that's considered AI now. There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals. There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the game opponents. As the linked Wikipedia article says, "AI is whatever we don't know how to do yet." There will be a time (rapidly approaching) where deep learning and robotics are common knowledge among skilled software engineers, and we won't consider them AI either. We'll find something else to call AI then, maybe consciousness or creativity or something.
nnq 8y ago
> humans can extrapolate and make reliable predictions about the future based on really small sample sizes You severely underestimate the bandwidth of your eyes and ears and other senses, and the volume of your brain's memory (despite it's uber-loosy compression). That's terabytes a day probably, if not big data than I dunno what is. Yeah, 99% of it is thrown away at passing through the first few hundreds of layers of your neural networks, but they still know what to throw away... To get a digital computer on "equal" terms with the zillions of hacky optimizations your semi-analog brain uses you need a shitton of raw power and data volume ("if you don't know what to throw away of the input data, you need to just sift through all/more of it") to compensate for the fact that you don't have N million years of evolution to devise similar hacky optimizations. Also, humans work as a "network of agents", that's also recurrent (aka "culture"). Current sub-human-level AI agents are far from any sort of reliable interop. My guess is that we'll get human level performance levels at AGI tasks when we learn to build swarms of AI-agents that cooperate well and "model each other", and few people are working on this... Heck, when it happens it will probably be an "accident" of some IoT optimizations thing, like, "oops, the worldwide network of XYZ industrial monitoring agents just reached sentience and human level intelligence bc it was the only way it could solve the energy-efficiency requirement goals it was tasked to optimize for" :)
- cirgue 8y ago
  > You severely underestimate the bandwidth of your eyes and ears and other senses Sample size and record size are two different things.
- gaius 8y ago
  You severely underestimate the bandwidth of your eyes and ears and other senses This is so common there’s a term for it https://en.m.wikipedia.org/wiki/Moravec%27s_paradox https://en.m.wikipedia.org/wiki/Moravec%27s_paradox
Aunche 8y ago
> We can pick up a new skill or recognize a new pattern with a high degree of accuracy really effing fast compared to a computer. Evolution by natural selection is the OG genetic algorithm, and it's been "running" on billions of organisms in parallel for hundreds of millions of years. The intuition that we take for granted such as the abstract concept of a shape is all hard-coded in our brains from trial and error.
dirkgently 8y ago
> Siri is cool No. Siri is shit.
- abiox 8y ago
  is anything in the category of siri-like things not shit?

by cirgue 8y ago

NicoJuicy 8y ago

Many people mention even a worser search experience with Google and AI

davidf18 8y ago
Could you please elaborate? Does that include DeepMind?

mlthoughts2018 8y ago

In fact, I would say apart from maybe self-driving cars, almost all of the biggest gains from machine learning are in unsexy, hidden backend problems, like automatically rectifying disparate data, optimizing resource utilization, flagging difficult-to-articulate events or triggers in a stream of data too large for human evaluation, machine translation, and other “unsexy” things. Product interfaces usually offer simple features to users and the value proposition is easy to see. Effective use of machine learning is well hidden upstream in a bunch of unsexy preprocessing or heavy lifting to get to the interface. Not something you’d ever need to emphasize in marketing, except maybe at tech meetups or in recruiting materials, but not to the end consumer. It just makes pop references to AI-powered products more egregious.

nostrademons 8y ago

I use & depend upon plenty of products that are built upon AI - GMail spam filtering & categorized inbox, Google image search, YouTube & Netflix recommendations, cheque OCR at my ATM, predictive keyboards on my phone, Amazon's "people also buy with this product" feature, Google translate, computer opponents in games that I play, and all of the signals that feed into Google Search. The irony is that not one of these bills itself as AI. It's just "a product that works", and the company that produces it is happy to keep the details secret and let users enjoy the product. So you may be right that the term "AI" itself is pure salesmanship. When it starts to work it ceases to be AI. https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect Also - humans only look like we're fast at picking up new domains because we apply a helluva lot of transfer learning, and most "new" domains aren't actually that different from our previous experiences. Drop a human in an environment where their sensory input is truly novel - say, a sensory deprivation tank where all visual & auditory stimulation is random noise - and they will literally go insane. I've got a 5-month-old and a project where I'm attempting to use AI to parse webpages, and I will bet you that I can teach my computer to read the web before I can teach my kid to do so.

cirgue 8y ago
None of the things you mentioned are even close to AI. They’re applied statistics, and they mostly use techniques we’ve known about for decades but have only now found a use case because computing and storage is cheap enough to make them viable.
- nostrademons 8y ago
  The recommendation, translation, & image classification algorithms are all done with deep-learning; that's considered AI now. There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals. There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the game opponents. As the linked Wikipedia article says, "AI is whatever we don't know how to do yet." There will be a time (rapidly approaching) where deep learning and robotics are common knowledge among skilled software engineers, and we won't consider them AI either. We'll find something else to call AI then, maybe consciousness or creativity or something.

nnq 8y ago

> humans can extrapolate and make reliable predictions about the future based on really small sample sizes You severely underestimate the bandwidth of your eyes and ears and other senses, and the volume of your brain's memory (despite it's uber-loosy compression). That's terabytes a day probably, if not big data than I dunno what is. Yeah, 99% of it is thrown away at passing through the first few hundreds of layers of your neural networks, but they still know what to throw away... To get a digital computer on "equal" terms with the zillions of hacky optimizations your semi-analog brain uses you need a shitton of raw power and data volume ("if you don't know what to throw away of the input data, you need to just sift through all/more of it") to compensate for the fact that you don't have N million years of evolution to devise similar hacky optimizations. Also, humans work as a "network of agents", that's also recurrent (aka "culture"). Current sub-human-level AI agents are far from any sort of reliable interop. My guess is that we'll get human level performance levels at AGI tasks when we learn to build swarms of AI-agents that cooperate well and "model each other", and few people are working on this... Heck, when it happens it will probably be an "accident" of some IoT optimizations thing, like, "oops, the worldwide network of XYZ industrial monitoring agents just reached sentience and human level intelligence bc it was the only way it could solve the energy-efficiency requirement goals it was tasked to optimize for" :)

cirgue 8y ago
> You severely underestimate the bandwidth of your eyes and ears and other senses Sample size and record size are two different things.
gaius 8y ago
You severely underestimate the bandwidth of your eyes and ears and other senses This is so common there’s a term for it https://en.m.wikipedia.org/wiki/Moravec%27s_paradox https://en.m.wikipedia.org/wiki/Moravec%27s_paradox

Aunche 8y ago

> We can pick up a new skill or recognize a new pattern with a high degree of accuracy really effing fast compared to a computer. Evolution by natural selection is the OG genetic algorithm, and it's been "running" on billions of organisms in parallel for hundreds of millions of years. The intuition that we take for granted such as the abstract concept of a shape is all hard-coded in our brains from trial and error.

dirkgently 8y ago

> Siri is cool No. Siri is shit.

abiox 8y ago
is anything in the category of siri-like things not shit?