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. You want to identify brain tumors? We'll just teach Watson to do it.

Whilst IBM research might be able to pull it off, it'll never get to market because there is nobody capable of making good products at IBM anymore.

As an ex-IBMer this is so true and so frustrating at the same time. Engineers are thrashed about on a nearly sprintly basis by PM's with short attention spans and no understanding of how disruptive their continuously changing requirements are.

It doesn't help that IBM consistently puts the cart before the horse is even born and pivots multiple teams all at the same time such that nothing you build upon is stable or consistent. Working there was maddening.

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.)

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

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.

I think you are on to something, put differently: If you need to use the term "AI" to enhance the marketability of the product it is probably because the product sucks.

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.

As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.

And

I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem.

I don't see how this follows from this:

the 'AI' tools we use are increasingly good function approximators

Nothing in the definition of AI says that AI has to work the same way the human brain does... and as far as that goes, we're probably not 100% sure that, in the end, the brain is anything more than a really good function approximator and some applied statistics.

I would say the canonical definition of AI, to the extent that there is one, is roughly something like "making computers do things that previously only humans could do". If people think "AI is bullshit" I'd say it's because they're applying their own definition to the term, where there definition imposes much more stringent requirements.

Yeah, maybe the marketing people said that..

Also, because something has been around for decades does not make it not AI. For ex the cheque OCR mentioned probably runs off (or can feasibly run off) of a neural network. I think the parent's comment holds well - not sure about the last line though ...

> when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI).

What I actually mean is people practicing what they call "AI" in academia and the industry have co-opted the name to make what they do sound more interesting. First it was called "statistics". Then it was called "pattern matching". Then it was called "machine learning". Now it's called "AI". But it hasn't changed meaningfully through any iteration of these labels.

To your point, I agree. They hype around the area has evolved much faster than the area itself.

FWIW, "AI" as a field has been around since the 1950's. So calling something "AI" in no way implies that the techniques are especially new.

then what is 'close to AI'?

The most creative, intelligent and least frustrating "AI" I've ever encountered was in some games, such as Dota2 or many years ago F.E.A.R. They were frustrating but only due to unpredictability, even after hundreds of hours of playtime. YouTube and NetFlix AI after hundreds/thousands of hours invested are also very unpredictable and frustrating, but that's the opposite experience I am looking for in those situations.

Translate can be useful at times...like once a year when I want to comprehend a Japanese website, usually I close the tab after 2 minutes.

I used GMail for many years and still do to some degree but I'm moving to a different mail provider. GMail's spam filter is great!

Not sure, since 2 years it became acceptable to make no difference between ML and AI. ML appears smart because of bizillions of training samples and I feel very impressed when I hear of that. But yeah, at the end of the day it doesn't have exactly the biggest impact on me... ;)

YouTube recommendations aren't great, they have a short memory and my feed is rarely diverse, it just shows a bunch of whatever I just watched.

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" :)

This is so common there’s a term for it https://en.m.wikipedia.org/wiki/Moravec%27s_paradox

Sample size and record size are two different 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.

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.

No. Siri is shit.

I’m at a large customer of theirs and they are bleeding the customer for every dollar as they get phased out. Very low caliber of services professionals too.

Here are a few of my notes (my words not the interviewee's):

- in order to use data science, you have to have creative people thinking about data on the front end

- they don't have to be data scientists, but they need to be creative and want data to support decisions and iteration via feedback loops

- that creativity and desire will lead to "doing good data science"

- management on the receiving end of data science output must be intelligent in terms of synthesizing many inputs and have a strong desire to puzzle through the implications. If management is asking the data science to actually make the decisions - the situation is broken

- data science must be done with provisions for decision support and feedback loops; this is the output that is helping drive the business.

- Lack of desire for decision support and feedback loops leads to "fancy pets" and management using data science as a means to brag about what they are doing; but the data science might not being doing anything to drive the business meaningfully.

- data science that attempts to actually make decisions vs providing decision support is likely in the category of "commodity data science". Corollary : non-commodity data science is the kind that supports decisions in executing higher-level business strategy. Strategy at that level has rather unique attributes and is embedded in unique circumstances for a particular business. This requires a good data scientist to help tackle.

(hope this is useful)

(edit typos,grammar)

whenever I'm asked to design a database for an early-stage system (I work in early stage tech ventures), I ask the following:

- what are the questions that this database should answer for you? How are those questions supporting your business goals 3,6,12 months out? (I'm trying to get to the business requirements here)

- who will be asking those questions (I'm trying to put together some user personas in my head)

- how frequently will they be asking these questions? corollary: how often will historical data be needed? (I'm thinking hot vs cold and complexity of retrieval, minimally required performance)

- how much data to we anticipate is needed to answer the questions (this is really tricky in new ventures - often the answer is more data than what will actually occur in practice in the first year)?

- finally, what systems & tools are people using to ask the questions and be notified of events? (I'm thinking about interfacing, apis)

its all an attempt to stay very focused on the questions and business drivers and the people who use the answers.

We run prediction markets inside companies and find that if we don't establish a good lifecycle of asking forecasting questions, having people respond with probabilities, then decision makers REACTING to those probabilities in some way (whether they agree with them or not, just acknowledge their existence) the likelihood of the project failing is far higher.

Watson is a $@#% amazing information retrieval system. Information retrieval is only a small part of what people think of when they think, "AI".

Which I imagine still has lots of value on it's own for large, complicated data sets.

I'd actually like to give Watson a spin for an IR problem I'm looking at, but, thanks to their hype machine being set to overdrive, they've got the thing priced in the "The Bold Leaders of the Future Creating a Bright New Tomorrow Full of People in Glasses Staring Wistfully Toward the Right Edge of the Photograph, While Blue Curvy Streaks Wave Through the Background and Random Zeroes and Ones Float Around Their Heads" tier. Sadly, I've only got a "businesses solving business problems" sized budget.

AI is not simply things that a computer can't do yet. But I think most of people who aren't currently trying to sell a piece of software would expect AI to include some things that you don't need to do to play Jeopardy. I'd want to see general-purpose pattern recognition, for example.

I can imagine someone who doesn't know at IBM selling a product:

"Hey we will solve all these problems like magic!"

Then IBM comes back:

"Hey do you have all this data in a specific format and a ton of time to enter and test it and maybe we'll get back to you???"

That's a big loss of trust there with the customer if you come back with that.

It seems like these are products where a lot of caveats needs to be made clear to customers and a real careful technical partnership formed with them to succeed long term. You have to bring the customer along for the ride and exploration and keep them excited for a long time it sounds to make it work.

To see one of their articles with the common press confusion of mixing different definitions and interpretations of AI (correct or incorrect) doesn’t help build confidence.

For example, what was used to play Jeopardy vs. approaches being taken to improve cancer treatment, are just so different, it seems almost disingenuous to throw it all haphazardly into one conceptual bucket.

The article does IBM a disservice in some ways. They come off looking bad overall but some of the failed projects mentioned like MD Anderson, failed for reasons beyond any control they had, other than recognizing some obvious red flags earlier and detaching their name and participation from it.

On the other hand I believe the article lets them offf the hook to easily when they bring out the old trope they’ve been using for years, which is encapsulated here:

“IBM Watson has great AI” [one engineer said] “It’s like having great shoes, but not knowing how to walk—they have to figure out how to use it.”

It doesn’t make sense to say, xyz is great we just have to figure out how to use it, as a stand alone argument. It’s nonsensical unless you mention something about the seeming implied untapped potential, specific innovations, novel approach, or whatever makes it great.

I’m not familiar with all their IP so maybe there are some great things, you just don’t get to claim that and get off the hook so many times in the press without providing at least some detail or reference point.

how many of those managers will now be able to get even a higher paying job because they have manager of Watson AI project on their resume?

Once scientists in biology and healthcare get on board like they are in linguistics and computer vision I'd expect things to pick up.

> A clinic could use the system to search its patient records and find, for example, all the men over age 45 who were overdue for a colonoscopy, and then use an autocall to remind them to schedule the dreaded appointment.

Even if successful, a system which could "interpret" a health record (such as a freetext note) using anything other than properly codified data would set the health industry back a decade. Moving doctors away from freetexting their notes is the only way to advance the industry.

Doing so could have incredible utility for sharing data across various clinics/hospitals/pharmacies/etc.

My previous doctor (who was probably mid-50s) didn't use email or any kind of secure electronic messaging system. Everything had to be faxed to him.

My new doctor who is younger uses all kinds of digital tools including a voice recorder with a pre-trained text-to-speech engine that understands medical terminology and codifies the transcription based on keywords.

So it's not entirely getting away from freetext but at least it's extracting some structured data from it automatically.

Maybe this was a terrible example, and the author didn't grasp a good example of legitimately non-schematic data points?

http://www.mikadosoftware.com/articles/ibmadverts

   that it will allow better and cheaper care.