They probably decided they wanted to be part of that future market about AI and therefore want to invest in AI/ML startups. Someone sold your company internally as being a nice fit for the AI portfolio, but when you answered the question about ML with "No" the door was shut.
From someone who makes large trade deals as his daily business, I learned "Never say 'No'" (during a negotiation). In most cases, it is better to be diplomatic. So might have been better off with something like:
'So far we had decent results without ML, but we are constantly evaluating options to improve our technology, which includes optimizations based on machine learning.'
That way, some might hear '[...] includes optimizations based on machine learning.', while you just said 'we do not use machine learning' ;-)
No, fuzzy logic won't make your washing machine better. Yes, you can implement some stuff in fuzzy logic.
Ideally, people that do nothing but tick off boxes in their mental buzzword checklists should not even be allowed to attend the pitches, and thereby waste other people's time by forcing them to hack around the unrepentant ignorance. But they that have the gold, make the rules.
[1] https://www.unc.edu/courses/2009spring/plcy/240/001/Kant.pdf
“Act only according to that maxim whereby you can at the same time will that it should become a universal law.” — Immanuel Kant, Grounding for the Metaphysics of Morals
There's really not, but it might make you feel better to think there is.
We have already seen this with things like offshoring.
A few big name companies did the numbers and found they could save some on moving their production somewhere else.
Then came a rush of companies that made big that they had offshored who knows what, simply because that was what the stock market expected of a would be forward looking company.
The basic problem is that these decisions are not done based on what is good for the company long term, but what is good for the stock price short term. And in large part because CEOs are paid in stock options, and can be ousted by activist stock owners.
Why not put "BeeLine Reader makes reading on-screen easier, faster, and more enjoyable. We use a simple cognitive trick — an eye-guiding color gradient — to pull your eyes from one line to the next." on your front page?
Maybe you guys have already done some kind of testing and found out that the current layout is optimal?
One lesson learned: I should have linked to the /individual page instead of the generic landing page in my comment. Updating link now...
Publishers, educators etc also need to understand what the value proposition is for their users.
I also think the page ought to be written in longer sentences. The current silo buttons aren't even making it clear that they explain more.
So, suggestions:
You just have to take the few sentences from your Individuals page "Reading on-screen can be tough on your eyes, especially if you have to do it all day long. We’re here to help. BeeLine Reader makes reading on-screen easier, faster, and more enjoyable." and put that on your landing page instead of "A New Way to Read."
Now change the "(choose)" subheading to "perspectives:", put it lower nearer the buttons instead of near the description, and perhaps left-align it.
Change the buttons from meaningless colours with labels to something descriptive like "for you the user :smile:", "for the publisher $" and "for the educator :graduation-hat-icon:".
Voila! good luck!
Maybe something generic like "improve focus while reading", which could appeal to individuals and publishing partners? Appreciate your taking the time to share feedback — this sort of stuff is not something we're skilled/experienced at.
I think it will be very helpful to have a simple description in your site. First impression matters.
p.s. I like the idea of your product.
The silos are good, you just need to make it much more obvious that the user needs to select one of the silos in order to get at more information. It's not particularly obvious that your buttons are buttons. I would also recommend that the content be full height / width so that on desktop at least the user doesn't scroll at all. The stock photo is probably unnecessary for the main page. Also you don't really need left/right margins since there isn't really text on the page, and that's what large margins are good for (but I'm assuming you know that since you made this app!)
Things get more complicated, as the browser plugins are subscription ($2/mo or $22/yr) and the iOS app is one-time IAPs (mostly a few bucks, but more for the Kindle feature). I'm not sure how to succinctly communicate this, but you and other commenters make clear that we need to have some info here.
Thanks for taking the time to share your thoughts!
CNET did a study [1] that showed BeeLine readers were much more likely to finish an article, but it would be great to take things to the next level and see how that translates into purchases or other conversions.
Shoot me an email! contact@[domain]
1: the CNET study is discussed at the bottom of this article http://www.theatlantic.com/technology/archive/2016/05/a-bett...
It doesn't need to be a lot of detail, or precise. Even just a "from $..." or "plans starting at ..." would be enough.
* I know that I will forget I even clicked on that link. The page has 3.5 seconds to convince me to stay longer.
* I'm mildly intrigued and form, in my head, a price I'd be willing to pay for the product.
* I scan the navbar for a pricing link to see if my expectation matches reality. I find nothing.
* The site obviously doesn't value my time, so why should I stay?
It's disrespectful.
Second, the other saying is if you have to ask how much it costs, you can’t afford it. So by concealing the price, the company is telling me the cost is too high and they know it. They are telegraphing that part of their sales process requires a salesperson to coerce the money out of me.
Also, note that we don't actually require (or even ask for) email addresses or anything personal when you install the browser plugin. You just click install, try it for 2 weeks, and then buy if you want. The standard pricing is $2/mo, with a discount for annual purchase.
I totally appreciate the feedback and will see how we can better communicate our pricing to alleviate concerns like this.
I should also note that reading ease is an important (but difficult to measure) piece of this as well. So if it felt better for you, then it might be worth trying out the browser plugin and then trying the reading challenge again. There could be a little bit of a learning curve, as your eyes adjust to how quickly you can jump from line to line.
Also, if you ever have considered two code variants where one is a bit better in situation A and the other in situation B, you can use machine learning to combine the two methods, (e.g. using a random forest (https://en.wikipedia.org/wiki/Random_forest)
Machine Learning = statistics + linear algebra + computer science, mostly.
Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems. Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.
Just because Bayesian isn’t over-hyped doesn’t mean it doesn’t solve real problems. Not everything can be solved with ML, unless the problem is getting more investor money.
Now, the biggest difference that I can see is that, within machine learning contexts, people are more concerned with the quality of the predictions than the interpretation of the independent variables. That’s not hard and fast, but it seems to be a common thread.
And for some problems, making a good prediction is really the right thing. In other cases, understanding the mechanisms you might use to effect an outcome is more important. Both are valid uses of statistical methods, depending on the problem.
I think within the realm of classical statistics, Bayesian methods’ super power is in being able to generate results that are much easier to communicate to lay people. Also Bayesian methods are nice if you want to do sensitivity analysis in a principled up front kind of way.
But I could imagine using those methods in an ML context even if they aren’t the current darlings of the methodological pantheon.
Having worked with Oracle's version, and seen entire systems buult out of that, I dread it.
Also, the argument that it's pure statistics doesn't really fly - support vector machines, random forests or deep learning multilayer perceptrons are just as much statistics as probabilistic graphical models.
I think they just feel "toy" because they've been used with great accuracy for so long.
Complex problems are rarely solved by a single approach. The harder the problem the more likely a suite will be used, and often naive bayes will be part of that in some capacity.
Naive Bayes is a common baseline solution because it's very simple to implement and very fast to run; however, pretty much any other ML method gives better accuracy than Naive Bayes. Sometimes the accuracy improvement is small and doesn't justify the highly increased computing cost, though, so Naive Bayes becomes the appropriate method to use, but it's not because of its accuracy.
Bayesian models are also advantageous in that they are much easier to describe to stakeholders. Neural networks, on the other hand, are highly difficult(if not impossible in many cases) to describe how they function.
AI is about substituting human decision makers or other conventional computer decision algorithms. AI is used for situational decision making. It needs to be quick (like the conventional algorithms) while also recognizing the nuance and multi-dimemaional nature of a lot of decisions. Robustness and quickness are often valued more than precision.
IOW the GP is only using ML for everything because it is a buzzword.
What you use as description for AI tempts me to use the "use it as a buzzword" angle back at you here - it is a stereotypical description of some use cases of AI approaches; but there are others - [machine] learning, knowledge representation, planning and scheduling, reasoning (both formal logic reasoning and also reasoning under uncertainty e.g. Bayesian approaches), intelligent agent representation, etc, are all parts of the AI field.
Machine learning is the use of computers to make decisions (or classifications) based on data without human intervention.
Hypotheses:
> corporate VC that wanted to make a strategic investment
They want to put money in your company to make sure they don't have to compete with you or compete with someone else who might buy you down the line.
> They kept prodding around on the ML stuff, and how we might be able to use ML to accomplish roughly the same thing.
They're looking for secret sauce, barriers, friction. Something patentable. Something to keep others out and keep them ahead. Perhaps something that they don't think they can do themselves or maybe rip off when they realize it's useful. Remember Flux?[0]
They're investment thesis makes sense from this perspective: they're thinking like classic, amoral businesses.
Also note that if your product is simple and popular enough, people will likely make free clones. You'll need to be a good steward to keep your value.
Best of luck!
----
A bit of feedback: you need to be more transparent and up front about what how and why you're using analytics, and let people opt-in. I was a bit disconcerted to see you using Google Analytics in your extension without informing me.
That's a new one to me. Is this Facebook changing the landscape right now, or have you been expecting this for a while? Do you have sites in mind that warn about GA?
Pretty much all websites use GA or something like it, and in my experience it's extremely rare to be warned about it. It always goes in the privacy policy, which you should be able to find. But I'm not sure I've ever seen an advance warning that analytics was taking place. I suppose it's assumed, but in any case appears to be normal and acceptable to not warn people that logging and searching of those logs exists.
Cookies are a different story, since the EU passed legislation requiring notice of their presence.
While that may be true, I'd suggest the VC's are just looking for a later exit when a greater fool buys them out. They're betting on the ML investment market getting hotter, and providing you meet the criteria of being a growing startup (by some metric) nominally in the ML field and that field attracts more investment dollars later - even whether or not you succeed as a bottom line business, or whether your business success comes from ML or not - doesn't necessarily matter.
More importantly, we don't tie GA or any other usage analytics to individual users. We basically just use it to see where the extension is used and where people are blacklisting sites. We use this data to make the extension run better on more sites. We don't monetize user data in any way. You're right that we should make this point more salient. In our Privacy Policy, we do describe how to opt out by blocking GA, but we should put this somewhere more prominent.
Thanks for the feedback!
You may not identify individual users but Google probably do...
> we should put this somewhere more prominent
You should make it opt in by default. Or stop doing it!
What? Can you post some examples of sites that already do this? Everybody has analytics on their landing pages, and I don't think I've ever personally seen an opt-in.
What you can find are thousands of examples of site policy pages that say something like: "by choosing to visit our site, you are sharing information with us that we log for the purposes of improving our customer experience. By using our service, you agree to this logging. If you don't want to be logged, then please don't use our service."
Google Analytics is already GDPR compliant, as long as:
* you are not pushing any unencrypted customer data to them in the clear
* you're careful regards who has access to your Google Analytics data
You can (and most businesses will) argue that you need to know how your website is used. It's a completely standard practice that doesn't put user privacy at risk. Moreover, Google has already offered an opt-out to Google Analytics globally, and you would be covered by that. As such, you don't need them to opt-in.
I wouldn't like it if someone followed me around meatspace with a clipboard all day. I don't like it online either.
I really appreciate all the candid advice in this thread and hope that we — and others in similar situations — can use it to improve our products.
- You're already not getting data from people with ad-blockers, consider also respecting Do Not Track[0] - Be transparent about what you track and how it's used - The data collected from opt-in users might end up being enough to build convincing enough reports for impact questionnaires? - Your privacy policy seems confused: "We think the best way to ensure that your personal information stays safe is to never collect any in the first place" then "we only use Google Analytics, which gathers information about our site users and generates aggregate reports to help us figure out who is using our site and extensions, and in what ways"
[0]: https://developer.mozilla.org/en-US/docs/Web/API/Navigator/d...
Parade of Fans for Houston’s Funeral
NEWARK - The guest list and parade of limousines with celebrities emerging from them seemed more suited to a red carpet event in Hollywood or New York than than a gritty stretch of Sussex Avenue near the former site of the James M. Baxter Terrace public housing project here.
1: https://gist.github.com/blairanderson/85cc961295fd03a6c4b3
The system doesn't actually use ML or AI in realtime. ML just validates the correct conditionals and decisions are used in the software.
Most ML models can be distilled down using the Pareto principle anyway, and the 80/20 rule written into the code.
A politically correct and high-integrity story to tell investors might be "We use machine learning regression analysis to validate our expert systems are operating on optimal statistical models."
Just a quick bug report - it does the wrong thing on right-to-left text. e.g. on this article [1], it highlights the beginning (right side) of one line and the end (left side) of the next line in the same color, which doesn't actually reflect the flow of reading. There's also a weird effect on LTR text embedded in the RTL (on my setup, the "a" in "axios" on the third line of the body is bright red, while the rest is complete black).
Sending a screenshot by e-mail now.
Do they just jump on every bandwagon in the hope that one of them might pan out?
VCs raise money from fund managers (sovereign wealth funds for example) who also present a thesis to their stakeholders for how the money will be managed.
Depends on the timing but I think VC focus on ML is a good indicator of where smart money thinks the money is going to be made. But there are a lot of people that catch a trend in the tail in, I don’t think we’ve gotten anywhere close to that for ML investments.
But in this particular case, it was a corporate VC, investing their own money.
(I'm working on a startup right now that's in a similar position - non-hyped B2B segment. VCs are all "hey, look, we don't understand X market very well, maybe go to Y VC?" And Y VC is all "hmm, you're only tangentially related to Z market that we actually understand, so you'll need to get to Series A-sized revenues before we'll put in a seed-size investment.")
What has AI really given us so far? Amazon / Youtube recommendations and some self driving cars that have crashed in unexpected ways. (Have they managed to debug that yet?)
But their manager told them ML, ML, so they need to invest in ML. This is not a joke.
But it's so much damn data so loosely coupled to a company's actual product that trying to get any actionable intelligence out of it is basically impossible. Enter ML. "Just" chuck your data at a NN use A/B tests to train and hope the company ends up with higher revenues, if they do: claim the 'data scientists' are definitely a profit center; if they don't: claim you need more data.
Until Exxon-Mobile stops storing metrics on how many goldfish I own in a given month, cranking out ML related companies seems like a good bet for VCs because it's not going to get any easier to turn progressively more esoteric data points into money.
Disqualify the easy "no", then everything else is a "maybe". Invest and move on.
Whether it is true or not in this case they may have perceived that if it was based on a ML approach it might be harder for the competition to replicate if it was successful.
You may have pitched a perfectly fine product to somebody in the market for a perfectly fine acquihire.
Interesting idea overall. I've been using spritz a lot recently and I'm liking it more and more though it doesn't seem to have progressed from when I discovered it a few years ago.
Re the investors blindly wanting ML - we have this problem too (and it's definitely a problem - I can see this in spite of having a masters in ML). On the bright side, some investors - often the better ones - have said things like "I was interested the moment you didn't use ML or blockchain in your pitch", so stay true to your vision :)
I read the updated /individual page and found it very... difficult.
The colours led me to speed up and slow down at an uncomfortable rate, to the extent I had to re-read it three times. I typically receive 200-1000 emails per day which all need to be read (yay!) plus at least 30k words per day out of email (business stuff but excluding newspapers, books, etc).
I speed-read about 6-10 words per flick of the eyes (for a short document), which is about 50 words per second. Do slow down to ponder: careful phrasing, a needingly precise written document, some graphics, and this is not for detriment.
I found the colour-coding very difficult, however.
Also, there are issues with red-screen sleep promoting systems like f.lux.
My first company provided a speed reading training course for the best part of a day.
It wasn't the best targeted course for most people there. We mainly read legal and policy documents which by their nature are terse, and the method of speed reading taught traded-off retention against speed against accuracy. It was more of a concept, which you can go off with an develop yourself. So here's the idea -
1. Start with a text that is quite verbose but not overly so. A code specification or Brett Easton Ellis novel is a no. A light news article should be fine or non-technical book should be fine.
2. Recognise that when you read, you don't read a word from letter start to finish, you basically look in the middle of it and the word is recognised. If you have dyslexia, this technically probably doesn't work; but if you have dyslexia, you've probably invested countless painful hours to something a lot of us take for granted and hats off to that.
You might not notice, but you probably read multiple-words at a time already. Commonly paired words are often read together, like 'a few'. YMMV. Harness that.
3. Mentally split a page of text into narrow columns, perhaps the width of 2 medium length words. Flick your eyes across a line of text. Only focus on the middle of the mental column and absorb the words, like a snapshot (no need to blink).
This is where accuracy starts to degrade. If the text consists of obscure words or lots of figures, accuracy can hurt at the start. Grammar patterns are important too - as long as the text is written by a native or very close near native speaker, anticipation and auto-ordering in this mini-snapshot can be hard or impossible; likewise unfamiliar or complex sentence structures from a native speaker.
4. Now, start to speed up over a paragraph. This is where retention starts to hurt. You certainly don't want to be wording-out words, as this will slow you down and defeat the purpose. But when you've read a block of text, have you been able to follow the meaning at the end.
Again, using well written, non-overly terse training text is important.
5. You should be tired by now, having done this for several hours. Our trainer then suggested changing the 'snapshot' from one line, to two lines, so you're essentially reading the start of the second line before finishing the end of the first line. I can do this but your text needs to be super non-terse for this to work at the start, and even then it's weird. If you can cover a single line pretty fast in less than a second certainly, then this starts to become feasible.
tl;dr
Went on a training course. Was pessimistic as were my colleagues at the start. Persisted afterwards by trying a few minutes per day. Became a habit, especially when seeing articles or documents that I needed to read but didn't need to scrutinise. Important to choose the right tool for the right job. I'd not use this technique to read a document where I'm not familiar with the content-scope; I wouldn't use this technique to read a contract I'm signing, or where specifications/requirements are being very clearly outlined. I do use it to read things like bloomberg.com articles, or PMBOK Guide :)
Hope a little useful, and the above can set you on some kind of direction to learn more. I highly recommend a half-day instructor-led course to kick you off. I imagine there's a college near you which has something.
Up front, many investors only seek buzzwords before they shift focus to numbers. Be a sales(wo)man, and feed them the ones they want. Later on, once you end up making them money, they're not going to care much about what technologies are involved.
I wish you the best of luck with your startup in the future.
(P.S. I had a FOUC on your webpage. The icons in the "want more" section were missing, and haphazardly popping in. Might want to fix that.)
I read a lot of blogs. Thought you could add a wp plugin if it doesn't exist.
Also you must have a ml/ai roadmap for tracking user interactions, etc for possibility of further personalization. Leaving aside the buzz of ai/ml, I think every business has opportunity to apply ml/ai. It doesn't happen often but when it does, its gold for any business
Right now, it's easy to say it's a color gradient that wraps from one line to the next, guiding your eyes. I don't know how I insert "machine learning" into that sentence somewhere and not sound like I'm trying to trend-surf.
I think people are too often too quick to assume that machine learning necessarily implies deep learning and neural networks, when often it can reasonably be reduced to simple statistical approaches.
You should probably link to the add-on page (https://addons.mozilla.org/en-US/firefox/addon/beelinereader...) instead of directly to the .xpi file. I would never accept to install a plug-in from a random link even if it hadn't been for the dialog's scary language.
Another explanation: they didn't actually know how they wanted us to us ML — they just knew they wanted us to use it.
I think you are underestimating how useful NLP could be in your application.
I tried out one of your test cases, and I did find it useful. However, I think that blending colors based on entity recognition along with your line based system could focus attention on important parts.
Have a look at [1] (sorry for the long URL) and imagine the colors blending with yours, so entities were slightly brighter than the rest of the text.
[1] https://explosion.ai/demos/displacy-ent?text=John%20Fitzgera...
But honestly the biggest barrier we face in adoption among licensees (platforms that would integrate our tech) is that they are simply uncomfortable with text that has colors in it. It's not about how much it helps any metrics (the things that NLP could improve) — it's just that most folks don't want to be early-adopters of crazy-looking tech.
Though I should note we do have some great licensees, especially in the education, impact, and accessibility markets.
Regardless, I'd love to chat with you further — please shoot me an email (contact@[domain]) if you're up for chatting about how we could deploy NLP as we develop.
It sounds to me like you should talk to those investors again ;)
I'll drop you an email.
So I guess test it and variations?
• CNET found that readers were 35% more likely to finish reading an article if they were reading with BeeLine turned on. See more on the CNET study in this article in The Atlantic [1].
• An optometric study using eye-tracking glasses found that BeeLine reduced the number of regressions and/or skipped lines for the vast majority of participants.
• Various educator-led studies (some more rigorous than others, and none as informal as the "case studies" that are unfortunately common in edtech) have found strong gains in reading fluency and/or comprehension.
I totally appreciate your skepticism, which is especially reasonable for someone who does not personally see a benefit from the tech.
The response from doctors who work in vision has been very positive, and our tools are recommended by doctors at Stanford Med School and UC Berkeley School of Optometry.
We even got featured by the American Optometric Association. The AOA committee that evaluated our technology said that this was the first time they'd ever had a unanimous vote in favor of anything. Apparently to them, it was quite obvious that this has beneficial effects (without having run any formal studies on it themselves).
To your point about colorblindness, I would note that I regularly come across people with red/green colorblindness who love/use our product. You can change the colors to be whatever you want, or use it in grayscale.
We are talking with publishers, but mostly on the digital side. Thanks for the input and questions!
1: http://www.theatlantic.com/technology/archive/2016/05/a-bett...
Just call what you do ML.
IBM, for one, never shied from calling any old BS "AI" and branding it Watson.
I indicated that it was actually quite effective without ML, and that it was easier to explain to users this way. They kept prodding around on the ML stuff, and how we might be able to use ML to accomplish roughly the same thing.
A week later they said that they were no longer interested because, although they liked what our tech was able to accomplish, it didn't fit with their investment thesis — which was all about ML.
My wife asked me why I didn't just make some stuff up and say we could do v2 using ML. Perhaps she was right.
1: http://www.beelinereader.com/individual
update: in response to feedback below, I edited the link to point to a page with relevant content instead of our generic landing page. Lesson learned!