tag:blogger.com,1999:blog-60316479615060054242024-02-19T05:15:59.160+02:00Lisp, the Universe and EverythingVsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-6031647961506005424.post-60612655310517670262017-01-02T18:46:00.000+02:002017-01-03T21:34:14.635+02:00(m8n)ware Open for Business<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzDjnR8EU3SDAiktvgvK21hLwdJmxlhKBM9VfdpVM32k9QyTpZiuAYyvGbqamJEQgLG8ToCsuFYXVk4D0M2zW0JQA1RpwJRweuenWLq0AnzJ3Aq0OOAmDzpOtrnc98BIkxHJtFtO4bWgo/s1600/logo.png" imageanchor="1" style="float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhzDjnR8EU3SDAiktvgvK21hLwdJmxlhKBM9VfdpVM32k9QyTpZiuAYyvGbqamJEQgLG8ToCsuFYXVk4D0M2zW0JQA1RpwJRweuenWLq0AnzJ3Aq0OOAmDzpOtrnc98BIkxHJtFtO4bWgo/s1600/logo.png" width="150" /></a></div>
<p>Today, I want to announce (m8n)ware (the name is an i18n-abbreviation of "meditationware" with a mix of Lisp parens). This is a thing I always wanted to build. After parting ways with Grammarly almost a year ago, I had some time to rest and think about my next move. And this thought I couldn't let go so I figured: you can always go work somewhere, but you don't have a lot of stabs at realizing some of your own ideas. Maybe, two or three in a lifetime. I had tried this once already with fin-ack almost 8 years ago, and the concept behind it was, basically, the same — the implementation differed.</p>
<h2>
<a id="user-content-in-theory" class="anchor" href="#in-theory" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>In theory</h2>
<p>What is (m8n)ware? It is a company aimed at solving problems in the area of cognition-related computing, which will be built as a distributed network of mostly Lisp research-oriented engineers. This sounds rather complex, so let me try to explain a couple of points:</p>
<ul>
<li>
<strong>Cognition-related computing</strong> is the best term I came to after a long thinking about the area of CS that includes various tasks related to cognition, intelligence, knowledge, and associated logic. The common marketing buzzword is Artificial Intelligence, but it has a negative history and is quite misleading. All computer programs implement some form of "artificial" intelligent behavior. The defining feature of cognition-related computing is that it requires some transformation of raw data into structured computer-processable information and back, which is similar to human cognitive functions that arguably do the same transformation for our own internal processing.</li>
<li>The zest of the <strong>distributed network</strong> notion is that the primary focus of (m8n)ware is building not a localized corporate-like structure, in which people are bound primarily by legal contracts and payment obligations, but a loosely coupled group of like-minded people, who share the same values, interests, and approaches to technology. This organization will be seeking a perfect middle-ground between a corporation and an open-source community.</li>
<li>
<strong>Research-oriented engineers</strong> is another "middle-ground" term that describes the main multidisciplinary role needed in this organization. We're not a scientific lab that is focused on fundamental research, neither are we an outsourcing shop that faithfully implements existing results according to a given spec. We're engineers in the sense that we deliver production-ready technology that may be useful to the end users in a straightforward manner. And, at the same time, we're researchers because we wield the methodology and are ready to experiment in new areas that don't have satisfactory state-of-the-art solutions.</li>
</ul>
<p>I don't believe in the VC mantra of "build a startup, get rich, change the world." First of all, I don't believe in changing the material world (which implies a conviction that you know better). I believe in changing yourself. Also, getting rich and doing something good (to the world) are not the goals that are always aligned. Moreover, they are usually in conflict. I'm not a businessman in the sense that money is not my ultimate goal. But I like to see things grow and develop, things that are bigger than myself. Thus I'm interested not in market share but in mind share.</p>
<p>Considering all of the above, (m8n)ware is not going to be a product company in a traditional sense. It will be a technology company that will create and disseminate knowledge-based services and products. Also, it will not aim at rapid growth, but rather at sustainable development.</p>
<p>In the <a href="http://lisp-univ-etc.blogspot.com/2016/09/the-technology-company-case.html">previous post</a>, I've listed my motivations for moving in this particular direction and explained my values. I can't say that it got overwhelmingly positive feedback, but, in general, the results were better than I expected :) Now, I have several clues on how to cross the most challenging chasm of scaling its operation from a single-person endeavor to a productive and sustainable group. Meanwhile, I was testing if this approach may work in practice and doing market research. Now, I'm ready to go all in and devote at least the next year of my professional life to building this thing.</p>
<p><a href="http://m8nware.com">http://m8nware.com</a> is oficially live. If you're interested in cooperation as a client, partner or co-worker, please, let me know...</p>
<h2>
<a id="user-content-in-practice" class="anchor" href="#in-practice" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>In practice</h2>
<p>There are several aspects that I'm betting on in (m8n)ware that are non-mainstream and may seem somewhat counter-intuitive. In this part, I want to provide more details about why and how, I think, this will work.</p>
<p>The first aspect is radical transparency. From this and the previous post, it should be clear that (m8n)ware originated and plans to continue functioning fully exposed to the outside world, not relying on any secret know-hows or clever tricks. I don't plan to conceal what we're going to do and why. Neither "fake it till you make it." Why this will work? First of all, my experience shows that in the current age of information overload, we're fighting primarily not for the purse but for the thoughts of our "customers" (in many possible markets: not only where you sell your product/services, but also in the labor market, and in the ecosystem of potential competitors, partners, and vendors). And this requires information sharing, not safekeeping and concealment. Secondly, in general, I'm not interested in competition — rather I'd like to find a unique niche in the market that will be served by the company in the best possible manner and will be big enough to sustain it. The good news is that the AI-market is, currently, growing very fast and this trend will last at least for a couple more years. So demand is greater than supply, and this means not a very harsh competitive environment. Another thing is Lisp: no one in their right mind will bet a company on Lisp, so I'm not really worried about the competition in the labor market. :) The final point about openness is that I personally endorse it, and as this is the company that aims to be as close to my ideal as possible it should endorse it.</p>
<p>Although it's not a classic product company, it's not going to be a typical outsourcing one either. Yes, initially, it will provide primarily consulting services, but the idea is that, with time, the share of these services will decrease in favor of supporting more general-purpose tools and technology developed in-house. And to ensure the constant priority of this goal, we'll be doing such work from day one. Currently, I see it in the following manner: the time of all engineers will be split in some proportion between for-pay consulting and developing open-source/research projects for free, and with time as some of these projects become important to the company, it will start paying the people who develop them for this work as well. This is a frugal approach, but I advocate it based on personal perspective: working at my previous gigs, I'd be eager to forfeit, say, 20% of my salary to be able to spend 20% of my time on open-source projects that matter to me personally. Actually, the percentage may be much bigger. Currently, I spend 50% of my time working on such projects and am quite happy with this. I deeply believe that such balance is more appealing to many programmers (especially, the kind of people I'd be willing to cooperate with) than a conventional approach.</p>
<p>Lisp again. From my experience working in cognitive problems domain, I can definitely say that it's not about coding. For several reasons. The obvious one is that 80% of resources are spent in other parts: thinking/learning, working with data, experiments, documentation. (The remaining 20% are still critical, especially since most of the solutions are resource-demanding and the code is algorithm-heavy). Then, current technology situation: the days of backend-only solutions are, unfortunately, gone. A lot of problems require heavy mobile or in-browser presence. And on the backend, thanks to microservices and other stuff, no one is developing in a single language and even on a single platform anymore. Finally, there's knowledge transfer. Programs may be not a bad medium to express concepts, but not the optimal one either: between scientific papers, blog posts, markdown documents, experiment notebooks, and production-optimized programs, there is no one-size-fits-all solution. All this creates conditions, in which the choice of a programming language becomes much less a constraint than it was just a a few years ago. On the other side, from the point of view of "internal" productivity (not concerned with integration into the bigger picture), Lisp has proven to be a great and rewarding environment very well suited for research work. Plus a great way to differentiate in the labor market... :)</p>
<h2>
<a id="user-content-our-value-proposition" class="anchor" href="#our-value-proposition" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Our value proposition</h2>
<p>So, if you need to solve some cognitive computing problems, here's what (m8n)ware may offer.</p>
<ol>
<li>Provide small-scale consulting services: talk to your people and help them with their challenges, perform an audit, study feasibility of some serious project, help with gathering relevant data sets, etc.</li>
<li>Develop a prototype solution for a particular problem, and deliver it as a set of data, documentation, and a working web-service to allow integration in your prototypes, testing in your environment, with your clients and data.</li>
<li>Develop a turnkey solution and integrate it into your environment. This is rather tricky as we'll prefer to work in Lisp, and not every environment will be ready for this. We're also willing to compromise and develop some non-critical integration parts in other languages when necessary, provided that the core remains Lisp-based. </li>
</ol>
<p>Why you should go to us instead of solving the problem on your own? The current situation in cognition-related computing is that such projects have high business value, but are not easy to complete: they require not just engineering, but a substantial/prevailing research component. Productive work in this area assumes a skill set of developers and managers that is different from conventional software development. Obviously, you want to develop this expertise in-house, but growing it, currently, is a slow and daunting process. Still, you should definitely do that for the long-term benefits, but this doesn't mean that you can say something in the lines of: in the next half a year I need to solve this complex AI problem, so I'll just hire a person/team in a couple of months and let them do it. It's a risky approach even in conventional software development, and in this field, it just doesn't work. The competition for AI researchers is insane and, moreover, if you're a regular company and not Google/Facebook or the latest-hottest startup your chances of hiring and retaining top talent are, basically, nil. (Why we'll be able to have the talented people while you won't? Because our particular focus — Cognitive+Tech+Distiributed+Lisp — will allow us to appeal to a portion of the talent pool that is not happy in mainstream environments).</p>
<p>Cognitive computing projects are risky and hard to predict. That's why for any serious long-term (longer than a couple of months) partnership we'll be dividing the work into reasonable chunks that will allow you to get at least part of the value at each checkpoint, see and assess progress, and pivot if your plans or conditions change.</p>
<p>We're open for business — write to <a href="mailto:info@m8nware.com">info@m8nware.com</a> if interested.</p>Vsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.com0tag:blogger.com,1999:blog-6031647961506005424.post-64159922753682206292016-12-28T13:16:00.003+02:002016-12-29T22:27:54.907+02:005 Steps to Grasping Modern ML<img src="https://imgs.xkcd.com/comics/progeny.png" width="200">
<p>Recently, I've been teaching an advanced Algorithms course, which concluded in a short introduction to Machine Learning. Obviously, ML is its own track in Computer Science curriculum, but, nevertheless, there's a substantial overlap between these 2 disciplines: algorithms and ML. However, ML adds another dimension that is not usually considered in the world of algorithmic thinking.</p>
<p>Anyhow, this experience helped me formulate the minimal selection of concepts that need to be grasped in order to start practical ML work. An ML crash course so to say.</p>
<p>As I've never seen such compilation, I'd like to share it in this post. Here are the 5 essential steps to understanding </p>
<h2>
<a id="user-content-step-1-understanding-the-ml-problem-formulation-knn-algorithm" class="anchor" href="#step-1-understanding-the-ml-problem-formulation-knn-algorithm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Step 1. Understanding the ML problem formulation. kNN algorithm</h2>
<p>The first thing one needs to realize is the difference between an ML problem and the common programming problems. Here training/test data and an objective function should be explained alongside with the 3 common "learning" approaches: supervised, unsupervised, and reinforcement. A widely used and good initial examples is the <a href="https://archive.ics.uci.edu/ml/datasets/Iris">Iris data set</a> and kNN algorithm.</p>
<h2>
<a id="user-content-step-2-adding-features-and-iterative-training-into-the-picture-perceptron-algorithm" class="anchor" href="#step-2-adding-features-and-iterative-training-into-the-picture-perceptron-algorithm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Step 2. Adding features and iterative training into the picture. Perceptron algorithm</h2>
<p>The second step is introduction of the concept of feature extraction that allows approaching a problem from different angles. The Iris data set already has features, but initially they may be perceived as given. Iterative training is another common ML approach (although some popular algorithms like kNN or Decision Trees don't rely upon it). Perceptron is the simplest algorithm to explain (which still remains in practical use) and leads nicely to the next step.</p>
<p>A good example task and data set for this part is the <a href="https://archive.org/details/BrownCorpus">Brown Corpus</a> and the problem of POS tagging. And there's a <a href="https://explosion.ai/blog/part-of-speech-pos-tagger-in-python">great post</a> outlining its soultion by Matthew Honnibal.</p>
<h2>
<a id="user-content-step-3-continuous-vs-discrete-learning-gradient-descent-softmax-algorithm" class="anchor" href="#step-3-continuous-vs-discrete-learning-gradient-descent-softmax-algorithm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Step 3. Continuous vs discrete learning, gradient descent. Softmax algorithm</h2>
<p>The obvious next step is transitioning from a discrete perceptron learning to continuous gradient descent used in Logistic regression. Andrew Ng provides a lucid connection in Part II & III of his <a href="https://datajobs.com/data-science-repo/Generalized-Linear-Models-%5BAndrew-Ng%5D.pdf">tutorial on Linear Models</a>. It also helps that Logistic regression and Softmax are the basic building blocks of Neural Networks that are to be discussed next. The example task for this problem may remain the same POS tagging, although others, like the ones used by Andrew, may be also utilized.</p>
<h2>
<a id="user-content-step-4-learning-graphs-aka-neural-nets-backprop-feed-forward-neural-network-algorithm" class="anchor" href="#step-4-learning-graphs-aka-neural-nets-backprop-feed-forward-neural-network-algorithm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Step 4. Learning graphs (aka neural nets), backprop. Feed-forward Neural Network algorithm</h2>
<p>As soon as we understand gradient descent and logistic regression, it's rather easy to make the next step to forming layers of such blocks to allow the combined model to "learn" higher-level feature representations. This is where the Backprop algorithm for efficient training comes into play (that is, by the way, another example of a dynamic programming algorithm). Also in this part, it's possible to talk about vector representations of words and other highly contextualized objects (landmark position in image, etc.) A great explanation of Backprop is presented in <a href="http://colah.github.io/posts/2015-08-Backprop/">this post</a> of Christopher Olah. Also, a good exaple data set here is the <a href="http://yann.lecun.com/exdb/mnist/">MNIST</a>.</p>
<h2>
<a id="user-content-step-5-bias-variance-tradeoff-regularization--ensembles-random-forest-algorithm" class="anchor" href="#step-5-bias-variance-tradeoff-regularization--ensembles-random-forest-algorithm" aria-hidden="true"><span aria-hidden="true" class="octicon octicon-link"></span></a>Step 5. Bias-variance tradeoff, regularization & ensembles. Random Forest algorithm</h2>
<p>Finally, we should return to the beginning and revisit the learning problem, but with some practical experience already under our belt. This is where the essential bias-variance tradeoff and common ways to tackle it should be discussed: regularization and ensembles. It's also a good place to introduce the Decision Tree algorithms and the ensemble methods based upon them (Random Forest and, maybe, others) as one of the most widely-used current practical approach.</p>
<p><a href="https://jvns.ca/blog/2016/01/02/winning-the-bias-variance-tradeoff/">"Winning the Bias-Variance Tradeoff"</a> by Juila Evans may be a good introductory text on this.</p>
<p>Overall, due to the highly condensed nature of such presentation, a lot of important things will be almost not covered. For example, unsupervised learning, CV with its convolutions, sequence models. However, I believe that with the obtained knowledge and conceptual understand of the mentioned basis those parts may be grasped quite easily.</p>
<p>If this plan turns out helpful to you or some essential improvements are necessary, please, leave your thoughts and comments...</p>
Vsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.com0tag:blogger.com,1999:blog-6031647961506005424.post-27536105030371741532016-09-23T21:42:00.003+03:002016-09-23T21:44:38.332+03:00The Technology Company Case<h2>What's a Technology Company?</h2>
<p>
I'm a programmer. Obviously, this means that I have to earn money and realize my talents working for some company that employs programmers (or on my own). It's worth noting that there are several kinds of such companies.
</p><p>
One is traditional enterprises, like banks or government agencies, that need programmers to automate their processes and improve output. Every company needs an accountant, and, likewise, nowadays every needs a programmer.
</p><p>
There are also companies that provide software development and related services - the so-called consulting or outsourcing firms. They employ programmers to automate the work and improve the output of, mainly, the first breed of companies.
</p><p>
Then, there are also technology product companies, like Instagram or Apple, that employ engineers to build their products, services or media, which are then consumed by ordinary people.
</p><p>
Finally, there are truly technology companies that produce new technology that is used by all the previous three groups, as well as by the technology companies themselves. From the business standpoint, this technology may be supplied either in the form of on-the-spot consulting work, licensing or even separate products.
</p><p>
Every group has some percentage of technology work in its operation. This work, often called R&D, comprises of implementation of existing technology (D in R&D) and creation of the new one (R). The share of the two differs substantially between the groups. The companies from the first one may be 1 to 10% dependent on R&D work and have almost 0% of R in it, the second group is 90% R&D work, still, with mere percents of R in it, the third group is just 30-50% R&D, and the share of R in it may rise to 10-20% but rarely more, and the last group should have 90% R&D with >50% R in it.
</p><p>
A technology company should be a thought leader in its sphere. This means not chasing fashions in our pop-culture-like industry but setting an example justified by technological excellence instead of marketing. This means building something that will last and have an impact for a substantially longer period of time than the ever-accelerating hype cycle. This means having an ultimate goal of solving hard technical problems and not chasing profits or market share. While product companies try to change the world by producing their innovative products that merely use technology, a technology company does that by producing technology that enables more innovative products. A closed vs an open approach.
</p>
<h2>10x Programmers</h2>
<p>
There's this popular meme of 10x programmers that constantly spurs discussion and flamewars among our peers. Is it just fad, who are those 10xers, do they really exist?
</p><p>
Let's first consider this question from the perspective of other crafts and professions. Are there 10x painters? Well, if we compare painter productivity by the number of pieces drawn it would be hard to tell. But if you think about price, clearly, there are even 1000x ones: an ordinary painter's work may cost $1000, and a famous masterpiece will be in the millions. If we consider the number of people reached the same rule applies: maybe, thousands will see quality works of a common professional painter, and millions or even billions - the works of a master. But you may say that painting, unlike programming, is an art. What about carpentry? Well, I'd compare with professions that require mostly intellectual work. Are there 100x doctors? Surely, there are those who saved 100x more people by inventing a new operation method or treatment. Lawyers? A person who writes a law impacts orders of magnitude more than an ordinary counselor at some random firm. This list may be continued on and on.
</p><p>
I've compiled a book called "<a href="https://leanpub.com/lisphackers">Interviews with 100x programmers</a>". To some extent, the name was an exaggeration. But, as they say, every joke has some truth in it. In fact, I fully subscribe to the 10x programmer concept. Moreover, I consider that there are not only 10x ones but also 100x, 1000x... Definitely, there are hardly any 10x coders, i.e. people who produce 10x the amount of code a good professional programmer will create in the same timeframe. But there's much more to programming than merely writing program code.
</p><p>
To be an order of magnitude more productive means to solve problems an order of magnitude more complex than the ones considered accessible at a given point in time. Obviously, such problems exist, and there will, probably, always be an unlimited supply of them. Also, it should be clear from the short history of computing that there are some people capable of bringing a new perspective, coming up with approaches that allow solving such problems either in a much better way or just solve them at all. As Alan Kay, who's for sure one of such 100x programmers, has famously said: "A change in perspective is worth 80 IQ points."
</p><p>
Still, there's more to it than just solving harder problems. Another popular explanation given to the 10x thing is that such a programmer is the one who makes 10 other programmers 2x more productive. This, from my point of view, implies the one who is showing a better approach, in other words, a thought leader, and the one who implements this vision in some technology that other programmers use. In fact, we're productive in our work at our current level mostly thanks to such prolific programmers: every day I use Unix, Emacs, Lisp, git and other tools that were initially conceived and built by a handful of the 10x programmers. Their vision and impulse made thousands and even millions more productive.
</p><p>
Those 10x programmers are the ones I'd like to be around at work. And so, my ideal company is the one that attracts such persons. And although a significant percent of such people are loners, most of them are also highly motivated by the presence of similar colleagues.
</p><p>
So which one of the 4 company types mentioned above will such people choose?
</p><p>
The first one is mostly out of consideration because in it the programmers are not the primary value creators - on the contrary, often they are considered a cost center. I.e. they are just another service function similar to an accountant or a janitor. Surely, there are exceptions to this rule when the company leaders realize the potential that technology change bears to their company, which, basically, means that the firm is transitioning to type 3. Even in such case, it's still a much less productive environment than a type 3 firm built with the right principles in mind from the start.
</p><p>
What about outsourcing companies? Their advantage is that programmers are their primary asset, which means that the company will be built around them, have a substantial number of them and will do a lot to attract and hold prominent people. The nature of work, unfortunately, is usually a severely limiting factor here. First of all, in most of the cases, the customer doesn't really care about the technological excellence or innovative nature of the result. The projects are in most of the cases counter-innovative, i.e. the more mundane, reproducible, and ordinary the technological solution that achieves the desired result is the better. And it's quite reasonable from the business standpoint: innovation is risky. This means that, ultimately, such companies reward uniformity and interchangeability of their stuff and their output, especially, since it's much easier to manage and scale. Have I mentioned that managing programmers is very hard (the common metaphor used is "herding cats")?
</p><p>
Now, let's look at product companies. Are they a heaven for 10x programmers? Well, a lot of such people flock there. One reason is that such companies understand the need for talented programmers because unlike the previous 2 types they may and should face unique technological challenges, and, moreover, their leadership is able to recognize that (type 1 companies also face those challenges, but usually they just don't view them from the technology standpoint). Yet, a product company is only X% new technology and another (100-X)% other things. What is the value of X? Maybe, it's 20-30% at Google or Facebook, and even less at smaller companies with fewer resources. Why? Because, as we discussed above, the ultimate goal of most of such companies is making money by serving masses of customers. This requires huge marketing, sales, operations, and support "vehicles" that employ professionals to operate and programmers to build, maintain and develop. But have quite little interesting technical challenges. Once again, this is the right thing from the business standpoint, especially if you have to earn more and more money each year and grow your market share. But focus on earnings and market share means that technological excellence becomes secondary. Surely, the best of the leaders and managers realize its importance, but they have to make many trade-offs all the time.
</p><p>
That's why I have singled out "pure" technology companies. Such organizations are naturally inclined to make tech excellency their focus. There are, surely, counterexamples that are infected with the Silicon Valley "growth virus" and try to win the market as fast as possible with marketing, but it doesn't mean that it always has to work that way. In my opinion, purely technological companies are the best place for 10x programmers because they will not merely utilize their work to some other end goal but have vested interest in amplifying its influence. They are not inclined to conceal the know-hows and innovations as trade secrets, but will benefit from sharing and promoting them. They may also provide maximum freedom of choice: of approaches, tools, supporting technologies, because their primary concern is not effective scaling of the same ultimately repetitive work to many similar programmers but creating breakthroughs. Their dependence on such ultra-productive programmers is existential.
</p><p>
I don't consider myself to be a 10x programmer, but, surely, I'd like to reach such level someday and I also aspire to work alongside them.
</p>
<h2>A Company I'd Build</h2>
<p>
All in all, being part of a technology company seems like the best choice for me both in terms of potential impact and possibilities to have 10x programmer colleagues. Eventually, either you have to join one or create one yourself. For the last 5 years, I've been working in the so-called AI, and my experience both from product company side and individual consultant work shows that demand for research-related technology expertise here is growing much faster than the supply. I see it as a chance for new technology companies to emerge and gather those few capable people in this field to amplify their impact. So I'm seriously considering starting a technology company, and I'm looking for like-minded people who share my values and vision to join our forces.
</p><p>
If I were to start such company, I'd build its foundation on a few things that really matter to me personally. Some principles or, as they used to call them, values. Unfortunately, the notion of "values" has somewhat lost its original meaning in the corporate world. When you see such qualities as effectiveness or adaptability cast as values that's a sign of such misconception. Values are something that you don't compromise upon at all. Surely, it's pointless to compromise any parts of your professionalism (such as effectiveness), so professionalism is a default value not even worth discussing. Real "values", however, are those aspects of your work culture that run a real risk of conflicting with the things that are considered universally important. In business, those are profits, market share, favorable competitive position. So, being true to your values means not forfeiting them even if you're going to lose in those basic areas.
</p></p>
Here is a list of the values that I subscribe to:
</p><ul>
<li><b>Technological excellence</b> should be a basic trait of any technology company. For me, an example of applying such value would be using Lisp as a starting point for most of the solutions despite the fact that the language is quite unpopular and underappreciated - my personal experience shows that it works very well, especially in the fields that are heavily knowledge-based. Another example is that in a technology company literally everyone should be technology-savvy: even the office manager should be programming at times.
<li><b>Personalism</b> is the main quality that a company has to support in its dealings with all the people it's interacting with: employees, customers, contractors and providers. This means, for example, striving to provide flexible and productive working conditions to each employee instead of trying to fit everyone in the same conditions (because management is hard). Overall, lack of management competency should never become a limiting factor. One manifestation of this is that a modern technology company should be built as a distributed organization from day 1.
<li><b>Ahimsa</b> is an ancient word meaning not harming anyone. It is a little bit more than our modern-day ethics, but it's worth it. Why create something if you know that it will cause misery and suffering to others? In effect, this means, for example, refusal to provide services to companies that are clearly unethical.
<li><b>Radical openness</b>. As they say, "information wants to be free." :) Maximal sharing and minimal secrecy makes so many things much simpler. And in our lowest-common-denominator technology world, ultimately, the risk of competitors copying and abusing your work is much less than that of brilliant people not joining your cause because they just haven't heard of it.
</ul>
<p>
So... If you're interested in solving complex AI challenges out of whatever part of the world you're living in, working with 10x programmers, using Lisp and other advanced technologies in the process - drop me a line, I'd be glad to chat.<br><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgr22aCqQU1OX_77CgBXAoYbU_Pal8JdlurTbsrnbajpyO1GkraOX-wes1RtgcNdA-zySiq2j6g8PMDcy2W8tItScwU2_on4w5L3gzFqUKOicAs_LBXr1C6eCCBg3KmdGCblSnZmO_zzAo/s320/m8n.png"></p>Vsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.com0tag:blogger.com,1999:blog-6031647961506005424.post-80652967093595503032012-07-12T23:04:00.002+03:002012-07-12T23:15:38.027+03:00Alan Kay about keeping history in CSSo true and very well explains my motives behind "Lisp Hackers":<br /><br /><blockquote>Binstock: You once referred to computing as pop culture.<br /><br />Kay: It is. Complete pop culture. I'm not against pop culture. Developed music, for instance, needs a pop culture. There's a tendency to over-develop. Brahms and Dvorak needed gypsy music badly by the end of the 19th century. The big problem with our culture is that it's being dominated, because the electronic media we have is so much better suited for transmitting pop-culture content than it is for high-culture content. I consider jazz to be a developed part of high culture. Anything that's been worked on and developed and you [can] go to the next couple levels.<br /><br />Binstock: One thing about jazz aficionados is that they take deep pleasure in knowing the history of jazz.<br /><br />Kay: Yes! Classical music is like that, too. But pop culture holds a disdain for history. Pop culture is all about identity and feeling like you're participating. It has nothing to do with cooperation, the past or the future — it's living in the present. I think the same is true of most people who write code for money. They have no idea where [their culture came from] — and the Internet was done so well that most people think of it as a natural resource like the Pacific Ocean, rather than something that was man-made. When was the last time a technology with a scale like that was so error-free? The Web, in comparison, is a joke. The Web was done by amateurs.</blockquote><br />-- <a href="http://www.drdobbs.com/architecture-and-design/interview-with-alan-kay/240003442">Dr.Dobbs «Interview with Alan Kay»</a>Vsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.com0tag:blogger.com,1999:blog-6031647961506005424.post-62257096052007710452009-02-01T11:54:00.002+02:002009-02-01T11:56:41.006+02:00Nokia Locate SensorFrom idea:<br /><blockquote>Received: by 10.141.28.2 with HTTP; Mon, 22 Oct 2007 05:25:31 -0700 (PDT)<br />Message-ID: <89dc7c5b0710220525p4e33dd50i6cb85d85cc1b0903@mail.gmail.com><br />Date: Mon, 22 Oct 2007 15:25:31 +0300<br />From: Vsevolod <vseloved@gmail.com><br />To: info@janchipchase.com<br />Subject: idea to help not forget phones<br />MIME-Version: 1.0<br />Content-Type: multipart/alternative;<br />boundary="----=_Part_8479_1626797.1193055931431"<br />Delivered-To: vseloved@gmail.com<br /><br />------=_Part_8479_1626797.1193055931431<br />Content-Type: text/plain; charset=ISO-8859-1<br />Content-Transfer-Encoding: 7bit<br />Content-Disposition: inline<br /><br />Hello Jan,<br /><br />My name is Vsevolod Dyomkin. I wanted to share a design idea related to<br />mobile phones with you, as an only person I can presently reach, who can<br />possibly facilitate its implementation.<br /><br />Today I have seen your presentation at TED.com concerning the uses of mobile<br />phones. What interested me in it is the mention of places, which serve as<br />gravity centers for important carried items. In my opinion, the existence of<br />such places, in spite of the natural need for them, brings about one big<br />potential inconvenience…<br /><br />Take my example, I work inside a big building and at my workplace the level<br />of mobile signal is very poor, so I'm forced to leave my phone in the other<br />part of the room where the connectivity is better. This sometimes leads to<br />unpleasant situations when I forget the phone, leaving the room and<br />building. Other example can be, when a person comes to a party and leaves<br />her phone in some place not to be distracted by it. Afterwards he'll pretty<br />probably forget about it. This may not only be the case with phones, but<br />also with other carried items as well.<br /><br />To prevent such situations I've come up with the following idea: to have a<br />small device which informs you (like beeps), when you part, for example, 10<br />meters from the item. It will consist of two parts — a number of RFID tags<br />(in a form-factor of small round colored stickers), which can be sticked to<br />a mobile phone, a key, an id card etc. and a receiver/speaker, which can be<br />a charm on a keyring or a bangle, which beeps. The receiver can optionally<br />show the color of a sticker, which caused an alarm.<br /><br />To me this is an example of delegation of mundane/error-prone tasks to<br />technology :-) — in this case the delegation of the necessity to flap one's<br />pockets...<br /><br />If you find it interesting, fell free to contact me<br />Best regards<br />Vsevolod</blockquote><br />...to <a href="http://www.electricpig.co.uk/2009/01/09/nokia-locate-sensor-never-lose-anything-again/">implementation </a>Vsevolod Dyomkinhttp://www.blogger.com/profile/07729454371491530027noreply@blogger.com0