In this position paper, we describe our vision of the future of machine-based programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software. The Three Pillars of Machine-Based Programming

There are many fields under the umbrella of the data science and sometimes these roles look similar to each other or are used interchangeably. Let us list these terms first and try to understand them

A couple of months ago I taught an introduction to statistical learning theory. I took inspiration from two very good introductory books on SLT: “Foundations of ML”, and “Understanding Machine Learning: From Theory to Algorithms”. I also covered some classical results about nearest neighbors from the seminal book “A Probabilistic Theory of Pattern Recognition”. The lectures were recorded and I put the videos on the youtube channel. Sadly I still did not learn the basic lesson to stick to a black pen when teaching on a whiteboard, so at (many) times it is hard to read what I write. I also got sick after the first lecture so my voice is quite weak in the following lectures, making it hard to hear too… Despite these fatal flaws some people still found the material useful, so check it out if you are interested. Below I give a rough syllabus for the lectures, and I also give some notes on “algorithmic generalization” which was in my opinion the highlight of the lectures (you can also check my earlier notes on SLT here and here).

Computational Theory of Mind In philosophy, a computational theory of mind names a view that the human mind or the human brain (or both) is an information processing system and that thinking is a form of computing. The theory was proposed in its modern form by Hilary Putnam in 1961, and developed by the MIT philosopher and cognitive scientist (and Putnam’s PhD student) Jerry Fodor in the 1960s, 1970s and 1980s. Despite being vigorously disputed in analytic philosophy in the 1990s (due to work by Putnam himself, John Searle, and others), the view is common in modern cognitive psychology and is presumed by many theorists of evolutionary psychology; in the 2000s and 2010s the view has resurfaced in analytic philosophy (Scheutz 2003, Edelman 2008). The computational theory of mind holds that the mind is a computation that arises from the brain acting as a computing machine. The theory can be elaborated in many ways, the most popular of which is that the brain is a computer and the mind is the result of the program that the brain runs. A program is the finite description of an algorithm or effective procedure, which prescribes a deterministic sequence of discrete actions that produces outputs based only on inputs and the internal states (memory) of the computing machine. For any admissible input, algorithms terminate in a finite number of steps. So the computational theory of mind is the claim that the mind is a computation of a machine (the brain) that derives output representations of the world from input representations and internal memory in a deterministic (non-random) way that is consistent with the theory of computation. Computational theories of mind are often said to require mental representation because ‘input’ into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object, but must interpret and represent the object in some form and then compute the representation. The computational theory of mind is related to the representational theory of mind in that they both require that mental states are representations. However the two theories differ in that the representational theory claims that all mental states are representations while the computational theory leaves open that certain mental states, such as pain or depression, may not be representational and therefore may not be suitable for a computational treatment. These non-representational mental states are known as qualia. In Fodor’s original views, the computational theory of mind is also related to the language of thought. The language of thought theory allows the mind to process more complex representations with the help of semantics. …

When I was out for a walk recently, I heard a loud low-flying aircraft passing overhead. This was not unusual since we live in the flight path of planes landing at a major international airport about 10 miles from our home. In this case, I thought to myself that the sound seemed more directly overhead and lower than normal as well as being suggestive of a larger than average jet aircraft.

Amazon Rekognition is a deep-learning-based image and video analysis service that can identify objects, people, text, scenes, and activities, as well as detect any inappropriate content. Using the new Amazon Rekognition face filtering feature, you can now have control over the quality and quantity of faces you can index for face recognition. This saves on cost, reduces development time, and improves face recognition accuracy.

On the heels of the release of Dataiku 5.0, we’re delighted to share yet another exciting development: Dataiku has been named a strong performer in The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning Solutions, Q3 2018.

In this paper, we present a survey of ‘on-disk’ data processing (ODDP). ODDP, which is a form of near-data processing, refers to the computing arrangement where the secondary storage drives have the data processing capability. Proposed ODDP schemes vary widely in terms of the data processing capability, target applications, architecture and the kind of storage drive employed. Some ODDP schemes provide only a specific but heavily used operation like sort whereas some provide a full range of operations. Recently, with the advent of Solid State Drives, powerful and extensive ODDP solutions have been proposed. In this paper, we present a thorough review of architectures developed for different on-disk processing approaches along with current and future challenges and also identify the future directions which ODDP can take. On-Disk Data Processing: Issues and Future Directions

I had a great time at the EARL Conference in London last week, and as always came away invigorated by all of the applications of R that were presented there. I’ll do a full writeup of the conference later this week, but in the meantime I wanted to share the materials from my own presentation there, “Not Hotdog: Image Recognition with R and the Custom Vision API”. I’ve embedded the slides below:

Multi-hop assortativities for networks classification