Nine Post-Moore Technologies
Data Science Association blog
by michaelmalak
2y ago
The original literal Moore's Law was just that the number of transistors would double every two years. Physics limits that so the continuation of progress requires thinking outside the box. Below is a list of technologies that can help that: Near-term 3D chips NVRAM as main computer memory Medium term Optical computing Quantum computing Neuromorphic/analog computing Alternative RAM Memristor Phase-change memory (PCM) Spin-transfer torque (STT) Magneto-resistive Non-silicon substrates Gallium arsenide Silicon germanium Carbon nanotubes Graphene Homogenization/deep integration of compu ..read more
Visit website
Are You a Complete Data Scientist?
Data Science Association blog
by michaelmalak
2y ago
The Washington Post reported that scientists discovered skipping breakfast leads to weight loss after all, not to weight gain as previously believed. What lead scientists astray previously was relying on observational studies, a.k.a. Quasi-Experimental Design. Only a randomized trial, the "gold standard", can establish causality. Designing and conducting a randomized trial is extremely expensive, and many data scientists have not had the privilege of being involved in one. But by relying on already-collected data, which incidentally often falls in the category of Big Data, data scientis ..read more
Visit website
Neuromorphic vs. Neural Net
Data Science Association blog
by michaelmalak
2y ago
Brain Artificial Neural Network Asynchronous Global synchronous clock Stochastic Deterministic Shaped waves Scalar values Storage and compute synonymous Storage and compute separate Training is a Mystery Backpropagation Adaptive network topology Fixed network Cycles in topology Cycle-free topology The diagram of biological brain waves comes from med.utah.edu and the diagram of an artificial neural network neuron comes from hemming.se The table above lists the differences between a regular artificial neural network (feed-forward non-spiking, to be specific) and a biologi ..read more
Visit website
Four Approaches to Artificial General Intelligence
Data Science Association blog
by michaelmalak
2y ago
Artificial General Intelligence (AGI), also known as Strong AI, distinguishes itself from the more general term AI by specifically having its a goal human-level or greater intelligence. There have been many attempts to achieve it. Pei Wang's page Artificial General Intelligence -- A Gentle Introduction has the best curated set of links. Various people have categorized the numerous approaches in various ways, but here is my categorization: Neural The neural approaches, sometimes called "connectionist", try to imitate the human brain in someway. 1. Neuroscience The neural approaches inspired b ..read more
Visit website
Business Rules: From JRules to Data Mining
Data Science Association blog
by michaelmalak
3y ago
Remember the "business rules" craze of the early 2000s? They were popular especially with mortgage lenders. An example ILOG JRules decision table for mortgage lending. Although sometimes imbued with some kind of magic reasoning (e.g. backtracking), business rules engines were really just simplistic decision trees under the covers. The real main advantage of a business rules engine at the time was that it exposed business logic in a form digestable and specifiable by business analysts and decision makers, as opposed to locking it up in Java or C++. But now we are in the era of machine learnin ..read more
Visit website
Causality
Data Science Association blog
by michaelmalak
3y ago
No doubt you've encountered the image below from Gizmodo in some PowerPoint somewhere this year. But that same PowerPoint likely didn't bother to answer the next logical question: How to get to causality? It's not an easy question to answer. Having really, really good correlation is definitely not the answer. First a couple of counterexamples. Common ancestral cause Putting aside spurious correlations such as the one above, the much more common scenario is that of a common cause, such as shown below. Finding the correlation of "street wet" and "hair wet" in some data set does not lead to the ..read more
Visit website
Hypothesis Formation
Data Science Association blog
by michaelmalak
3y ago
Somewhat of a sequel to my earlier post on causality, where do hypotheses come from? The ideal hypothesis: Has basis in a reasonable engineering, physical, or economic, etc. model. Is as simple as can be in terms of number of variables. I.e. Occam's Razor has been applied. Either has been vetted against a number of other hypotheses and selected as the most reasonable, or will be tested along with other reasonable hypotheses. Will be tested in the gold standard, the randomized controlled experiment. Is actionable. Real life is not ideal, so below I discuss compromises and trade-offs inv ..read more
Visit website
QED: Controlling for Confounders
Data Science Association blog
by michaelmalak
3y ago
We see it all the time when reading scientific papers, "controlling for confounding variables," but how do they do it? The term "quasi-experimental design" is unknown even to many who today call themselves "data scientists." College curricula exacerbate the matter by dictating that probability be learned before statistics, yet this simple concept from statistics requires no probability background, and would help many to understand and produce scientific and data science results. As discussed previously, a controlled randomized experiment from scratch is the "gold standard". The reason is beca ..read more
Visit website
17 Qualities of the Ideal Recommender System
Data Science Association blog
by michaelmalak
3y ago
When constructing a recommender system and selecting algorithms, there is more to consider than just "accuracy". The most "accurate" recommender system would recommend the same items (whether those "items" are books, websites, options available to a software end user, etc.) over and over again, focused on a narrow topic area, and ignorant of context. Below are features of various recommender systems that, if combined, would perhaps form the ideal recommender system to produce "useful" rather than "accurate" results. However, in reality, some of these features are at odds with one another, suc ..read more
Visit website
Cognitive Bias in Data Science
Data Science Association blog
by michaelmalak
3y ago
A Business Insider infographic 20 cognitive biases that screw up your decisions went viral this past week. Each and every one of those 20 biases can negatively affect data scientists in their work: Ancorhing bias. A data scientist might find an interesting result in early exploration, and ignore other possible results or worse, ignore conflicting information. Availability heuristic. As I blogged in If All Your Data is Big Data, You May Not Be a Complete Data Scientist, data scientists all too often rely only on pre-collected data and do not conduct randomized controlled experiments of their ..read more
Visit website

Follow Data Science Association blog on FeedSpot

Continue with Google
Continue with Apple
OR