Blogs

Ownership and Control of Algorithm Source Codes

Professional data scientists understand that algorithm source codes is the secret sauce to significant competitive advantages, better public policy and massive profits. Ownership and control of the algorithm source code is of critical importance.

Data scientists who work as an employee directly for an employer concede legal ownership and control of any and all work products to the employer.

The Swarm and Network-edge Technology

A swarm is a number of phenomena massed together in motion in space or time. One technical definition is a group of independent agents interacting with one another and with their environment. Natural examples include a flock of birds, colony of ants and school of fish. There is a unique type of intelligence from the swarm resulting from the collective behavior of decentralized, and self determined and organized systems.

AI is Beautiful: Careful with that Blackbox Eugene

Professional data scientists frequently use artificial intelligence (AI) to help achieve goals. AI adds significant value when used properly. Building AI into data science processes is awesome fun and sometimes mind blowing.

Every year and every month AI gets better, stronger and smarter. Sometimes AI will exhibit unique conceptual frameworks that humans would not think of - adding massive competitive advantage when applied to a myriad of processes.

AI can be a beautiful thing.

​​Confirmation Bias: Check and Balance Processes

COVID19 has exposed confirmation biases among some expert scientists. Confirmation bias occurs when people actively search for and favor information or evidence that confirms their preconceptions or hypotheses while ignoring or slighting adverse or mitigating evidence. It is a type of cognitive bias (pattern of deviation in judgment that occurs in particular situations - leading to perceptual distortion, inaccurate judgment, or illogical interpretation) and represents an error of inductive inference toward confirmation of pre-existing world-views (or hypotheses).

Quit Jamming Me With More Data

Professional data scientists understand that more data does NOT make better decision making. A diversity of the right smart data sets applying the appropriate conceptual framework, scenario planning and probability theory is optimal decision strategy.

Training machine learning algorithms does require huge amounts of data yet risks bad, biased, incomplete, and low quality data. While learning algorithms can add massive value in specific contexts, they can also be dangerous and create significant downside risks considering severe limitations with real-world scenarios.

The Life of Data Science is Experience

Applied data science at the high level is about creating value, finding new truths and helping optimal decision making. Seasoned data scientists understand that the life of data science is experience and NOT models, statistics, math, artificial intelligence, logic or elegant computer code (although data scientists must be proficient with these disciplines). Technology and artificial intelligence are simply awesome tools that may be useful to achieve specific goals.

Strategic Wisdom vs. Tactical Scenarios

Professional data scientists are synthesizers of a wide diversity of data - turning data and information into specialized high value knowledge to achieve specific goals.

Strategic wisdom is a prudent plan to achieve one or more "big" long-term goals - consisting of a number of subsidiary medium-term and short-term goals. Developing long-term strategic wisdom is extremely difficult and required to formulate subsidiary goals. Senior data scientists help formulate strategy and modify as future reality shifts in complex environments.

Photonic Tensor Cores + 5G = Killer Apps

Speed kills in data science. Faster and higher performing processors allow more efficient data science tools at reasonable cost. This opens a blue ocean of high value data science in reasonable time frames for optimal near real time decision making.

Tensor core units (TCU) outperform graphic processing units (GPU) by three (3) times. Photonic Tensor Cores have the potential to crunch massive amounts and diversities of data faster and cheaper.

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