Lifeboat Foundation

The flying robot with wings is controlled by a magnetic field instead of heavy motors and gears.

By studying the risk of re-identification more thoroughly, researchers were able to better articulate the fundamental requirements for information to be anonymous. They realized that a robust definition of anonymous should not rely on what side information may be available to an attacker. This led to the definition of Differential Privacy in 2006 by Cynthia Dwork, then a researcher at Microsoft. It quickly became the gold standard for privacy and has been used in global technology products like Chrome, the iPhone, and Linkedin. Even the US Census used it for the 2020 census.

Differential privacy solves the problem of side information by looking at the most powerful attacker possible: an attacker who knows everything about everyone in a population except for a single individual. Let’s call her Alice. When releasing information to such an attacker, how can you protect Alice’s privacy? If you release exact aggregate information for the whole population (e.g., the average age of the population), the attacker can compute the difference between what you shared and the expected value of the aggregate with everyone but Alice. You just revealed something personal about Alice.

The only way out is to not share the exact aggregate information but add a bit of random noise to it and only share the slightly noisy aggregate information. Even for the most well-informed of attackers, differential privacy makes it impossible to deduce what value Alice contributed. Also, note that we have talked about simple insights like aggregations and averages but the same possibilities for re-identification apply to more sophisticated insights like machine learning or AI models, and the same differential privacy techniques can be used to protect privacy by adding noise when training models. Now, we have the right tools to find the optimal tradeoff: adding more noise makes it harder for a would-be attacker to re-identify Alice’s information, but at a greater loss of data fidelity for the data analyst. Fortunately, in practice, there is a natural alignment between differential privacy and statistical significance.

WASHINGTON, Feb 1 (Reuters) — Tesla Inc (TSLA.O) will recall 53,822 U.S. vehicles with the company’s Full Self-Driving (Beta) software that may allow some models to conduct “rolling stops” and not come to a complete stop at some intersections posing a safety risk.

The National Highway Traffic Safety Administration (NHTSA) said the recall covers some 2016–2022 Model S and Model X, 2017–2022 Model 3, and 2020–2022 Model Y vehicles. NHTSA said the feature also known as FSD Beta may allow vehicles to travel through an all-way stop intersection without first coming to a stop.

Tesla will perform an over-the-air software update that disables the “rolling stop” functionality, NHTSA said. The agency added it “maintains regular discussions with all manufacturers to discuss potential safety concerns of these types of systems.”

After simulating 10 contests, with more than 5,000 participants, AlphaCode has ranks in the top 54%.

American auto magazine Motor Trend is back to rehash its initial predictions for Apple’s yet unannounced “Apple Car”, this time updating it for the “inevitable” autonomous future.

The new report is, once again, a think piece that collates a collection of rumors into Motor Trend’s best guess at what Apple might have in the works.

It doesn’t take long for the publication to reference its first stab at imagining the “Apple Car,” one which wound up being ridiculed for being too “podlike.” Yet, as Motor Trend points out, podlike cars are being developed all over.

When I did my doctoral studies I studied a number of growth disciplines in areas like: complexity science, social network science (relationship and collaboration science), system thinking science, information science, and cognitive science. As a result of this knowledge, I learned how to connect business strategy goals using diverse growth strategies and analyze underlying operating systems that were either enabling relationship strength and growth outcomes or creating negative systemic feedback loops that prevented revenue acceleration.

There is a word in the English language seldom used called quaquaversal which means looking in all directions all at once which represents the field of complexity science and is the reality of the executive mindset that needs to operate in the board room and in today’s fast paced world — *what one sees as relevant today may well be obsolete tomorrow.*

This blog series will explore each of these discipline areas and connect real life examples of AI approaches that are enabling growth acceleration techniques using these science and social science techniques. This is the first blog in this five part blog series and will focus on complexity science.

Continue reading “Accelerating Growth Using AI — A Look At Complexity And The Metaverse Series 1/5” »

Ford Motor Co. is working with Israeli startup Electreon to construct a mile-long road near Detroit’s Michigan Central Terminal that will charge electric vehicles as they travel on it. The pilot program will deploy an inductive in-road charging system in partnership with the Michigan Department of Transportation, the Michigan Office of Future Mobility and Electrification and the Michigan Economic Development Corp. “As we aim to lead the future of mobility and electrification by boosting electric vehicle production and lowering consumer costs, a wireless in-road charging system is the next piece to the puzzle for sustainability,” Michigan Governor Gretchen Whitmer said in a statement. Also supporting the project, which is expected to be operational in 2023, are Next Energy and the Jacobs Engineering Group. Ford purchased the long-abandoned train station and is converting it to be the hub of what it calls its “mobility innovation district,” where software developers and others will focus on making electrified and autonomous transportation more practical.

Full Story:

Alphabet’s AI lab DeepMind says it’s created an AI coding engine that is as good as the average human programmer. However, the system was only tested on specific coding challenges that do not reflect real-world tasks.

Teaching old tech to do new tricks (and do them solo).