Lifeboat Foundation

This post is a collaboration with Dr. Augustine Fou, a seasoned digital marketer, who helps marketers audit their campaigns for ad fraud and provides alternative performance optimization solutions; and Jodi Masters-Gonzales, Research Director at Beacon Trust Network and a doctoral student in Pepperdine University’s Global Leadership and Change program, where her research intersects at data privacy & ethics, public policy, and the digital economy.

The ad industry has gone through a massive transformation since the advent of digital. This is a multi-billion dollar industry that started out as a way for businesses to bring more market visibility to products and services more effectively, while evolving features that would allow advertisers to garner valuable insights about their customers and prospects. Fast-forward 20 years later and the promise of better ad performance and delivery of the right customers, has also created and enabled a rampant environment of massive data sharing, more invasive personal targeting and higher incidences of consumer manipulation than ever before. It has evolved over time, underneath the noses of business and industry, with benefits realized by a relative few. How did we get here? More importantly, can we curb the path of a burgeoning industry to truly protect people’s data rights?

There was a time when advertising inventory was finite. Long before digital, buying impressions was primarily done through offline publications, television and radio. Premium slots commanded higher CPM (cost per thousand) rates to obtain the most coveted consumer attention. The big advertisers with the deepest pockets largely benefitted from this space by commanding the largest reach.

The subtle whirring of a battery-powered motor, the crunch of dried grass and leaves, and the whizzing of wind are all you will hear from the 2nd/14th Light Horse Regiment of Australia’s Queensland Mounted Infantry as its soldiers rush through the brush on stealthy e-bikes.

The e-bikes are being trialed to see if they can provide a worthy option for speedy, silent, and safe on-the-ground reconnaissance.

Continue reading “Could Stealthy E-Bikes Be Australian Army’s New Reconnaissance Transportation?” »

Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.

Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.

Full Story:

Continue reading “Artificial networks learn to smell like the brain” »

Yahoo Finance’s Ines Ferre reports on LinkedIn shutting down its app in China with plans to launch a jobs-only platform later this year.
Don’t Miss: Valley of Hype: The Culture That Built Elizabeth Holmes.
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Watch the 2021 Berkshire Hathaway Annual Shareholders Meeting on YouTube:
https://youtu.be/gx-OzwHpM9k.

Continue reading “LinkedIn shutting down in China due to censorship controversy” »

If the properties of materials can be reliably predicted, then the process of developing new products for a huge range of industries can be streamlined and accelerated. In a study published in Advanced Intelligent Systems, researchers from The University of Tokyo Institute of Industrial Science used core-loss spectroscopy to determine the properties of organic molecules using machine learning.

The spectroscopy techniques energy loss near-edge structure (ELNES) and X-ray near-edge structure (XANES) are used to determine information about the electrons, and through that the atoms, in materials. They have high sensitivity and high resolution and have been used to investigate a range of materials from electronic devices to drug delivery systems.

However, connecting spectral data to the properties of a material—things like optical properties, electron conductivity, density, and stability—remains ambiguous. Machine learning (ML) approaches have been used to extract information for large complex sets of data. Such approaches use artificial neural networks, which are based on how our brains work, to constantly learn to solve problems. Although the group previously used ELNES/XANES spectra and ML to find out information about materials, what they found did not relate to the properties of the material itself. Therefore, the information could not be easily translated into developments.

While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past.

1:42 Are we on the wrong train to AGI?
4:20 Marvin Minsky and AI generalization problem.
11:57 Defining intelligence in AI
17:17 Is AI masquerading as a trendy statistical analysis tool?
23:35 AI systems lack our most basic intuitions.
27:38 The public not wanting to face Reality.
29:36 Equipping AI with Kant’s categories of the mind (Time, Space, Causality)
33:40 Neural nets VS traditional tools.
34:50 Causality in AI
37:14 Lack of interdisciplinary learning.
45:54 How can we achieve human level of understanding in AI?
49:21 More limitations.
59:35 Motivation in inanimate systems.
1:01:31 Lack of body and transcendent consciousness.
1:05:55 What interdisciplinary learning would you encourage?
1:06:49 Book recommendations.

Gary Marcus is CEO and Founder of Robust AI, well-known machine learning scientist and entrepreneur, author, and Professor Emeritus at New York State University.

Continue reading “0:00 Introduction” »

From cryogenics to drugs the dream of immortality is closer than ever and now the world’s richest men including Richard Branson, Jeff Bezos and Elon Musk are putting their weight behind the research.

A Long March 2F rocket launched the Shenzhou 13 spacecraft carrying astronauts Zhai Zhigang (commander), Wang Yaping and Ye Guangfu to the Tianhe core module of China’s new space station on Oct. 15 2021. [Full Story](https://www.space.com/china-launches-shenzhou-13-astronauts-to-space-station)

Credit: China Central Television