Evolution and Humanoid AI Robots

EVOLUTION - is the process by which the genetic material of a population changes over time, resulting in new traits, altered genes, and sometimes new species. It's a fundamental part of modern biological theory and is based on the idea that all species are related and change gradually over time.

Evolution occurs when there are changes in the proportions of genes in a population, or in the genetic material itself. This genetic material, DNA, is inherited from parents and contains chemical codes that produce proteins. The information in DNA can change through mutation, or the way genes are expressed can change. These changes can affect an organism's physical characteristics, or phenotype.

Evolution is a response to organisms adapting to their changing environments. For example, genetic studies indicate that humans are still evolving, even though we face fewer hazards today than in the past.

SELF ORGANIZATION - is the process by which individuals organize their communal behavior to create global order by interactions amongst themselves rather than through external intervention or instruction. As a highly complex and dynamic system involving many different elements interacting with each other, the nervous system displays many features of self-organization. This chapter discusses three forms of neural self-organization namely self-organization in development, self-organization as a complement to experiential changes, and self-organization as a complement to damage. 

Self-organization in development is concerned the development of the nervous system. Since a key challenge in our understanding of the nervous system is to comprehend how such a highly structured yet complex system can emerge from a single fertilized egg. Self-organization as a complement to experiential changes refers to later stages in development, when self-organization plays a role along with other mechanisms such as those involving external signals arising from the sensory environment.

SELF SIMILARITY - is a property of many natural objects that makes them appear to have repeating patterns, curves, branching patterns, or substructures that are similar to the whole object. These objects are sometimes called fractal-like and are often super self-similar, meaning they are the most common type of self-similarity in nature.

Here are some examples of self-similarity in nature:
Trees
The branches of trees can exhibit natural disorder that may seem chaotic, but it's actually more organized than it appears.
Pine cones
The scales of pine cones spiral in a way that reflects the seeds they protect. This fractal design is caused by accelerated growth.
Coastlines
The coastline of Britain is an example of a shape that is self-similar, even when zoomed in by a thousand times.

Other examples of self-similarity in nature include: clouds, waves, ferns, cauliflowers, and flowers.

Now...

While this may be a lot to take in all at one time, Evolution, Self-Organization, and Self-Similarity are exactly the THREE VARIABLES that when given to humanoid AI robots in the form of algorithms allow them to TEACH THEMSELVES and transform into machines that can function like humans.

From the simple comes the complex...

Simple algorithms tell the robot to repeat the task over and over again, trashing the bad examples while keeping the good, to create an evolved robot over time.

When scientists program a robot with a brain and the simple task of learning to walk.  Within twenty evolutions, the robot without any further human interaction or programming taught itself to walk.

The robot uses the basic concepts of NATURE to evolve...

For a NOVICE like me, I find this fascinating.

 

Shaping the Future of Machine Learning

Few challenges are as formidable as building a quantum computer. It’s not just about wiring its components but also making them work together to produce accurate computation results despite the presence of noise that can introduce errors in quantum computations.

While you can write down a quantum computation as an abstract mathematical model, implementing it practically still means adjusting certain parameters, such as microwave pulses or lasers, which you can only do with limited precision. Thus, there would inevitably be a gap between the model you intended to implement and the actual outcome.

Qruise leverages machine learning to narrow this gap. By building a physical model from a quantum computer’s experimental data and comparing it to the intended behavior, Qruise helps physicists and engineers to improve their quantum computers. This also works for other quantum devices, such as quantum sensors, and other fields like photonics.

Founded as a spinoff from Forschungszentrum Jülich in late 2021 by Shai Machnes, Frank Wilhelm-Mauch, Tommaso Calarco, and Simone Montangero, Qruise raised funding from Constructor Capital and went through the Creative Destruction Lab startup program.

Learn more about the future of machine learning for quantum computing and beyond from our interview with the co-founder and CEO, Shai Machnes:    Why Did You Start Qruise?

Even before Qruise, my co-founders and I were researching how to control quantum systems. At some point, more academic groups than we could handle as researchers wanted to collaborate with us, so we decided to found a company in late 2021.

Our initial focus was just quantum control, but we soon realized that the very same tools could be applied to quantum sensing or even other domains, like photonics.

When I think of Qruise today, we’re actually building a “machine learning physicist”—a system that can predict and control all kinds of physical processes. I already had this vision more than 15 years ago as a researcher, but I wasn’t able to realize it. With recent advances in computing power and data availability, machine learning has greatly improved, making it possible for us to pursue this vision.  READ MORE...

Controlling Autonomous Robots

In the film "Top Gun: Maverick," Maverick, played by Tom Cruise, is charged with training young pilots to complete a seemingly impossible mission—to fly their jets deep into a rocky canyon, staying so low to the ground they cannot be detected by radar, then rapidly climb out of the canyon at an extreme angle, avoiding the rock walls. 

Spoiler alert: With Maverick's help, these human pilots accomplish their mission.

A machine, on the other hand, would struggle to complete the same pulse-pounding task. To an autonomous aircraft, for instance, the most straightforward path toward the target is in conflict with what the machine needs to do to avoid colliding with the canyon walls or staying undetected. 

Many existing AI methods aren't able to overcome this conflict, known as the stabilize-avoid problem, and would be unable to reach their goal safely.  MIT researchers have developed a new technique that can solve complex stabilize-avoid problems better than other methods. 

Their machine-learning approach matches or exceeds the safety of existing methods while providing a tenfold increase in stability, meaning the agent reaches and remains stable within its goal region.

In an experiment that would make Maverick proud, their technique effectively piloted a simulated jet aircraft through a narrow corridor without crashing into the ground.  READ MORE...

Artificial Intellilgence Warning

A serial artificial intelligence investor is raising alarm bells about the dogged pursuit of increasingly-smart machines, which he believes may soon advance to the degree of divinity.

In an op-ed for the Financial Times, AI mega-investor Ian Hogarth recalled a recent anecdote in which a machine learning researcher with whom he was acquainted told him that "from now onwards," we are on the brink of developing artificial general intelligence (AGI) — an admission that came as something of a shock.

"This is not a universal view," Hogarth wrote, noting that "estimates range from a decade to half a century or more" before AGI comes to fruition.

All the same, there exists a tension between the explicitly AGI-seeking goals of AI companies and the fears of machine learning experts — not to mention the public — who understand the concept.

"'If you think we could be close to something potentially so dangerous,' I said to the researcher, 'shouldn’t you warn people about what’s happening?'" the investor recounted. "He was clearly grappling with the responsibility he faced but, like many in the field, seemed pulled along by the rapidity of progress."

Like many other parents, Hogarth said that after this encounter, his mind drifted to his four-year-old son.

"As I considered the world he might grow up in, I gradually shifted from shock to anger," he wrote. "It felt deeply wrong that consequential decisions potentially affecting every life on Earth could be made by a small group of private companies without democratic oversight."  READ MORE...

Machine Learning Matters

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. 

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. 

Here are a few widely publicized examples of machine learning applications you may be familiar with:

The heavily hyped, self-driving Google car? 

The essence of machine learning.
Online recommendation offers such as those from Amazon and Netflix? 

Machine learning applications for everyday life.
Knowing what customers are saying about you on Twitter? 

Machine learning combined with linguistic rule creation.
Fraud detection? 

One of the more obvious, important uses in our world today.

Machine Learning and Artificial Intelligence
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.

Why is machine learning important?
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.  READ MORE...

Math and Machine Learning

Machine learning makes it possible to generate more data than mathematician can in a lifetime

For the first time, mathematicians have partnered with artificial intelligence to suggest and prove new mathematical theorems. While computers have long been used to generate data for mathematicians, the task of identifying interesting patterns has relied mainly on the intuition of the mathematicians themselves. However, it’s now possible to generate more data than any mathematician can reasonably expect to study in a lifetime. Which is where machine learning comes in.

Two separate groups of mathematicians worked alongside DeepMind, a branch of Alphabet, Google’s parent company, dedicated to the development of advanced artificial intelligence systems. András Juhász and Marc Lackenby of the University of Oxford taught DeepMind’s machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhász and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

András Juhász and Marc Lackenby of the University of Oxford taught DeepMind’s machine learning models to look for patterns in geometric objects called knots. The models detected connections that Juhász and Lackenby elaborated to bridge two areas of knot theory that mathematicians had long speculated should be related. In separate work, Williamson used machine learning to refine an old conjecture that connects graphs and polynomials.

“The most amazing thing about this work and it really is a big breakthrough is the fact that all the pieces came together and that these people worked as a team,” said Radmila Sazdanovic of North Carolina State University.

Some observers, however, view the collaboration as less of a sea change in the way mathematical research is conducted. While the computers pointed the mathematicians toward a range of possible relationships, the mathematicians themselves needed to identify the ones worth exploring.

Internet of Things

The Internet of Things (IoT) describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These devices range from ordinary household objects to sophisticated industrial tools. With more than 7 billion connected IoT devices today, experts are expecting this number to grow to 10 billion by 2020 and 22 billion by 2025. Oracle has a network of device partners.

Over the past few years, IoT has become one of the most important technologies of the 21st century. Now that we can connect everyday objects—kitchen appliances, cars, thermostats, baby monitors—to the internet via embedded devices, seamless communication is possible between people, processes, and things.

By means of low-cost computing, the cloud, big data, analytics, and mobile technologies, physical things can share and collect data with minimal human intervention. In this hyperconnected world, digital systems can record, monitor, and adjust each interaction between connected things. The physical world meets the digital world—and they cooperate.

While the idea of IoT has been in existence for a long time, a collection of recent advances in a number of different technologies has made it practical.

• Access to low-cost, low-power sensor technology. Affordable and reliable sensors are making IoT technology possible for more manufacturers.
• Connectivity. A host of network protocols for the internet has made it easy to connect sensors to the cloud and to other “things” for efficient data transfer.
• Cloud computing platforms. The increase in the availability of cloud platforms enables both businesses and consumers to access the infrastructure they need to scale up without actually having to manage it all.
• Machine learning and analytics. With advances in machine learning and analytics, along with access to varied and vast amounts of data stored in the cloud, businesses can gather insights faster and more easily. The emergence of these allied technologies continues to push the boundaries of IoT and the data produced by IoT also feeds these technologies.
• Conversational artificial intelligence (AI). Advances in neural networks have brought natural-language processing (NLP) to IoT devices (such as digital personal assistants Alexa, Cortana, and Siri) and made them appealing, affordable, and viable for home use.

TO READ MORE ABOUT IoT,  CLICK HERE...

Predictive Analytics

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.

Though predictive analytics has been around for decades, it's a technology whose time has come. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. 

Why now?

• Growing volumes and types of data, and more interest in using data to produce valuable insights.
• Faster, cheaper computers.
• Easier-to-use software.
• Tougher economic conditions and a need for competitive differentiation.

With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.

Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Common uses include:

Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.

Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.

Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.

Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.  READ MORE...

HyperAutomation

Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.

Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including:

• Artificial intelligence (AI)
• Machine learning
• Event-driven software architecture
• Robotic process automation (RPA)
• Business process management (BPM) and intelligent business process management suites (iBPMS)
• Integration platform as a service (iPaaS)
• Low-code/no-code tools
• Packaged software
• Other types of decision, process and task automation tools

As a large-scale endeavor, hyperautomation requires careful planning and deliberation. At the same time, the potential upside and return-on-investment should enervate team members from across the organization; they have the opportunity to unburden teams from repetitive labor and potentially boost the success of their value-producing work.

While the goal of hyperautomation is to truly encompass all automatable processes, realistic implementation is more nuanced. A feasible hyperautomation strategy involves several key steps:

Assess budget and identify cost savings by highlighting existing automations. Workflows using RPA or other technologies can be expanded to other areas of the company in the course of the hyperautomation process.

Gather information on the existing processes that can be automated or otherwise must remain manual. Pay special attention to bottlenecks that maintain existing delays and can be improved through automation. One particularly effective technique for analysis is digital twinning, where an organization creates a virtual model of a process for deeper analysis and manipulation without affecting existing workflows.

Collect data. Automations are only as good as the data they run on, and the pipelines delivering the right data to the right place must be created alongside these machine-driven workflows. Creating a pipeline between these data stores and the automations that will use them is an essential step.

Identify automation tools. Project leaders may choose to begin by replicating one existing automation into another area of their operation, such as standardizing one automated approvals process for other decision makers. In fact, the automations themselves can be automated for more efficiency gains. It all depends on which tools and platforms are put to use throughout the project.

Predict outcomes. Automation for automation’s sake is even less effective at the enterprise-level and can lead to workers growing unclear on where their responsibilities lie. Outcome prediction involves setting the inputs for an automation, noting any hand-offs or human interventions, and predicting the results that arrive, as well as larger considerations like efficiency improvements and overall ROI.

Implement automations. AI tools can build the automations iteratively with human guidance to achieve consistent benchmarks. It’s this collaboration between machine learning and human intuition that drives successful hyperautomation endeavors, and creates clear guidance on the future of employee responsibilities.

Next Steps: Leveraging Hyperautomation
The key word for hyperautomation is interoperability. A single automation can be extended to processes, while an operation that once relied mostly or entirely on manual work can be enhanced through AI tools. A single document submission, for example, may OCR to interpret the text, sentiment analysis to identifying the underlying meaning, and AI to draft model responses. Each tool individually works on its own for a wider variety of tasks, with deployment made easy by successful replication and oversight from IT.  READ MORE...

Ultra Compact Camera

Scientific ingenuity means cameras keep on getting smaller and smaller, and the latest to appear is not only incredibly tiny – the same size as a grain of salt – it's also able to produce images of much better quality than a lot of other ultra-compact cameras.

Using a technology known as a metasurface, which is covered with 1.6 million cylindrical posts, the camera is able to capture full-color photos that are as good as images snapped by conventional lenses some half a million times bigger than this particular camera.

And the super-small contraption has the potential to be helpful in a whole range of scenarios, from helping miniature soft robots explore the world, to giving experts a better idea of what's going on deep inside the human body.

Existing micro-sized camera (left) versus the new model (right). (Princeton University)

"It's been a challenge to design and configure these little microstructures to do what you want," says computer scientist Ethan Tseng from Princeton University in New Jersey.

"For this specific task of capturing large field of view RGB images, it was previously unclear how to co-design the millions of nano-structures together with post-processing algorithms."

One of the camera's special tricks is the way it combines hardware with computational processing to improve the captured image: Signal processing algorithms use machine learning techniques to reduce blur and other distortions that otherwise occur with cameras this size. The camera effectively uses software to improve its vision.  READ MORE...

Forecasting 2022

Artificial Intelligence (AI) and machine learning, cloud computing, and 5G will be the most important technologies in 2022, according to a survey to global technology leaders from the U.S., U.K., China, India, and Brasil, conducted by IEEE.

The IEEE is the Institute of electronic and Electrical engineers....  and they believe the following areas will lead the way:

• Telemedicine
• Remote surgery
• remote learning
• personal communications
• professional communications
• live event streaming
• manufacturing
• transportations
• energy efficiency
• agriculture

Deepfake Technology

Deepfakes (a portmanteau of "deep learning" and "fake") are synthetic media in which a person in an existing image or video is replaced with someone else's likeness. While the act of faking content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content with a high potential to deceive. The main machine learning methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs).

An example of deepfake technology: in a scene from Man of Steel,
actress Amy Adams in the original (left) is modified
to have the face of actor Nicolas Cage (right)

Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud. This has elicited responses from both industry and government to detect and limit their use.

Photo manipulation was developed in the 19th century and soon applied to motion pictures. Technology steadily improved during the 20th century, and more quickly with digital video.

Deepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities.  More recently the methods have been adopted by industry.  SOURCE: Wikipedia