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Summary: Humans and computers can interact via multiple modes and channels to respectively gain wisdom and deepen intelligence.

Source: Intelligent Computing

Deyi Li from the Chinese Association for Artificial Intelligence believes that humans and machines have a mutually beneficial relationship.

His paper on machine intelligence, which was published inIntelligent Computing builds on five groundbreaking works by Schrdinger, the father of quantum mechanics, Turing, the father of artificial intelligence, and Wiener, the father of cybernetics.

Schrdinger and beyond: Machines can think and interact with the world as time goes by.

Inspired by Schrdingers book What is Life? The Physical Aspect of the Living Cell, Li believes that machines can be considered living things. That is, like humans, they decrease the amount of entropy or disorder in their environment through their interactions with the world.

The machines of the agricultural age and the industrial age existed only at the physical level, but now, in the age of intelligence, machines consist of four elements at two different levels: matter and energy at the physical level, and structure and time at the cognitive level. The machine can be the carrier of thought, and time is the foundation of machine cognition, Li explained.

Turing and beyond: Machines can think, but can they learn?

In 1936, Turing published what has been called the most influential mathematics paper, establishing the idea of a universal computing machine able to perform any conceivable computation. Such hypothetical computers are called Turing machines.

His 1950 paper Computing Machinery and Intelligence introduced what is now known as the Turing test for measuring machine intelligence, sparking a debate over whether machines can think. A proponent of thinking machines, Turing believed that a child machine could be educated and eventually achieve an adult level of intelligence.

However, given that cognition is only one part of the learning process, Li pointed out two limitations of Turings model in achieving better machine intelligence: First, the machines cognition is disconnected from its environment rather than connected to it.

This shortcoming has also been highlighted in a paper by Michael Woodridge titledWhat Is Missing from Contemporary AI? The World.Second, the machines cognition is disconnected from memory and thus cannot draw on memories of past experiences.

As a result, Li defines intelligence as the ability to engage in learning, the goal of which is to be able to explain and solve actual problems.

Wiener and beyond: Machines have behavioral intelligence.

In 1948, Wiener published a book that served as the foundation of the field of cybernetics, the study of control and communication within and between living organisms, machines and organizations.

In the wake of the success of the book, he published another, focusing on the problems of cybernetics from the perspective of sociology, suggesting ways for humans and machines to communicate and interact harmoniously.

According to Li, machines follow a control pattern similar to the human nervous system. Humans provide missions and behavioral features to machines, which must then run a complex behavior cycle regulated by a reward and punishment function to improve their abilities of perception, cognition, behavior, interaction, learning and growth.

Through iteration and interaction, the short-term memory, working memory and long-term memory of the machines change, embodying intelligence through automatic control.

In essence, control is the use of negative feedback to reduce entropy and ensure the stability of the embodied behavioral intelligence of a machine, Li concluded.

The strength of contemporary machines is deep learning, which still requires human input, but leverages the ability of devices to use brute force methods of solving problems with insights gleaned directly from big data.

A joint future: from learning to creating

Machine intelligence cannot work in isolation; it requires human interaction. Furthermore, machine intelligence is inseparable from language, because humans use programming languages to control machine behavior.

The impressive performance of ChatGPT, a chatbot showcasing recent advances in natural language processing, proves that machines are now capable of internalizing human language patterns and producing appropriate example texts, given the appropriate context and goal.

Since AI-generated texts are increasingly indistinguishable from human-written texts, some are saying that AI writing tools have passed the Turing test. Such declarations provoke both admiration and alarm.

Li is among the optimists who envision artificial intelligence in a natural balance with human civilization. He believes, from a physics perspective, that cognition is based on a combination of matter, energy, structure and time, which he calls hard-structured ware, and expressed through information, which he calls soft-structured ware.

He concludes that humans and machines can interact through multiple channels and modes to gain wisdom and intelligence, respectively. Despite their different endowments in thinking and creativity, this interaction allows humans and machines to benefit from each others strengths.

Author: Xuwen LiuSource: Intelligent ComputingContact: Xuwen Liu Intelligent ComputingImage: The image is credited to Deyi Li

Original Research: Open access.Cognitive PhysicsThe Enlightenment by Schrdinger, Turing, and Wiener and Beyond by Deyi Li. Intelligent Computing

Abstract

Cognitive PhysicsThe Enlightenment by Schrdinger, Turing, and Wiener and Beyond

In the first half of the 20th century, 5 classic articles were written by 3 outstanding scholars, namely, Wiener (1894 to 1964), the father of cybernetics, Schrdinger (1887 to 1961), the father of quantum mechanics, and Turing (1912 to 1954), the father of artificial intelligence.

The articles discuss the concepts such as computability, life, machine, control, and artificial intelligence, establishing a solid foundation for the intelligence of machines (how machines can recognize as humans do?) and its future development.

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Autonomous transport company Aurrigo has improved its driverless vehicles capabilities in a project with Aston University.

Aurrigos airport Auto-Dolly is now able to differentiate between many different objects

The two-year Knowledge Transfer Partnership (KTP) with the university developed a new machine vision solution, using machine learning and artificial intelligence that means the Coventry-based companys driverless vehicles are now able to see and recognise objects in greater detail. This results in improved performance across a wider spectrum of test situations.

Previously the companys driverless vehicles were only capable of detecting that there was an object in their path, not the type of object, so would just stop when they encountered something in their way.

The new computer vision systems, coupled with machine learning and artificial intelligence, are now able to differentiate between different objects, enabling Aurrigos airport Auto-Dolly to differentiate between many different objects airside.

Professor David Keene, CEO of Aurrigo, said: This partnership has allowed us to produce a system which has resulted in our vehicles becoming smarter and more capable and enabled us to expand our operations, particularly with baggage handling in airports worldwide.

Dr George Vogiatzis, senior lecturer in computer science at Aston University, added: This KTP has been a great way for us to work with a new industrial partner whilst applying our expertise in deep learning and robotics to the exciting field of autonomous vehicles.

It is very rewarding to see the success of this collaboration.

The project findings will also be applied to other vehicles in the Aurrigo product range.

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Spreadsheets are often used by businesses of all sizes to complete both simple and complex tasks. Machine learning technology advancements have the potential to revolutionise different industries. Spreadsheet usage is meant to be accessible to all types of users, whereas machine learning is usually perceived as being too complex to use. Google is currently attempting to shift that paradigm for its online spreadsheet application Google Sheets. Explore more about the new machine learning add on for Google Sheets right below.

The operation of Google Sheets works in these three steps given below.

Check out the benefits of simple ML or new machine learning addon in Google sheets right below.

The beta version of Simple ML for Sheets is now accessible. A team of TensorFlow developers developed the Google Sheets add-on to make machine learning available to Sheets users with no prior experience with machine learning. Pretrained machine learning models and other no-code features are primarily used to achieve this.

Predicting missing values and identifying abnormal values are the two main ML tasks that this machine learning add-on is intended to support. Nevertheless, Simple ML for Sheets can also be used for more complex use cases like developing, testing, and analyzing machine learning models. It is likely that Simple MLs Advanced Tasks will need to be used, especially for data scientists and more experienced users who want to use Simple ML to make predictions.

For installing Simple ML for Sheets, users should go to the Extensions tab, get over the Add-ons options, and get add-ons. From there, finding and installing Simple ML is a fairly simple process.

Bottom Lines

Even though Simple ML is quick and reasonably accurate, users still need to know how to set up their data and read the newly created model to be successful. This new machine learning addon is very beneficial for the users of Google sheets. Hence, explore this wonderful addon of Google sheets and enjoy the best features to grab success in your business. You can find your business operating smoothly with simple ML.

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By Subramanyam Reddy, CEO and Founder, KnowledgeHut upGrad

The global Machine Learning market was valued at US$ 6.9 billion in 2018 and is projected to grow at a CAGR of over 43% between 2019 to 2025, as per a Bloomberg report. Against this, ML has also emerged as one of the fastest-growing fields for career seekers, boasting a year-on-year growth rate of 300%, enjoying unprecedented levels of popularity among young professionals. Machine Learnings growth and popularity are rooted in the growing digitization of all sectors across the world, significantly, in education.

Particularly during the pandemic and after, the education sector has had to fast-track the adoption of tech in delivery. AI and ML applications have found their way into revolutionizing the education and EdTech sectors with the technology driving delivery, assessment, and enhanced retention amongst learners. After the USA, India is one of the biggest markets for e-learning solutions in the world.

The autonomous way in which computers learn is in turn creating an impact in how learning happens in classrooms and beyond. Machine Learning (ML)s giant strides in rapidly transforming the field of education in India are expected to continue in 2023 and beyond.

Lets look at some of the trends emerging in the sector this year and beyond:

Personalised learning is emerging as one of the forerunners in the impact areas of ML in education. Across schools and universities in India, personalised learning is gaining traction and it is driven by AI & ML. Analyzing patterns and behaviors, ML aids instructors and teachers in customising learning for different learners needs. The effectiveness of these interventions is also analyzed by ML.

Another emerging trend driven by AI & ML in education is the development of AI-powered tools to aid learning. The shockwaves created by Chat GPT and other AI-powered platforms are making way for curiosity in how these tools will help people learn be it coding, writing better, developing creative concepts, and more. The access to the vast quantum of data and superfast processing capabilities of such platforms generate accurate answers to questions posed. While several may argue that humans cannot match supercomputers in terms of access or processing, the aim of these technologies is not to one-up humans. The approach to learning changes in a fundamental manner with the advent of such tools. What are the outcomes we seek through learning, and how can tech aid those outcomes, becomes a focal point here.

India has sixteen official languages and hundreds of unofficial languages and dialects spoken across the country. Effective communication is often one of the biggest challenges in the public works domain. For effective reach and improved access to information, AI & ML tools and technologies such as NLP play a significant role in helping people learn languages and improve communication and collaboration across geographies. With MLs aid, learning languages can become simpler and more accessible to a larger audience.

When it comes to assessment and evaluation in learning, the human perspective is more often than not, rooted in personal prejudices and biases. The objective perspective is lost in such scenarios, making evaluations a tool to deter rather than advance. The way ML steps into these areas of assessment and evaluation completely change the game. The same assessments then become a path for advancement, through the identification of areas of improvement and existing strengths of the learner.

Overall, the use of ML in education in India is expected to continue to grow in 2023, with more educators and institutions turning to these technologies to improve the learning experience for students.

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What is machine learning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation enginesare a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms.Unsupervised learning algorithms are good for the following tasks:

Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:

Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards -- which it receives when it performs an action that is beneficial toward the ultimate goal -- and avoid punishments -- which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:

Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook's news feed.

Facebook uses machine learning to personalize how each member's feed is delivered. If a member frequently stops to read a particular group's posts, the recommendation engine will start to show more of that group's activity earlier in the feed.

Behind the scenes, the engine is attempting to reinforce known patterns in the member's online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly.

In addition to recommendation engines, other uses for machine learning include the following:

Machine learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars.

When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviors over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.

Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.

But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.

There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.

The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.

Step 1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

Step 2: Collect data, format it and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

Step 3: Chose which algorithm(s) to use and test to see how well they perform. This step is usually carried out by data scientists.

Step 4: Continue to fine tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it's important for the business to explain how every decision was made. This is especially true in industries with heavy compliance burdens such as banking and insurance.

Complex models can produce accurate predictions, but explaining to a lay person how an output was determined can be difficult.

While machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today's most advanced AI applications.

Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.

As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

1642 - Blaise Pascal invents a mechanical machine that can add, subtract, multiply and divide.

1679 - Gottfried Wilhelm Leibniz devises the system of binary code.

1834 - Charles Babbage conceives the idea for a general all-purpose device that could be programmed with punched cards.

1842 - Ada Lovelace describes a sequence of operations for solving mathematical problems using Charles Babbage's theoretical punch-card machine and becomes the first programmer.

1847 - George Boole creates Boolean logic, a form of algebra in which all values can be reduced to the binary values of true or false.

1936 - English logician and cryptanalyst Alan Turing proposes a universal machine that could decipher and execute a set of instructions. His published proof is considered the basis of computer science.

1952 - Arthur Samuel creates a program to help an IBM computer get better at checkers the more it plays.

1959 - MADALINE becomes the first artificial neural network applied to a real-world problem: removing echoes from phone lines.

1985 - Terry Sejnowski's and Charles Rosenberg's artificial neural network taught itself how to correctly pronounce 20,000 words in one week.

1997 - IBM's Deep Blue beat chess grandmaster Garry Kasparov.

1999 - A CAD prototype intelligent workstation reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did.

2006 - Computer scientist Geoffrey Hinton invents the term deep learning to describe neural net research.

2012 - An unsupervised neural network created by Google learned to recognize cats in YouTube videos with 74.8% accuracy.

2014 - A chatbot passes the Turing Test by convincing 33% of human judges that it was a Ukrainian teen named Eugene Goostman.

2014 - Google's AlphaGo defeats the human champion in Go, the most difficult board game in the world.

2016 - LipNet, DeepMind's artificial intelligence system, identifies lip-read words in video with an accuracy of 93.4%.

2019 - Amazon controls 70% of the market share for virtual assistants in the U.S.

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