Category Archives: Ai

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has led at least in the West to warnings of a global AI arms race and concerns about the growing reach of Chinas authoritarian surveillance state. But treating China as a villain in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese governments approach to AI that are highly concerning and rightly should be condemned, its important that this does not cloud all analysis of Chinas AI innovation.

The world needs to engage seriously with Chinas AI development and take a closer look at whats really going on. The story is complex and its important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

Nesta has explored the broad spectrum of AI activity in China the good, the bad and the unexpected.

The Good

Chinas approach to AI development and implementation is fast-paced and pragmatic, oriented towards finding applications which can help solve real-world problems. Rapid progress is being made in the field of healthcare, for example, as China grapples with providing easy access to affordable and high-quality services for its ageing population.

Applications include AI doctor chatbots, which help to connect communities in remote areas with experienced consultants via telemedicine; machine learning to speed up pharmaceutical research; and the use of deep learning for medical image processing, which can help with the early detection of cancer and other diseases.

Since the outbreak of COVID-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis and new drug development. AI tools used in Wuhan, China, to tackle COVID-19 by helping accelerate CT scan diagnosis are now being used in Italy and have been also offered to the NHS in the UK.

The Bad

But there are also elements of Chinas use of AI which are seriously concerning. Positive advances in practical AI applications which are benefiting citizens and society dont detract from the fact that Chinas authoritarian government is also using AI and citizens data in ways that violate privacy and civil liberties.

Most disturbingly, reports and leaked documents have revealed the governments use of facial recognition technologies to enable the surveillance and detention of Muslim ethnic minorities in Chinas Xinjiang province.

The emergence of opaque social governance systems which lack accountability mechanisms are also a cause for concern.

In Shanghais smart court system, for example, AI-generated assessments are used to help with sentencing decisions. But it is difficult for defendants to assess the tools potential biases, the quality of the data and the soundness of the algorithm, making it hard for them to challenge the decisions made.

Chinas experience reminds us of the need for transparency and accountability when it comes to AI in public services. Systems must be designed and implemented in ways that are inclusive and protect citizens digital rights.

The Unexpected

Commentators have often interpreted the State Councils 2017 Artificial Intelligence Development Plan as an indication that Chinas AI mobilisation is a top-down, centrally planned strategy.

But a closer look at the dynamics of Chinas AI development reveals the importance of local government in implementing innovation policy. Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.

Beyond the thriving major cities of Beijing, Shanghai and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an AI Town, clustering together the tech company Alibaba, Zhejiang University and local businesses to work collaboratively on AI development. Chinas local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.

Chinas accelerating AI innovation deserves the worlds full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understand and learn from the nuances of whats really happening.

Hessy Elliott is aresearcher atNesta.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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China and AI: What the World Can Learn and What It Should Be Wary of - Nextgov

Early on in the evolution of artificial intelligence, researchers realized the power and possibility of machines that are able to understand the meaning and nuances of human speech. Conversation and human language is a particularly challenging area for computers, since words and communication is not precise. Human language is filled with nuance, context, cultural and societal depth, and imprecision that can lead to a wide range of interpretations. If computers can understand what we mean when we talk, and then communicate back to us in a way we can understand, then clearly weve accomplished a goal of artificial intelligence.

Conversational interaction as a pattern of AI

Conversational pattern of AI

This particular application of AI is so profound that it makes up one of the fundamental seven patterns of AI: the conversation and human interaction pattern. The fundamental goal of the conversational pattern is to enable machines to communicate with humans in human natural language patterns, and for machines to communicate back to humans in the language they understand. Instead of requiring humans to conform to machine-modes of interaction such as typing, swiping, clicking, or using computer programming languages, the power of the conversational pattern is that we can interact with machines the way we interact with each other: by speaking, writing, and communicating in a way that our brains have already been wired to understand.

Many cases of todays narrow applications of AI are focused on human communication. If a computer can understand what a human means when they communicate, we can create all manner of applications of practical value from chatbots and conversational agents to systems that can read what we write in our documents and emails and even systems that can accurately translate from one human language to another without losing meaning and context.

Machine to human, machine to machine, and human to machine interactions are all examples of how AI communicates and understands human communication. Some real-life examples include voice assistants, content generation, chatbots, sentiment analysis, mood analysis, and intent analysis, and also machine powered translation. The applications of the conversational pattern are so broad that entire market sectors are focused on the use of AI-enabled conversational systems, from conversational finance to telemedicine and beyond. Beyond simply understanding written or spoken language, the power of the conversational pattern of AI can be seen in machine ability to understand sentiment, mood, and intent, or take visual gestures and translate them into machine understandable forms.

Natural Language Processing: evolving over the past few decades

Accurately processing and generating human language is particularly complicated, with constant technology evolution happening over the past sixty years. One of the easier to solve problems is the conversion of audio waveforms into machine readable text, known as Automatic Speech Recognition (ARS). While ASR is somewhat complicated to implement, it doesnt need machine learning or AI capabilities generally, and some fairly accurate speech-to-text technologies have been around for decades. Speech-to-text is not natural language understanding. While the computer is transcribing what the human is saying, it is taking waveforms that it understands and converting them to words. It is not interpreting the data it is hearing.

The inverse capability, text-to-speech, also doesnt require much in the way of machine learning or AI to be performed. Text-to-speech is simply the generation of waveforms by the computer to speak words that are already known. There is no understanding of the meaning of those words when simply using text-to-speech. The technology behind text to speech has been around for years, you can hear it in the movie War Games (1983): would you like to play a game?

However, speech-to-text and text-to-speech isnt where AI and machine learning are needed, even though machine learning has helped text-to-speech become more human sounding, and speech-to-text more accurate. Natural language processing (NLP) involves more than translation of waveforms and generation of audio waveforms. Just because you have text doesnt mean that machines can understand it. To gain that understanding, machines need to be able to understand and generate parts of speech, extract and understand entities, determine meanings of words, and use much more complicated processing activities to connect together concepts, phrases, concepts, and grammar into the larger picture of intent and meaning.

Natural language processing consists of two parts: natural language understanding and natural language generation. Natural language understanding is where a computer interprets human input such as voice or text and can translate that into something the machine is capable of using in the intended manner. Natural language understanding consists of many subdomains in trying to understand intent from text generated from audio waveforms or typed by humans in text-mode interactions such as chatbots or messaging interfaces. AI is applied to lexical parsing to understand grammar rules and break sentences into structural components. Regardless of the approach used, most natural-language-understanding systems share some common components. Then, once the components are identified, each piece can be semantically understood to interpret words based on context and word order. Further logical analysis and deduction can be used to determine meaning based on what the various parts are referring to, using knowledge graphs and other Methods to deduce meaning.

Natural language generation is the process of the AI being able to prepare communication for humans in any form that is natural and does not sound like it was made by a computer. In order for a computer process to be considered natural language generation the computer actually has to interpret content and understand its meaning for effective communication. This involves the reverse of many of the steps identified in natural language understanding, taking concepts and generating human-understandable conversations from how the machine understands the way humans communicate.

Why is machine-facilitated conversation so important?

When it comes down to the pattern of human and computer communication, it is receiving so much focus because our interactions with systems can be very difficult at times. Typing or swiping can take time and not communicate our needs properly while reading static content like an FAQ might not be helpful for most customers. People want to interact with machines efficiently and effectively. Many user interfaces are quite suboptimal for human interaction, requiring confusing menu interaction, interactive voice response systems that are too simplistic, or rules-based chatbots that fail to satisfy user needs.

Development of more intelligent conversational systems goes back decades, with the ELIZA chatbot first developed in 1966 as an illustration of the possibilities of machine-mediated conversation. Nowadays, users are more familiar with voice assistants such as Alexa, Google Assistant, Apple Siri, Microsoft Cortana, and web-based chatbots. However, if youve interacted with any of them recently, they still are lacking in understanding in many significant ways. Theres no doubt that much of the work of AI researchers is going into improving the ways that machines can understand and generate human language and thus reinforce the power of those applications that leverage the conversational pattern of AI.

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Machines That Can Understand Human Speech: The Conversational Pattern Of AI - Forbes

A survey suggests that 58% organisations have embedded at least 1 AI capability in their processes or their product. With the graph leaning more heavily towards AI adoption, we can agree that it is the inevitable future and why shouldnt it be? AI processes have helped the workforce tremendously in terms of efficiency and quicker updates. AI that has been launched in product or service segments have also seen a welcoming response across industries.

Even during this pandemic, AI capabilities have shown wonders in the healthcare, fintech and communication segments. AI technology has been particularly helpful in gathering data about hotspots and identifying what areas the authorities need to work on. Adding to this, robots have found fame in hospitals for sanitising the premises and serving food to infected persons.

While the adoption of AI into business processes and products may have become mainstream, there are still a lot of companies that struggle with adapting to it.

One thing that companies need to realise is that AI is an integral part of their business. There cannot be a business strategy that doesnt account for it. Since AI capabilities are embedded products in your existing technology, you need to build your strategy around it. Choose the areas of your business that need AI the most and invest in those areas.

Core processes such as Customer Relationship management also involve the use of AI and would reap you big benefits. Chatbots and virtual assistants would be useful for almost any organisation that is looking to give their customers a real-life experience digitally. Your strategy would fall flat if you include AI in just one aspect of its being.

Lack of clarity arises in two situations- when there isnt enough knowledge about AI or when you havent trained your employees well. After decades of research and experimentation, we are finally at a stage where AI algorithms are working well. You must train your employees to understand AI and work alongside it.

When you introduce change, there is always resistance. It is just how the human psyche works. The comfort with familiarity, no matter how bad, is easier than change. To make your employees more comfortable, you need to first resolve their misunderstandings about AI technology. They might have concerns over their own jobs becoming redundant due to AI or misconception about its complexity in usage. You need to address these and help them adapt to new technology.

While AI applications have come a long way with successful algorithms, there will arise ethical concerns. These concerns are quite big and can be affecting your decision to introduce AI into your business. Sometimes, you might not be using it to its full potential due to ethical concerns which would be almost like not using it all.

However, a lot of companies that are giving AI as a service are rolling out new policies which are in accordance with ethical AI expectations. As a business, you can also ensure that you feed it with the right data so it does not learn biases. Be sure to introduce an independent set of policies and guidelines for using AI technology.

Summing Up: AI is your competitive advantage

Companies are already leveraging AI in various aspects of their business which is making them understand consumers better. To be a part of the competition, an organisation will have to eventually adapt AI or there will be no competition at all. Once they are using the same level of technology, how you choose to personalise your AI models will define your customer success.

If you have an interesting article / experience / case study to share, please get in touch with us at [emailprotected]

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How Can Companies Overcome Challenges That Come With AI Adoption? - Express Computer

Google comes up with some handy features to facilitate its users. Google sheet is testing an artificial intelligence (AI) based smart fill to ease the annoying job of filling every cell in the sheet. This feature will facilitate the user by auto-filling the cell. The intelligence of the feature will auto-fill the cells. It will learn the pattern and fill each cell. In simple words, this feature helps in auto-filling the data and reduce the work burden from the user.

For instance, a user is willing to create a file that has a full name column in it. As the user will start typing the first name, the software will perceive the pattern. Once the software has learned the pattern, it will initiate a formula and auto-fill the rest of the columns, reducing the work burden on the user. It will make the data consistent and error-free.

Not only this but the smart compose will let the user minimize mistakes and produce even effective content. It will delete the repeated words; reduce spelling errors and grammatical errors. Once you have imported the data, Data Clean up panel pops up. This panel suggests you remove repeated phrases, clear rows, and correct formatting issues.

When you are done with the formatting and correcting you can go through the entire document. Furthermore, the smart features of the sheet highlight the data in such a way that you can come up with a summary in no time. Such as, it will highlight the most repeated value; you can easily analyze the data and jump to the conclusion.

This edition comes with a very smart feature that can detect the natural voice, analyze it, and answers it. For example, you made a sheet regarding students. You can simply ask Which student has the highest score? The smart feature will detect the voice, evaluate it and the sheet will answer the question. Isnt it amazing?

However, currently, G Suite customers cannot experience this new feature. They have to wait for next year.

Two years ago, Google launched the Quick Access tool. It was a machine learning-powered suggestive tool, which users could use for editing sheets, slides, and docs. Latter Google rolled out a new feature of auto-correcting content in Spanish. Before this, the autocorrect was only available in English.

Read next: Google Photos Will No Longer Create An Automatic Backup Of Social Media Folders

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Google Sheet Comes Up With A New Feature AI-Backed Smart Fill and Smart Clean Up That Will Make The Data Error Free And Consistent - Digital...

Vendors of artificial intelligence technology should not be shielded by intellectual property claims and will have to disclose elements of their designs and be able to explain how their offering works in order to establish accountability, according to a leading official from the Cybersecurity and Infrastructure Security Agency.

I dont know how you can have a black-box algorithm thats proprietary and then be able to deploy it and be able to go off and explain whats going on, said Martin Stanley, a senior technical advisor who leads the development of CISAs artificial intelligence strategy. I think those things are going to have to be made available through some kind of scrutiny and certification around them so that those integrating them into other systems are going to be able to account for whats happening.

Stanley was among the speakers on a recent Nextgov and Defense One panel where government officials, including a member of the National Security Commission on Artificial Intelligence, shared some of the ways they are trying to balance reaping the benefits of artificial intelligence with risks the technology poses.

Experts often discuss the rewards of programming machines to do tasks humans would otherwise have to labor onfor both offensive and defensive cybersecurity maneuversbut the algorithms behind such systems and the data used to train them into taking such actions are also vulnerable to attack. And the question of accountability applies to users and developers of the technology.

Artificial intelligence systems are code that humans write, but they exercise their abilities and become stronger and more efficient using data that is fed to them. If the data is manipulated, or poisoned, the outcomes can be disastrous.

Changes to the data could be things that humans wouldnt necessarily recognize, but that computers do.

Weve seen ... trivial alterations that can throw off some of those results, just by changing a few pixels in an image in a way that a person might not even be able to tell, said Josephine Wolff, a Tufts University cybersecurity professor who was also on the panel.

And while its true that behind every AI algorithm is a human coder, the designs are becoming so complex, that youre looking at automated decision-making where the people who have designed the system are not actually fully in control of what the decisions will be, Wolff says.

This makes for a threat vector where vulnerabilities are harder to detect until its too late.

With AI, theres much more potential for vulnerabilities to stay covert than with other threat vectors, Wolff said. As models become increasingly complex it can take longer to realize that something is wrong before theres a dramatic outcome.

For this reason, Stanley said an overarching factor CISA uses to help determine what use cases AI gets applied to within the agency, is to assess the extent to which they offer high benefits and low regrets.

We pick ones that are understandable and have low complexity, he said.

Among other things federal personnel need to be mindful of is who has access to the training data.

You can imagine you get an award done, and everyone knows how hard that is from the beginning, and then the first thing that the vendor says is OK, send us all your data, hows that going to work so we can train the algorithm? he said. Those are the kinds of concerns that we have to be able to address.

Were going to have to continuously demonstrate that we are using the data for the purpose that it was intended, he said, adding, Theres some basic science that speaks to how you interact with algorithms and what kind of access you can have to the training data. Those kinds of things really need to be understood by the people who are deploying them.

A crucial but very difficult element to establish is liability. Wolff said ideally, liability wouldbe connected to a potential certification program where an entity audits artificial intelligence systems for factors like transparency and explainability.

Thats important, she said, for answering the question of how can we incentivize companies developing these algorithms to feel really heavily the weight of getting them right and be sure to do their own due diligence knowing that there are serious penalties for failing to secure them effectively.

But this is hard, even in the world of software development more broadly.

Making the connection is still very unresolved. Were still in the very early stages of determining what would a certification process look like, who would be in charge of issuing it, what kind of legal protection or immunity might you get if you went through it, she said. Software developers and companies have been working for a very long time, especially in the U.S., under the assumption that they cant be held legally liable for vulnerabilities in their code, and when we start talking about liability in the machine learning and AI context, we have to recognize that thats part of what were grappling with, an industry that for a very long time has had very strong protections from any liability.

View from the Commission

Responding to this, Katharina McFarland, a member of the National Security Commission on Artificial Intelligence, referenced the Pentagons Cybersecurity Maturity Model Certification program.

The point of the CMMC is to establish liability for Defense contractors, Defense Acquisitions Chief Information Security Officer Katie Arrington has said. But McFarland highlighted difficulties facing CMMC that program officials themselves have acknowledged.

Im sure youve heard of the [CMMC], theres a lot of thought going on, the question is the policing of it, she said. When you consider the proliferation of the code thats out there, and the global nature of it, you really will have a challenge trying to take a full thread and to pull it through a knothole to try to figure out where that responsibility is. Our borders are very porous and machines that we buy from another nation may not be built with the same biases that we have.

McFarland, a former head of Defense acquisitions, stressed that AI is more often than not viewed with fear and said she wanted to see more of a balance in procurement considerations for the technology.

I found that we had a perverse incentive built into our system and that was that we took, sometimes, I think extraordinary measures to try to creep into the one percent area for failure, she said, In other words, we would want to 110% test a system and in doing so, we might miss the venue of where its applicability in a theater to protect soldiers, sailors, airmen and Marines is needed.

She highlighted upfront a need for testing a verification but said it shouldnt be done at the expense of adoption. To that end, she asks that industry help by sharing the testing tools they use.

I would encourage industry to think about this from the standpoint of what tools would we needbecause theyre using themin the department, in the federal space, in the community, to give us transparency and verification, she said, so that we have a high confidence in the utility, in the data that were using and the AI algorithms that were building.

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Artificial Intelligence Systems Will Need to Have Certification, CISA Official Says - Nextgov

While artificial intelligence systems continue to make huge strides forward, they're still not particularly good at dealing with chaos or unpredictability. Now researchers think they have found a way to fix this, by teaching AI about physics.

To be more specific, teaching them about the Hamiltonian function, which gives the AI information about the entirety of a dynamic system: all the energy contained within it, both kinetic and potential.

Neural networks, designed to loosely mimic the human brain as a complex, carefully weighted type of AI, then have a 'bigger picture' view of what's happening, and that could open up possibilities for getting AI to tackle harder and harder problems.

"The Hamiltonian is really the special sauce that gives neural networks the ability to learn order and chaos," says physicist John Lindner, from North Carolina State University.

"With the Hamiltonian, the neural network understands underlying dynamics in a way that a conventional network cannot. This is a first step toward physics-savvy neural networks that could help us solve hard problems."

The researchers compare the introduction of the Hamiltonian function to a swinging pendulum it's giving AI information about how fast the pendulum is swinging and its path of travel, rather than just showing AI a snapshot of the pendulum at one point in time.

If neural networks understand the Hamiltonian flow so where the pendulum is, in this analogy, where it might be going, and the energy it has then they are better able to manage the introduction of chaos into order, the new study found.

Not only that, but they can also be built to be more efficient: better able to forecast dynamic, unpredictable outcomes without huge numbers of extra neural nodes. It helps AI to quickly get a more complete understanding of how the world actually works.

A representation of the Hamiltonian flow, with rainbow colours coding a fourth dimension. (North Carolina State University)

To test their newly improved AI neural network, the researchers put it up against a commonly used benchmark called the Hnon-Heiles model, initially created to model the movement of a star around a sun.

The Hamiltonian neural network successfully passed the test, correctly predicting the dynamics of the system in states of order and of chaos.

This improved AI could be used in all kinds of areas, from diagnosing medical conditions to piloting autonomous drones.

We've already seen AI simulate space, diagnose medical problems, upgrade movies and develop new drugs, and the technology is, relatively speaking, just getting started there's lots more on the way. These new findings should help with that.

"If chaos is a nonlinear 'super power', enabling deterministic dynamics to be practically unpredictable, then the Hamiltonian is a neural network 'secret sauce', a special ingredient that enables learning and forecasting order and chaos," write the researchers in their published paper.

The research has been published in Physical Review E.

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Artificial Intelligence Can't Deal With Chaos, But Teaching It Physics Could Help - ScienceAlert

A few months ago, NASA unveiled its next-generation space suit that will be worn by astronauts when they return to the moon in 2024 as part of the agencys plan to establish a permanent human presence on the lunar surface. The Extravehicular Mobility Unitor xEMUis NASAs first major upgrade to its space suit in nearly 40 years and is designed to make life easier for astronauts who will spend a lot of time kicking up moon dust. It will allow them to bend and stretch in ways they couldnt before, easily don and doff the suit, swap out components for a better fit, and go months without making a repair.

But the biggest improvements werent on display at the suits unveiling last fall. Instead, theyre hidden away in the xEMUs portable life-support system, the astro backpack that turns the space suit from a bulky piece of fabric into a personal spacecraft. It handles the space suits power, communications, oxygen supply, and temperature regulation so that astronauts can focus on important tasks like building launch pads out of pee concrete. And for the first time ever, some of the components in an astronaut life-support system will be designed by artificial intelligence.

Jesse Craft is a senior design engineer at Jacobs, a major engineering firm based in Dallas that was tapped by NASA to revamp the xEMU life-support system. For Craft and the hundreds of other engineers working on the project, this requires a careful balancing act between competing priorities. The life-support system has to be safe, obviously, but it also has to be light enough to fit the weight limits for the lunar lander and strong enough to withstand the intense g-forces and vibrations it will experience during a rocket launch. Its a really big engineering challenge, says Craft.

Squeezing more stuff into less space with reduced mass is the kind of complex optimization problem that aerospace engineers deal with all the time. But NASA wants boots on the moon by 2024, and meeting that aggressive timeline meant that Craft and his colleagues couldnt spend weeks debating the ideal shape of each widget. Instead, theyre piloting a new AI-fueled design software that can rapidly come up with new component designs.

We consider AI to be a technology that can do something faster and better than a trained human can do, says Jesse Coors-Blankenship, the vice president of technology at PTC, the American company that made the software. Some of the software technologies are things engineers are already familiar with, like structural simulation and optimization. But with AI, we can do it faster. This approach to engineering is known as generative design. The basic idea is to feed the software a set of requirements for a components maximum size, the weight it has to bear, or the temperatures it will be exposed to and let the algorithms figure out the rest.

PTCs software combines several different approaches to AI, like generative adversarial networks and genetic algorithms. A generative adversarial network is a game-like approach in which two machine-learning algorithms face off against one another in a competition to design the most optimized component. Its the same technique used to generate photos of people who dont exist. Genetic algorithms, by contrast, are analogous to natural selection. They generate multiple designs, combine them, and then take the best designs of the new generation and repeat. In the past, NASA has used genetic algorithms to design optimaland bizarreantennas.

The machines iterative process is 100 or 1,000 times more than we could do on our own, and it comes up with a solution that is ideally optimized within our constraints, says Craft. Its especially helpful given that the final design of the space suit life-support system is still in flux. Even a small change to the requirements in the future could result in weeks of wasted work by engineers.

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NASAs New Moon-Bound Space Suits Will Get a Boost From AI - WIRED

Beyond the thriving major cities of Beijing, Shanghai and Shenzhen, efforts to develop successful innovation hubs are also underway in other regions. A promising example is the city of Hangzhou, in Zhejiang Province, which has established an AI Town, clustering together the tech company Alibaba, Zhejiang University and local businesses to work collaboratively on AI development.

Chinas accelerating AI innovation deserves the worlds full attention, but it is unhelpful to reduce all the many developments into a simplistic narrative about China as a threat or a villain. Observers outside China need to engage seriously with the debate and make more of an effort to understand and learn from the nuances of whats really happening.

(This article was first published on The Conversation and has been republished with their permission).

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China & AI: What the World Can Learn And What It Should Be Wary of - The Quint

Jul 4th 2020

Artificial intelligence is an oxymoron (Technology quarterly, June 13th). Intelligence is an attribute of living things, and can best be defined as the use of information to further survival and reproduction. When a computer resists being switched off, or a robot worries about the future for its children, then, and only then, may intelligence flow.

I acknowledge Richard Suttons bitter lesson, that attempts to build human understanding into computers rarely work, although there is nothing new here. I was aware of the folly of anthropomorphism as an AI researcher in the mid-1980s. We learned to fly when we stopped emulating birds and studied lift. Meaning and knowledge dont result from symbolic representation; they relate directly to the visceral motives of survival and reproduction.

Great strides have been made in widening the applicability of algorithms, but as Mr Sutton says, this progress has been fuelled by Moores law. What we call AI is simply pattern discovery. Brilliant, transformative, and powerful, but just pattern discovery. Further progress is dependent on recognising this simple fact, and abandoning the fancy that intelligence can be disembodied from a living host.

ROB MACDONALDRichmond, North Yorkshire

I agree that machine learning is overhyped. Indeed, your claim that such techniques are loosely based on the structure of neurons in the brain is true of neural networks, but these are just one type among a wide array of different machine- learning methods. In fact, machine learning in some cases is no more than a rebranding of existing processes. If by machine learning we simply mean building a model using large amounts of data, then good old ordinary least squares (line of best fit) is a form of machine learning.

TOM ARMSTRONGToronto

The scope of your research into green investing was too narrow to condemn all financial services for their woolly thinking (Hotting up, June 20th). You restricted your analysis to microeconomic factors and to the ability of investors to engage with companies. It overlooked the bigger picture: investors can also shape the macro environment by structured engagement with the system itself.

For example, the data you used largely originated from the investor-led Carbon Disclosure Project (for which we hosted the first ever meeting, nearly two decades ago). In addition, investors have also helped shape sustainable-finance plans in Britain, the EU and UN. Investors also sit on the industry-led Taskforce on Climate-related Financial Disclosure, convened by the Financial Stability Board, which has proved effective.

It is critical that governments apply a meaningful carbon price. But if we are to move money at the pace and scale required to deal with climate risk, governments need to reconsider the entire architecture of markets. This means focusing a wide-angled climate lens on prudential regulation, listing rules, accounting standards, investor disclosure standards, valuation conventions and stewardship codes, as well as building on new interpretations of legal fiduciary duty. This work is done most effectively in partnership with market participants. Green-thinking investors can help.

STEVE WAYGOODChief responsible investment officerAviva InvestorsLondon

Estimating indirectly observable GDP in real time is indeed a hard job for macro-econometricians, or wonks, as you call us (Crisis measures, May 30th). Most of the components are either highly lagged, as your article mentioned, or altogether unobservable. But the textbook definition of GDP and its components wont be changing any time soon, as the reader is led to believe. Instead what has always and will continue to change are the proxy indicators used to estimate the estimate of GDP.

MICHAEL BOERMANWashington, DC

Reading Lexingtons account of his garden adventures (June 20th) brought back memories of my own experience with neighbours in Twinsburg, Ohio, in the late 1970s. They also objected to vegetables growing in our front yard (the only available space). We were doing it for the same reasons as Lexington: pleasure, fresh food to eat, and a learning experience for our young children. The neighbours, recently arrived into the suburban middle class, saw it as an affront. They no longer had to grow food for their table. They could buy it at the store and keep it in the deep freeze. Our garden, in their face every day, reminded them of their roots in Appalachian poverty. They called us hillbillies.

Arthur C. Clarke once wrote: Any sufficiently advanced technology is indistinguishable from magic. Our version read, Any sufficiently advanced lifestyle is indistinguishable from hillbillies.

PHILIP RAKITAPhiladelphia

Bartleby (May 30th) thinks the benefits of working from home will mean that employees will not want to return to the office. I am not sure that is the case for many people. My husband is lucky. He works for a company that already expected its staff to work remotely, so had the systems and habits in place. He has a spacious room to work in, with an adjustable chair, large monitor and a nice view. I do not work so he is not responsible for child care or home schooling.

Many people are working at makeshift workspaces which would make an occupational therapist cringe. Few will have a dedicated room for their home office, so their work invades their mental and physical space.

My husband has noticed that meetings are being set up both earlier and later in the day because there is an assumption that, as people are not commuting, it is fine to extend their work day. Colleagues book a half-hour meeting instead of dropping by someones desk to ask a quick question. Any benefit of not commuting is lost. My husband still struggles to finish in time to have dinner with our children. People with especially long commutes now have more time, but even that was a change of scenery and offered some incidental exercise.

JENNIFER ALLENLondon

As Bartleby pointed out, the impact of pandemic working conditions wont be limited to the current generation. By exacerbating these divides, will covid-19 completely guarantee a future dominated by the baby-Zoomers?

MALCOLM BEGGTokyo

The transition away from the physical office engenders a lackadaisical approach to the work day for many workers. It brings to mind Ignatius Reillys reasoning for his late start at the office from A Confederacy of Dunces:

I avoid that bleak first hour of the working day during which my still sluggish senses and body make every chore a penance. I find that in arriving later, the work which I do perform is of a much higher quality.

ROBERT MOGIELNICKIArlington, Virginia

This article appeared in the Letters section of the print edition under the headline "On artificial intelligence, green investing, GDP, gardens, working from home"

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Letters to the editor - The Economist

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Artificial Intelligence (AI) in Agriculture Market: Technological Innovations and Analysis Till 2030 - Cole of Duty