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This article is part of our series that explores thebusiness of artificial intelligence

Applied artificial intelligence tops the list of 14 most influential technology trends in McKinsey & Companys Technology Trends Outlook 2022 report.

For now, applied AI (which might also be referred to as enterprise AI) is mainly the use of machine learning and deep learning models in real-world applications. A closely related trend that also made it to McKinseys top-14 list is industrializing machine learning, which refers to MLOps platforms and other tools that make it easier to train, deploy, integrate, and update ML models in different applications and environments.

McKinseys findings, which are in line with similar reports released by consulting and research firms, show that after a decade of investment, research, and development of tools, the barriers to applied AI are slowly fading.

Large tech companies, which often house many of the top machine learning/deep learning scientists and engineers, have been researching new algorithms and applying them to their products for years. Thanks to the developments highlighted in McKinseys report, more organizations can adopt machine learning models in their applications and bring their benefits to their customers and users.

The recent decade has seen a revived and growing mainstream interest in artificial intelligence, mainly thanks to the proven capabilities of deep neural networks in performing tasks that were previously thought to be beyond the limits of computers. During the same period, the machine learning research community has made very impressive progress in some of the challenging areas of AI, including computer vision and natural language processing.

The scientific breakthroughs in machine learning were largely made possible because of the growing capabilities to collect, store, and access data in different domains. At the same time, advances in processors and cloud computing have made it possible to train and run neural networks at speeds and scales that were previously thought to be impossible.

Some of the milestone achievements of deep learning were followed by news cycles that publicized (and often exaggerated) the capabilities of contemporary AI. Today, many companies try to present themselves as AI first, or pitch their products as using the latest and greatest in deep learning.

However, bringing ML from research labs to actual products presents several challenges, which is why most machine learning strategies fail. Creating and maintaining products that use machine learning requires different infrastructure, tools, and skill sets than those used in traditional software. Organizations need data lakes to collect and store data, and data engineers to set up, maintain, and configure the data infrastructure that makes it possible to train and update ML models. They need data scientists and ML engineers to prepare the data and models that will power their applications. They need distributed computing experts that can make ML models run in a time- and cost-efficient manner and at scale. And they need product managers who can adapt the ML system to their business model and software engineers who can integrate the ML pipeline into their products.

The data, hardware, and talent costs that come with enterprise AI have been often too prohibitive for smaller organizations to make long-term investments in ML strategies.

It is against this backdrop that the McKinsey & Company reports findings are worth examining.

The report ranks tech trends based on five quantifiable measures: search engine queries, news publications, patents, research publications, and investment. It is worth noting that such quantitative measures dont always paint the most accurate picture of the relevance of a trend. But tracking them over time can give a good estimate of how a technology goes through the different steps of hype, adoption, and productivity cycle.

McKinsey further corroborated its findings through surveys and interviews with experts from 20 different industries, which gives a better picture of what the opportunities and challenges are.

The report is based on 2018-2021 data, which does not fully account for the downturn that capital markets are currently undergoing. According to the findings, applied AI has seen growth in all quantifiable measures except for the search engine queries category (which is a grey area, since AI terms and trends are constantly evolving). McKinsey gives applied AI the highest innovation score and top-five investment score with $165 billion in 2021.

(Measuring investment is also very subjective and depends on how you define applied AIe.g., if a company that secures a huge round of funding uses machine learning as a small part of its product, will it count as an investment in applied AI?)

In terms of industry relevance, some of the ML applications mentioned in the report include use cases such as recommendation engines (e.g., content recommendation, smart upselling), detection and prevention (e.g., credit card fraud detection, customer complaint modeling, early disease diagnosis, defect prediction), and time series analysis (e.g., managing price volatility, demand forecasting). Interestingly, these are some of the areas of machine learning where the algorithms have been well-developed for years. Though computer vision is only mentioned once in the use cases, some of the applications might benefit from it (e.g., document scanning, equipment defect detection).

The report also mentions some of the more advanced areas of machine learning, such as generative deep learning models (e.g., simulation engines for self-driving cars, generating chemical compounds), transformer models (e.g., drug discovery), graph neural networks, and robotics.

This further drives the point that the main hurdle for the adoption of applied AI has not been poor machine learning algorithms but the lack of tooling and infrastructure to put well-known and -tested algorithms to efficient use. These constraints have limited the use of applied AI to companies that dont have enormous resources and access to scarce machine learning talent.

In recent years, there has been tremendous advances in some of these fronts. Weve seen the advent and maturity of no-code ML platforms, easy-to-use ML programming libraries, API-based ML services (MLaaS), and special hardware for training and running ML models. At the same time, the data storage technologies underlying ML services have evolved to become more flexible, interoperable, and scalable. Meanwhile, some enterprise AI companies have started to develop and provide ML solutions for specific sectors (e.g., financial services, oil and gas, retail).

All these developments reduce the financial and technical barriers to adopting machine learning in their business models. In many cases, companies can integrate ML services into their applications without having in-depth knowledge of the algorithms running in the background.

According to McKinseys 2021 survey of industry experts, 56 percent of respondents said their organizations had adopted AI, up from 50 percent in the 2020 survey. The 2021 survey also indicated that adopting AI can have financial benefits: 27 percent of respondents attributed 5 percent or more of their companies EBIT to AI.

The second AI-related tech trend included in the McKinsey & Company report is the industrialization of machine learning. This is a vague term and has much overlap with the applied AI category, so the report defines it as an interoperable stack of technical tools for automating ML and scaling up its use so that organizations can realize its full potential.

The technologies underlying advances in this field are mostly the same that have led to the growth of applied AI (better data storage platforms, hardware stacks, ML development tools and platforms, etc.). However, one specific field that has seen impressive developments in recent years is machine learning operations (MLOps), the set of tools and practices that streamline the training, deployment, and maintenance of ML models.

MLOps platforms provide tools for curating, processing, and labeling data; training and comparing different machine learning models; versioning control for dataset and models; deploying ML models and monitoring their performance; and updating ML models as their performance decays, their environment changes, and new data becomes available. MLOps platforms, which are growing in number and maturity, bring together several different tasks that were previously carried out desperately and in an ad hoc fashion.

According to the report, the industrialization of machine learning can shorten the production time frame for ML applications by 90 percent (from proof of concept to product) and reduce development resources by up to 40 percent.

Despite the advances in applied AI, the field still has some gaps to bridge. The McKinsey report states that the availability of resources such as talent and funding remain two of the hurdles for the further growth of enterprise AI. Currently, the capital markets are in a downturn, and all sectors, including AI, are facing problems funding their startups and companies.

However, despite the AI capital pie becoming smaller, funding has not stopped altogether. According to a recent CB Insights report, companies that have already achieved product/market fit and are ready for aggressive growth are still managing to secure mega-funding rounds (above $100 million). This suggests that companies that dont have the margins to launch new ML strategies will have a hard time receiving outside funding. But applied ML platforms that have already cornered their share of the market will continue to draw interest from investors.

Another important challenge that the report mentions is data risks and vulnerabilities. This is becoming an increasingly critical issue for applied machine learning. Like its development lifecycle, the security threat landscape of machine learning is different from that of traditional software. The security tools used in most software development platforms are not designed to detect adversarial examples, data poisoning, membership inference attacks, and other types of threats against ML models.

Fortunately, the security and machine learning communities are coming together to develop tools and practices for creating secure ML pipelines. As applied AI continues to grow, we can expect other sectors to speed up their adoption of ML, which will in turn further accelerate the pace of innovation in the field.

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The growth stage of applied AI and MLOps - TechTalks

For those who choose to let their cats live a more or less free-range life, there are usually two choices. One, you can adopt the role of servant and run for the door whenever the cat wants to get back inside from their latest bird-murdering jaunt. Or two, install a cat door and let them come and go as they please, sometimes with a present for you in their mouth. Heads you win, tails you lose.

Theres another way, though: just let the cat ask to be let back in. Thats the approach that [Tennis Smith] took with this machine-learning kitty doorbell. Its based on a Raspberry Pi 4, which lives inside the house, and a USB microphone thats outside the front door. The Pi uses Tensorflow Lite to classify the sounds it picks up outside, and when one of those sounds fits the model of a cats meow, a message is dispatched to AWS Lambda. From there a text message is sent to alert [Tennis] that the cat is ready to come back in.

Theres a ton of useful information included in the repo for this project, including step-by-step instructions for getting Amazon Web Services working on the Pi. If youre a dog person, fear not: changing from meows to barks is as simple as tweaking a single line of code. And if youd rather not be at the beck and call of a cat but still want to avoid the evidence of a prey event on your carpet, machine learning can help with that too.

[via Toms Hardware]

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Machine Learning Gives Cats One More Way To Control Their Humans - Hackaday

The last century alone has seen a meteoric increase in the accumulation of data and we are able to store unfathomable quantities of information to help us solve problems known and unknown. At some point the ability to optimally utilise these vast amounts of data will be beyond our reach, but not beyond that of the tools we have made. At the North-West University (NWU), Professor Marelie Davel, director of the research group MUST Deep Learning, and her team are ensuring that our ever-growing data repositories will continue to benefit society.

The teams focus on machine learning and, specifically, deep learning, is creating magic to the untrained eye. Here is why.

Machine learning is a catch-all term for systems that learn in an automated way from their environment. These systems are not programmed with the steps to solve a specific task, but they are programmed to know how to learn from data. In the process, the system uncovers the underlying patterns in the data and comes up with its own steps to solve the specific task, explains Professor Davel.

According to her, machine learning is becoming increasingly important as more and more practical tasks are being solved by machine learning systems: From weather prediction to drug discovery to self-driving cars. Behind the scenes we see that many of the institutions we interact with, like banks, supermarket chains and hospitals, all nowadays incorporate machine learning in aspects of their business. Machine learning makes everyday tools from internet searches to every smartphone photo we take work better.

The NWU and MUST go a step beyond this by doing research on deep learning. This is a field of machine learning that was originally inspired by the idea of artificial neural networks, which were simple models of how neurons were thought to interact in the human brain. This was conceived in the early forties! Modern networks have come a long way since then, with increasingly complex architectures creating large, layered models that are particularly effective at solving human-like tasks, such as processing speech and language, or identifying what is happening in images.

She explains that, although these models are very well utilised, there are still surprisingly many open questions about how they work and when they fail.

We work on some of these open questions, specifically on how the networks perform when they are presented with novel situations that did not form part of their training environment. We are also studying the reasons behind the decisions the networks make. This is important in order to determine whether the steps these models use to solve tasks are indeed fair and unbiased, and sometimes it can help to uncover new knowledge about the world around us. An example is identifying new ways to diagnose and understand a disease.

The uses of this technology are nearly boundless and will continue to grow, and that is why Professor Davel encourages up-and-coming researchers to consider focusing their expertise in this field.

By looking inside these tools, we aim to be better users of the tools as well. We typically apply the tools with industry partners, rather than on our own. Speech processing for call centres, traffic prediction, art authentication, space weather prediction, even airfoil design. We have worked in quite diverse fields, but all applications build on the availability of large, complex data sets that we then carefully model. This is a very fast-moving field internationally. There really is a digital revolution that is sweeping across every industry one can think of, and machine learning is a critical part of it. The combination of practical importance and technical challenge makes this an extremely satisfying field to work in.

She confesses that, while some of the ideas of MUSTs collaborators may sound far-fetched at first, the team has repeatedly found that if the data is there, it is possible to build a tool to use it.

One can envision a future where human tasks such as speech recognition and interaction have been so well mimicked by these machines, that they are indistinguishable from their human counterparts. The famed science fiction writer Arthur C Clarke once remarked that any sufficiently advanced technology is indistinguishable from magic. At the NWU, MUST is doing their part in bringing this magic to life. DM

Author: Bertie Jacobs

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Machine and deep learning are a MUST at the North-West... - Daily Maverick

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do.

But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter a must be added to end of a word to make the masculine form feminine in Serbo-Croatian.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

The researchers trained and tested the model using problems from linguistics textbooks that featured 58 different languages. Each problem had a set of words and corresponding word-form changes. The model was able to come up with a correct set of rules to describe those word-form changes for 60 percent of the problems.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words. It is especially unique because the system discovers models that can be readily understood by humans, and it acquires these models from small amounts of data, such as a few dozen words. And instead of using one massive dataset for a single task, the system utilizes many small datasets, which is closer to how scientists propose hypotheses they look at multiple related datasets and come up with models to explain phenomena across those datasets.

One of the motivations of this work was our desire to study systems that learn models of datasets that is represented in a way that humans can understand. Instead of learning weights, can the model learn expressions or rules? And we wanted to see if we could build this system so it would learn on a whole battery of interrelated datasets, to make the system learn a little bit about how to better model each one, says Kevin Ellis 14, PhD 20, an assistant professor of computer science at Cornell University and lead author of the paper.

Joining Ellis on the paper are MIT faculty members Adam Albright, a professor of linguistics; Armando Solar-Lezama, a professor and associate director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; as well as senior author

Timothy J. ODonnell, assistant professor in the Department of Linguistics at McGill University, and Canada CIFAR AI Chair at the Mila -Quebec Artificial IntelligenceInstitute.

The research is published today in Nature Communications.

Looking at language

In their quest to develop an AI system that could automatically learn a model from multiple related datasets, the researchers chose to explore the interaction of phonology (the study of sound patterns) and morphology (the study of word structure).

Data from linguistics textbooks offered an ideal testbed because many languages share core features, and textbook problems showcase specific linguistic phenomena. Textbook problems can also be solved by college students in a fairly straightforward way, but those students typically have prior knowledge about phonology from past lessons they use to reason about new problems.

Ellis, who earned his PhD at MIT and was jointly advised by Tenenbaum and Solar-Lezama, first learned about morphology and phonology in an MIT class co-taught by ODonnell, who was a postdoc at the time, and Albright.

Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human. We wanted to see if we can emulate the kinds of knowledge and reasoning that humans (linguists) bring to the task, says Albright.

To build a model that could learn a set of rules for assembling words, which is called a grammar, the researchers used a machine-learning technique known as Bayesian Program Learning. With this technique, the model solves a problem by writing a computer program.

In this case, the program is the grammar the model thinks is the most likely explanation of the words and meanings in a linguistics problem. They built the model using Sketch, a popular program synthesizer which was developed at MIT by Solar-Lezama.

But Sketch can take a lot of time to reason about the most likely program. To get around this, the researchers had the model work one piece at a time, writing a small program to explain some data, then writing a larger program that modifies that small program to cover more data, and so on.

They also designed the model so it learns what good programs tend to look like. For instance, it might learn some general rules on simple Russian problems that it would apply to a more complex problem in Polish because the languages are similar. This makes it easier for the model to solve the Polish problem.

Tackling textbook problems

When they tested the model using 70 textbook problems, it was able to find a grammar that matched the entire set of words in the problem in 60 percent of cases, and correctly matched most of the word-form changes in 79 percent of problems.

The researchers also tried pre-programming the model with some knowledge it should have learned if it was taking a linguistics course, and showed that it could solve all problems better.

One challenge of this work was figuring out whether what the model was doing was reasonable. This isnt a situation where there is one number that is the single right answer. There is a range of possible solutions which you might accept as right, close to right, etc., Albright says.

The model often came up with unexpected solutions. In one instance, it discovered the expected answer to a Polish language problem, but also another correct answer that exploited a mistake in the textbook. This shows that the model could debug linguistics analyses, Ellis says.

The researchers also conducted tests that showed the model was able to learn some general templates of phonological rules that could be applied across all problems.

One of the things that was most surprising is that we could learn across languages, but it didnt seem to make a huge difference, says Ellis. That suggests two things. Maybe we need better methods for learning across problems. And maybe, if we cant come up with those methods, this work can help us probe different ideas we have about what knowledge to share across problems.

In the future, the researchers want to use their model to find unexpected solutions to problems in other domains. They could also apply the technique to more situations where higher-level knowledge can be applied across interrelated datasets. For instance, perhaps they could develop a system to infer differential equations from datasets on the motion of different objects, says Ellis.

This work shows that we have some methods which can, to some extent, learn inductive biases. But I dont think weve quite figured out, even for these textbook problems, the inductive bias that lets a linguist accept the plausible grammars and reject the ridiculous ones, he adds.

This work opens up many exciting venues for future research. I am particularly intrigued by the possibility that the approach explored by Ellis and colleagues (Bayesian Program Learning, BPL) might speak to how infants acquire language, says T. Florian Jaeger, a professor of brain and cognitive sciences and computer science at the University of Rochester, who was not an author of this paper. Future work might ask, for example, under what additional induction biases (assumptions about universal grammar) the BPL approach can successfully achieve human-like learning behavior on the type of data infants observe during language acquisition. I think it would be fascinating to see whether inductive biases that are even more abstract than those considered by Ellis and his team such as biases originating in the limits of human information processing (e.g., memory constraints on dependency length or capacity limits in the amount of information that can be processed per time) would be sufficient to induce some of the patterns observed in human languages.

This work was funded, in part, by the Air Force Office of Scientific Research, the Center for Brains, Minds, and Machines, the MIT-IBM Watson AI Lab, the Natural Science and Engineering Research Council of Canada, the Fonds de Recherche du Qubec Socit et Culture, the Canada CIFAR AI Chairs Program, the National Science Foundation (NSF), and an NSF graduate fellowship.

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AI that can learn the patterns of human language - MIT News

Photo courtesy of Society for Science

Despite conservationists efforts, animal poaching continues to devastate vulnerable species. So, when New Yorker Anika Puri came across ivory jewelry at a market in India four years ago, she felt inspired to do her part in stopping elephant hunting. The solution: she invented a low-cost machine learning software that can detect poachers in real time with 91% accuracy.

Discovering the numerous ivory objects in Mumbai was the catalyst for her project. I was quite taken aback because I always thought, Well, poaching is illegal, how come it really is still such a big issue? she says about the incident. So, the 17-year-old delved into the poaching numbers and discovered that Africa's forest elephant population declined by about 61% between 2002 and 2011, with numbers that continue to drop.

Poachers are usually detected by drones; however, Puri noticed the success rate could be significantly higher. I realized that we could use this disparity between these two movement patterns in order to actually increase the detection accuracy of potential poachers, she explains. As a result, Puri spent two years developing her solution: a machine learning software named ElSa (an abbreviation for Elephant Savior). It analyzes the movement patterns of humans and elephants in thermal infrared videos and is four times more accurate than the existing detection methods. Even better, the software can be used with low-cost cameras, eliminating the need for high-resolution thermal cameras.

Puri presented her project at the Regeneron Internation Science and Engineering Fair, winning the $10,000 Peggy Scripps Award for Science Communication and first place in the earth and environmental sciences category. It's quite remarkable that a high school student has been able to do something like this, Jasper Eikelboom, an ecologist at Wageningen University in the Netherlands, comments. Not only the research and the analysis but alsobeing able to implement it in the prototypes. Puri will be attending MIT in fall 2022 with hopes to expand her project to protect other endangered animal species.

h/t: [Smithsonian]

Meet the All-Female Anti-Poaching Team Changing the Face of Conservation in Africa [Interview]

Gorillas Pose for a Selfie with Virunga National Parks Anti-Poaching Rangers

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17-Year-Old Invents Software That Detects Elephant Poaching - My Modern Met