Category Archives: Machine Learning
AI vs. Machine Learning: Their Differences and Impacts – CIO Insight
Artificial intelligence (AI) vs. machine learning. Just the words can bring up visions of decision-making computers that are replacing whole departments and divisionsa future many companies believe is too far away to warrant investment. But the reality is, AI is here, and here to stay. And particularly at the enterprise level, a growing number of companies are tuning in to the productivity and promise of machines that can think for themselves.
In fact, a recent study by McKinsey showed that by 2019, venture capital investment in AI had already topped $18.5 billion. And IDC predicted that by 2023, global spending on AI and Machine Learning solutions will reach nearly $98 billion.
All this development promises to have a tremendous impact on every corner of industry. McKinsey recently released figures predicting that by 2030, 375 million workersabout 14 percent of the total global workforcewill need to switch occupations as robots and algorithms take over tasks once done by humans. Yet most analyses project net job gains as a result of AIlike this report from Gartner, which predicts that in the US, AI will displace as many as 1.8 million jobs in the near future, yet experience a net gain of at least 500,000 to two million new jobs as companies expand to absorb the new productivity.
So, with all that in mind, how do you understand dial back the AI vs. machine learning hype? And how should you be thinking about what cognitive computing can do for your business? Lets take a closer look.
Artificial intelligence is a computer system designed to think the way humans think. That means more than just doing one task well, like say, Alexa, who responds to your voice command to play your favorite song. True artificial intelligence has the ability to parse data, make decisions, and learn from those decisions to create something new.
AI has been famously used to tackle big problems, like testing drug compounds for curing cancer. Alibaba uses AI not just for predictive advertising on their sites, but also for monitoring cars and creating constantly changing traffic patterns, or helping farmers monitor crops to increase yield. Amazon Go is using AI to rethink the future of retail, creating unmanned convenient stores that monitor your shopping experience and charge you automatically when you walk out the door with an item.
Experimental AI has written novels (badly), played chess against world masters (very well), and parsed the worlds medical literature to help doctors make better and more complete diagnoses (and saved lives.) With AI platforms like Microsoft Azure, Google Cloud, and many others, developers now have the resources they need to think creatively about AI for their own businesses. Further, AI in the cloud significantly reduces a companys infrastructure costs for the massive computing capacity AI needs to be most useful.
Sometimes, machine learning is used interchangeably with artificial intelligence, but thats not quite correct. Machine learning is actually a subset of artificial intelligence. Machine learning refers to a program which does one task really well by parsing and analyzing data over time. It is only as good as the data flowing into it. However, examples of machine learning are all around us, from Alexa on our tabletops, to the dynamic pricing that goes up or down on a website based on your personal information, or the email that gets automatically filtered to your inbox, and the chatbot that responds when you ask a question on a website.
Artificial Intelligence has promise, and is becoming more feasible for companies to incorporate into their systems, says Sitima Fowler, vice president of marketing for national IT consulting firm Iconic IT. But she recommends most companies start small.
AI is trendy right now, definitely. But the reality is, most companies will be starting with machine learning, such as bots that parse their user traffic, for instance, to mine data. They might use it for chatbots on their website to direct consumer inquiries to the right information. From there, many companies can use the AI development tools available in the cloud from services like Amazon and Microsoft to develop AI that powers their consumer facing apps, and so much more. Were all very excited about the future of where artificial intelligence can take us. But its important to take it one step at a time, so the rest of your systems can integrate and keep up, Fowler said.
For example, at Iconic IT, we use AI to prevent cyber security breaches.Just simply installing an antivirus and email spam filter on your computer isnt enough. The bad guys have figured out ways around this software.So we incorporate AI on top of these software so it looks as the persons normal behavior and interactions with other people. Over time it learns a users email habits, communication styles, contacts to determine if a particular email is legitimate or potentially harmful, she added.
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AI vs. Machine Learning: Their Differences and Impacts - CIO Insight
Machine Learning Through The Lens of Econometrics – Analytics India Magazine
While we can predict house prices with accuracy, we cannot use such ML models to answer questions like whether one needs more dining rooms.
Artificial Intelligence has been a force of nature in many fields. From augmenting advancements in health and education to bridging gaps through speech recognition and translation AImachine intelligence is becoming more vital to us every day. Sendhil Mullainathan, a professor at the University of Chicago Booth School of Business, and Jann Spiess, an assistant professor at the Stanford Graduate School of Business, observed how machine learning, specifically supervised machine learning, was more empirical than it was procedural. For instance, face recognition algorithms do not use rigid rules to scan certain pixel recognitions. Au contraire, these algorithms utilise large datasets of photographs to predict how a face looks. This means that the machine would use the images to estimate a function f(x) that predicts the presence (y) of a face from pixels (x).
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Another discipline that heavily relies on such approaches is econometrics. Econometrics is the application of statistical procedures in economic data to provide empirical analysis on economic relationships. With machine learning being used on data for uses like forecasting, can empirical economists employ ML tools in their work?
Today, we see a considerable change in what constitutes the data individuals can work within. Machine learning enables statisticians and analysts to work with data considered too high dimensional for standard estimation methods, such as online posts and reviews, images, and language information. Statisticians could barely look at such data types for processes such as regression. In a 2016 study, however, researchers used images from Google Street View to measure block-level income in New York City and Boston. Moreover, a 2013 research developed a model to use online posts to predict the outcome of hygiene inspections. Thus, we see how machine learning can augment how we research today. Lets look at this in further detail.
Traditional estimation methods, like ordinary least squares (OLS), are already used to make predictions. So how does ML fit into this? To see this, we return to Sendhil Mullainathan and Jann Spiess workwhich was written in 2017, when the former taught and the latter was a PhD candidate at Harvard University. The paper took an example, predicting house prices, for which they selected ten thousand owner-occupied houses (chosen at random) from the 2011 American Housing Surveys metropolitan sample. They included 150 variables on the house and its location, such as the number of bedrooms. They used multiple tools (OLS and ML) to predict log unit values on a separate set of 41,808 housing unitsfor out-of-sample testing.
Applying OLS to this will require making specifically curated choices on which variables to include in the regression. Adding every interaction between variables (e.g. between base area and the number of bedrooms) is not feasible because that would consist of more regressors than data points. ML, however, searches for such interactions automatically. For instance, in regression trees, the prediction function would take the form of a tree that splits at each node, representing one variable. Such methods would allow researchers to build an interactive function class.
One problem here is that a tree with these many interactions would result in an overfiti.e. It would not be flexible enough to work with other data sets. This problem can be solved by something called regularisation. In the case of a regression tree, a tree of a certain depth will need to be chosen based on the tradeoff between a worse in-sample fit and a lower overfit. This level of regularisation will be selected by empirically tuning the ML algorithmby creating an out-of-sample experiment within the original sample.
Thus, picking the ML-based prediction function involves two steps: selecting the best loss-minimising function and finding the optimal level of complexity by empirically tuning it. Trees and their depts are just one such example. Mullainathan and Speiss stated that the technique would work with other ML tools such as neural networks. For their data, they tested this on various other ML methods, including forests and LASSO, and found them to outperform OLS (trees tuned by depths, however, were not more effective than the traditional OLS). The best prediction performance was seen by an Ensemble that ran several separate algorithms (the paper ran LASSO, tree and forest). Thus, econometrics can guide design choices to help improve prediction quality.
There are, of course, a few problems associated with the use of ML here. The first is the lack of standard errors on the coefficients in ML approaches. Lets see how this can be a problem: The Mullainathan-Spiess study randomly divided the sample of housing units into ten equal partitions. After this, they re-estimated the LASSO predictor (with the regulariser kept fixed). The results displayed a massive problem: a variable used by the LASSO model in one partition may be unused in another. There were very few stable patterns throughout the partitions.
This does not affect the prediction accuracy too much, but it does not help decipher whether two variables are highly correlated. In traditional estimation methods, such correlations are reflected as significant standard errors. Due to this, while we can predict house prices with accuracy, we cannot use such ML models to answer questions like whether a variable, e.g. number of dining rooms, is unimportant in this research just because the LASSO regression did not use it. Regularisation also leads to problems: it allows the choice of less complex but potentially wrong models. It could also bring up concerns of omitted variable biases.
Finally, it is essential to understand the type of problems ML solves. ML revolves around predicting a function y from variable x. However, many economic applications work around estimating parameter that might underlie the relationship between x and y. ML algorithms are not built for this purpose. The danger here is taking an algorithm built for y= and presuming that its value would have the properties associated with estimation output.
Still, ML does improve predictionso one might benefit from it by looking for problems with more significant consequences (i.e. situations where improved predictions have immense applied value).
One such category is within the new kinds of data (language, images) mentioned earlier. Analysing such data involves prediction as a pre-processing step. This is particularly relevant in the presence of missing data on economic outcomes. For example, a 2016 study trained a neural network to predict local economic outcomes with the help of satellite data in five African countries. Economists can also use such ML methods in policy applications. An example provided by Mullainathan and Spiess paper was of deciding which teacher to hire. This would involve a prediction task (deciphering the teachers added value) and help make informed decisions. These tools, therefore, make it clear that AI and ML are not to be left unnoticed in todays world.
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Machine Learning Through The Lens of Econometrics - Analytics India Magazine
Cellarity: Transforming Drug Development at the Confluence of Biology and Machine Learning – BioSpace
In the field of drug discovery, one must always begin with the target, right? Not if you ask Cellarity, a quickly emerging biotech company revolutionizing the drug development space.
Rather than the traditional target centric approach to drug discovery, Cellarity works at the level of the cell to understand how disease impacts cell behavior via a target agnostic approach that can help illuminate the most complex diseases science has not yet been able to crack.
For decades, drug discovery has been about reducing diseases down to a single molecular target that we can drug to influence the course of a given disease, explained Cellarity CEO Fabrice Chouraqui, who is also a CEO-Partner at Flagship Pioneering. This approach has produced a significant number of breakthrough treatments, but the target-centric assumptions that we make in vitro or in vivo do not often translate into human. Human biology is far more complex than any single target could ever predict, which is one reason why right now many drugs fail in clinical development. Our approach is different.
Founded in 2019 and already rising to the top of lists such as BioSpaces own Top Life Sciences Startups to Watch in 2021, Cellarity believes there is a better wayone based on the computational modeling of cell behavior.
Cellaritys unique platform generates unprecedented biological insights by combining unique expertise in network biology, high-resolution data, and machine learning. The result is a new understanding of the cells trajectory from health to disease and how cells relate to one another in tissues. This in turn opens up a world of opportunities for the discovery of novel therapeutics, particularly for complex diseases.
Diseases are complex and often not linked to a single target in a single cell in a single system, said Chouraqui. Conditions like T cell exhaustion, metabolic disease, complex neurodegenerative diseases like Alzheimerstheres a reason we havent made a lot of progress in these areas. So we asked ourselves if there was a way to work at a higher levelthe level of the cellto really harness the complexity of human biology.
Because Cellaritys pioneering approach does not start with a single molecular target, its scientists are able to uncover a much more diverse set of compounds that can be deeply characterized to understand how they work on both known and previously unknown targets. Indeed, the companys algorithms, data assets and approach were inspired by systems biology.
In the early 2000s, systems biology proposed that by looking at biology as a whole, we would be able to better understand the interplay between its parts, specifically genes, proteins and pathways in the context of disease and health, said Cellarity Chief Digital and Data Officer Milind Kamkolkar. However, due to a lack of well-integrated high-resolution data and sophisticated computational power, the industry had no choice but to study biologys parts in the absence of its networks.
A lot has changed since then. In the past five years alone, phenotypic drug discovery has evolved as an alternative to the single-target approach, thanks to advances in high-throughput imaging technology and machine learning. Yet the gap between a drugs success in vitro and an efficacious drug in patients remains immense.
Cellaritys solution: Unlike single molecules, single target or phenotypic representations of cellular programs, Cellarity directly targets cellular programs critical to disease, leveraging a platform that systematically addresses the problems of translation beyond simplifying target discovery, toxicity, adverse effects, and drug design.
One key part of the approach is the way Cellarity predicts drugs and their properties by tying them to computationally engineered representations of cell behavior called Cellarity Maps.
Cellarity Maps give us a much higher-resolution picture of the cellular components of a tissue and really allow us to understand the mechanism of action that one would want to reverse to go from a state of disease to a state of health, said Chouraqui.
Chouraqui believes that there is no limit to where this cell-centric approach can take Cellarity and the field of medicine. His assertion is backed up by the cadre of investors that recently put up $123 million in series B financing.
Our investors recognized that Cellarity stood out in the field of drug discovery, said Saif Rathore, MD PhD, Cellaritys VP and Head of Strategy and Partnerships. We are the only company taking a target-agnostic approach that evaluates cell behavior changes and works through product optimization, whereas others in the field are primarily working on optimizing different parts of the target centric molecular or phenotypic drug discovery processes.
To execute its vision, Cellarity has assembled a team of diverse, world class talent. We have brought together international leaders from pharma, graduates of Flagship Pioneering academic programs, physicians, scientists, and pedigrees that span the spectrum from the Broad Institute to McKinsey. said Rathore.
The outcome: the pioneering biotech already has 7 drug discovery programs underway across 10 therapeutic areas including the high-value fields of hematology, immuno-oncology, metabolism, and respiratory.
All diseases stem from a disorder at the cellular level, said Chouraqui. This cell-centric approach can be applied to virtually every single disease. We are progressing programs in diverse therapeutic areas to show the depth of our platform, starting with diseases for which there is a well-understood and direct correlation between a change in cell behavior and the etiology of the disease.
Chouraquis vision for the platform transcends the company. In a few years time, he sees Cellarity with unparalleled predictive power in different drug modalities and a deep exploratory pipeline with multiple clinical proofs of concept in different disease areas.
Our platform has the potential to change how the world approaches the discovery of medicines said Chouraqui.
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Cellarity: Transforming Drug Development at the Confluence of Biology and Machine Learning - BioSpace
The intersection of machine learning and biology is the future, and we want to be one of the first companies really helping push that forward. – CTech
It is not often when an entrepreneur with an idea to improve our lives can literally improve the biology of our bodies, but that is exactly what Luis Voloch and his co-founder Noam Solomon at Immunai are doing. Combining machine learning and biology, they are working to no and reprogram our immune systems by applying knowledge about certain cancers, for example, to other forms of cancer. The company currently works with various pharmaceutical companies to help them develop, improve, and combine their drugs, but Voloch has goals of helping people understand their immune systems better beyond cancer as well, and onto fighting autoimmune and age-related diseases as well. To reach their goals, they have hired the best and the brightest they can find in fields such as software, computational biology, and immunology. Other crucial characteristics for Immunais hires are that they are very curious about the other disciplines and want to learn and work together.
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Michael Matias, Forbes 30 Under 30, is the author of Age is Only an Int: Lessons I Learned as a Young Entrepreneur. He studies Artificial Intelligence at Stanford University, while working as a software engineer at Hippo Insurance and as a Senior Associate at J-Ventures. Matias previously served as an officer in the 8200 unit. 20MinuteLeaders is a tech entrepreneurship interview series featuring one-on-one interviews with fascinating founders, innovators and thought leaders sharing their journeys and experiences.
Contributing editors: Michael Matias, Amanda Katz
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The intersection of machine learning and biology is the future, and we want to be one of the first companies really helping push that forward. - CTech
Machine Learning Deserves Better Than This | In the Pipeline – Science Magazine
This is an excellent overview at Stat on the current problems with machine learning in healthcare. Its a very hot topic indeed, and has been for some time. There has especially been a flood of manuscripts during the pandemic, applying ML/AI techniques to all sorts of coronavirus-related issues. Some of these have been pretty far-fetched, but others are working in areas that everyone agrees that machine learning can be truly useful, such as image analysis.
How about coronavirus pathology as revealed in lung X-ray data? This new paper (open access) reviewed hundreds of such reports and focused in on 62 papers and preprints on this exact topic. On closer inspection, none of these is of any clinical use at all. Every single one of the studies falls into clear methodological errors that invalidate their conclusions. These range from failures to reveal key details about the training and experimental data sets, to not performing robustness or sensitivity analyses of their models, not performing any external validation work, not showing any confidence intervals around the final results (or not revealing the statistical methods used to compute any such), and many more.
A very common problem was the (unacknowledged) risk of bias right up front. Many of these papers relied on public collections of radiological data, but these have not been checked to see if the scans marked as COVID-19 positive patients really were (or if the ones marked negative were as well). It also needs to be noted that many of these collections are very light on actual COVID scans compared to the whole database, which is not a good foundation to work from, either, even if everything actually is labeled correctly by some miracle. Some papers used the entire dataset in such cases, while others excluded images using criteria that were not revealed, which is naturally a further source of unexamined bias.
In all AI/ML approaches, data quality is absolutely critical. Garbage in, garbage out is turbocharged to an amazing degree under these conditions, and you have to be really, really sure about what youre shoveling into the hopper. We took all the images from this public database that anyone can contribute to and took everyones word for it is, sadly, insufficient. For example, one commonly used pneumonia dataset turns out to be a pediatric collection of patients between one and five, so comparing that to adults with coronavirus infections is problematic, to say the least. Youre far more likely to train the model to recognize children versus adults.
That point is addressed in this recent preprint, which shows how such radiology analysis systems are vulnerable to this kind of short-cutting. Thats a problem for machine learning in general, of course: if your data include some actually-useless-but-highly-correlated factor for the system to build a model around, it will do so cheerfully. Why wouldnt it? Our own brains pull stunts like that if we dont keep a close eye on them. That paper shows that ML methods too often pick up on markings around the edges of the actual CT and X-ray images if the control set came from one source or type of machine and the disease set came from another, just to pick one example.
To return to the original Nature paper, remember, all this trouble is after the authors had eliminated (literally) hundreds of other reports on the topic, for insufficient documentation. They couldnt even get far enough to see if something had gone wrong, or how, because these other papers did not provide details of how the imaging data were pre-processed, how the training of the model was accomplished, how the model was validated, or how the final best model was selected at all. These fall into Paulis category of not even false. A machine learning paper that does not go into such details is, for all real-world purposes, useless. Unless you count putting a publication on the CV as a real-world purpose, and I suppose it is.
But if we want to use these systems for some slightly more exalted purposes, we have to engage in a lot more tire-kicking than most current papers do. I have a not-very-controversial prediction: in coming years, virtually all of the work thats being published now on such systems is going to be deliberately ignored and forgotten about, because its of such low quality. Hundreds, thousands of papers are going to be shoved over into the digital scrap heap, where they most certainly belong, because they never should have been published in the state that theyre in. Who exactly does all this activity benefit, other than the CV-padders and the scientific publishers?
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Machine Learning Deserves Better Than This | In the Pipeline - Science Magazine
PathAI to Present Machine Learning-based Quality Control Tool for HER2 Testing in Breast Cancer at the American Society of Clinical Oncology Virtual…
BOSTON, June 8, 2021 /PRNewswire/ --PathAI, a global leader of AI-powered technology applied to pathology, today announced that new data highlighting a quality control tool for HER2 testing in digital pathology images captured in clinical trials will be presented in the American Society of Clinical Oncology (ASCO) Virtual Scientific Program 2021, held from June 4-8, 2021. These results will be shared in the poster presentation, Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials (Abstract #3061), in the session, Developmental Therapeutics Molecularly Targeted Agents and Tumor Biology.
Together, PathAI, AstraZeneca (LSE/STO/Nasdaq: AZN) and Daiichi Sankyo Company, Limited have developed ML-based models for the automated quantification of HER2 IHC images in breast cancer tissue. Expression of HER2, a protein localized in the cell membrane, is typically assessed by pathologists to evaluate patient eligibility for anti-HER2 targeted therapies. ML-based models trained to identify and quantify tumor histology features can provide highly accurate and reproducible scores that are highly concordant with manual pathology.
The PathAI HER2 models were developed to generate HER2 scores consistent with the 2018 ASCO/CAP HER2 scoring guidelines. The models also produce metrics that reflect the quality of HER2 testing, such as the area and number of tumor cells, the presence of ductal carcinoma in situ (DCIS), background staining and artifact content. In a test set including diverse tissue-types across a wide range of breast cancer types, ML quantification of HER2 was consistent with manual scores from a consensus of pathologists (ICC 0.88, 95% CI 0.82-0.92). ML scores were even more closely aligned with pathologist scores after further training to learn pathologist scoring methods (ICC 0.91, 95% CI 0.89-0.94). By providing consistent, automated HER2 IHC image analysis, PathAI ML models can provide real-time QC read-outs enabling identification of drifts or inconsistencies in HER2 testing data and images captured during clinical trials.
PathAI's broad approach towards integrating AI-powered tools into oncology clinical trial workflows is also represented by a separate study that PathAI is presenting at ASCO (Abstract #106). Both presentations are examples of how AI can enhance pathologist performance by generating accurate and reproducible clinically relevant scores that can be scaled to levels that are currently unachievable.
About PathAI:PathAI is a leading provider of AI-powered research tools and services for pathology. PathAI's platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit pathai.com.
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Machine Learning as a Service (MLaaS) Market to Witness Huge Growth by 2028 | Microsoft, International Business Machine, Amazon Web Services KSU |…
Global Machine Learning as a Service (MLaaS) Market Report is an objective and in-depth study of the current state aimed at the major drivers, market strategies, and key players growth. The study also involves the important Achievements of the market, Research & Development, new product launch, product responses and regional growth of the leading competitors operating in the market on a universal and local scale. The structured analysis contains graphical as well as a diagrammatic representation of worldwide Machine Learning as a Service (MLaaS) Market with its specific geographical regions.
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Avnet to showcase power of AI and machine learning – IT Brief Australia
Tech distributor Avnet will showcase new innovative technology, applications and solutions in artificial intelligence and machine learning at the Avnet AI Cloud Exhibition, together with its suppliers and partners.
The company will also hold the Avnet 2021 Artificial Intelligence Cloud Conference on 29 June, 2021. Joined by developers, engineers, and decision makers in the AI field, the summit will feature cutting-edge technology trends in artificial intelligence and machine learning, and in-depth discussions on the development, future prospects and blueprints for AI to encourage and accelerate innovation.
"MarketsandMarkets forecasts the global artificial intelligence market size to grow to over USD$300 billion by 2026, and the market in Asia Pacific is anticipated to grow at the highest CAGR during the forecast period," says KS Lim, senior director of supplier management at Avnet Asia.
"As the world's leading technology distributor and solution provider, Avnet has a comprehensive ecosystem that provides customers with end-to-end artificial intelligence and machine learning solutions, reducing the cost and complexity of product development to enable application scenarios," he says.
"We will continue to work hand in hand with our suppliers and partners to further contribute to the development and maturity of the entire AI ecosystem."
The virtual exhibition is divided into three sections: AI smart solution demonstration area, Avnet design service demonstration area, and a partner solution demonstration area.
In the AI smart solution demonstration area, participants can learn about Avnet's various innovative technologies and industrial applications, including:
AI camera: A smart AI camera utilising a neural network implemented in the FPGA fabric. It integrates an independent high-performance ISP camera module based on the Xilinx Zynq7020 to achieve a variety of functions, including noise reduction, wide dynamic range, light source detection, motion detection and edge enhancement function.
BlueBox AI platform: The embedded edge artificial intelligence box can perform multi-channel convolutional neural network operations. It facilitates real-time multi-channel AI functions such as face detection, passenger and traffic statistics, and license plate recognition. All functions operate independently and can work simultaneously to provide edge artificial intelligence analytic solutions.
The box integrates all the above functions through the underlying Xilinx Zynq UltraScale+ MPSoC to perform AI computing on demand.
ROS on Ultra96: The open source Robot Operating System (ROS) runs on the Avnet Ultra96 development board, which features the Xilinx Zynq UltraScale+ MPSoC. The programmable logic part of the Zynq UltraScale+ MPSoC provides deep learning acceleration capabilities, while consolidating a range of ROS functions such as control, SLAM, and navigation. The small form-factor Ultra96 single board computer running ROS makes an ideal platform for developing autonomous robots, service robots, and general purpose ROS experimentation.
In the partner demonstration area, Avnet's suppliers and partners will also showcase their innovations:
ON Semiconductor: A variety of advanced imaging technologies such as high speed, short exposure, global shutter and platform solutions will be displayed to address the application of different scenarios such as factory automation and the challenges faced in industrial AI applications, to accelerate innovation.
Samtec: Fast-growing technologies like Artificial Intelligence are driving new system architectures that demand increased bandwidths, frequencies and densities. To meet these challenges, Samtec offers innovative high-performance interconnects that exceed AI industry standards.
STMicroelectronics: Introduce embedded AI solutions based on deep learning models running on high performance 32-bit microcontroller and also machine learning based MEMS sensor.Western Digital: IX SN530 NVMeTM industrial-grade SSD will be displayed, which supports a new generation of data-rich industrial design and autonomous vehicle design.
Xilinx: Will showcase its real-time multi-task autonomous driving AI perception processing solution, which uses the industry-leading lightweight optimisation algorithm to achieve vehicle detection, lane line detection, lane detection in ADAS and autonomous driving scenarios through a single model. It can perform multiple tasks such as driving area detection and depth estimation.
In addition, Xilinx will also demonstrate the application of Versal-based DPU in low-latency automatic driving and pose detection.
YAGEO Group: Will showcase the flagship products from its main brands YAGEO, KEMET and PULSE, including YAGEO resistors, KEMET polymer capacitors, and PULSE network devices, to provide high reliability polymer and ceramic capacitor solutions for AI chips and DC power supplies for autopilot computers.
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Avnet to showcase power of AI and machine learning - IT Brief Australia
Hardening AI: Is machine learning the next infosec imperative? – ITProPortal
As enterprise deployments of machine learning continue at a strong pace, including in mission-critical environments such as in contact centers, for fraud detection and in regulated sectors like healthcare and finance for example, they are doing so against a backdrop of rising and evermore ferocious cyberattacks.
Take, for example, the SolarWinds hack in December 2020, arguably one of the largest on record, or the recent exploits that hit Exchange servers and affected tens of thousands of customers. Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the technology arriving in April 2021. The EU's landmark proposals build on GDPR legislation, carrying heavy penalties for enterprises that fail to consider the risks and ensure that trust goes hand in hand with success in AI.
Altogether, a climate is emerging in which the significance of securing machine learning can no longer be ignored. Although this is a burgeoning field with much more innovation to come, the market is already starting to take the threat seriously.
Our research surveys reveal a steep change in deployments of machine learning during the pandemic, with more than 80 percent of enterprises saying they are trialing the technology or have put it into production, up from just over half a year ago.
But the topic of securing those systems has received little fanfare by comparison, even though research into the security of machine learning models goes back to the early 2000s.
We've seen several high-profile incidents that highlight the risks stemming from greater use of the technology. In 2020, a misconfigured server at Clearview AI, the controversial facial recognition start-up, leaked the company's internal files, apps and source code. In 2019, hackers were able to trick the Autopilot system of a Tesla Model S by using adversarial approaches involving sticky notes. Both pale in comparison to more dangerous scenarios, including the autonomous car that killed a pedestrian in 2018 and a facial recognition system that caused the wrongful arrest of an innocent person in 2019.
The security community is becoming more alert to the dangers of real-world AI. The CERT Coordination Center, which tracks security vulnerabilities globally, published its first note on machine learning risks in late 2019, and in December 2020, The Partnership on AI introduced its AI Incident Database, the first to catalog events in which AI has caused "safety, fairness, or other real-world problems".
The challenges that organizations are facing with machine learning are also shifting in this direction.
Several years ago, problems with preparing data, gaining skills and applying AI to specific business problems were the dominant headaches, but new topics are now coming to the fore. Among them are governance, auditability, compliance and above all, security.
According to CCS Insight's latest survey of senior IT leaders, security is now the biggest hurdle companies face with AI, cited by over 30 percent of respondents. Many companies struggle with the most rudimentary areas of security at the moment, but machine learning is a new frontier, particularly as business leaders start to think more about the risks that arise as the technology is embedded into more business operations.
Missing until recently are tools that help customers improve the security of their machine learning systems. A recent Microsoft survey, for example, found that 90 percent of businesses said they lack tools to secure their AI systems and that security pros were looking for specific guidance in the field.
Responding to this need, the market is now stepping up. In October 2020, non-profit organization MITRE, in collaboration with 12 firms including Microsoft, Airbus, Bosch, IBM and Nvidia, released an Adversarial ML Threat Matrix, an industry-focused open framework to help security analysts detect and respond to threats against machine learning systems.
Additionally, in April 2021, Algorithmia, a supplier of an enterprise machine learning operations (MLOps) platform that specializes in the governance and security of the machine learning life cycle, released a host of new security features focused on the integration of machine learning into the core IT security environment. They include support for proxies, encryption, hardened images, API security and auditing and logging. The release is an important step, highlighting my view that security will become intrinsic to the development, deployment and use of machine learning applications.
Finally, just last week, Microsoft released Counterfit, an open-source automation tool for security testing AI systems. Counterfit helps organizations conduct AI security risk assessments to ensure that algorithms used in businesses are robust, reliable and trustworthy. The tool enables pen testing of AI systems, vulnerability scanning and logging to record attacks against a target model.
These are early but important first steps that indicate the market is starting to take security threats to AI seriously. I encourage machine learning engineers and security professionals to get going begin to familiarize yourselves with these tools and the kinds of threats your AI systems could face in the not-so-distant future.
As machine learning becomes part of standard software development and core IT and business operations in the future, vulnerabilities and new methods of attack are inevitable. The immature and open nature of machine learning makes it particularly susceptible to hacking and that's why I predicted last year that we would see security become the top priority for enterprises' investment in machine learning by 2022.
A new category of specialism will emerge devoted to AI security and posture management. It will include core security areas applied to machine learning, like vulnerability assessments, pen testing, auditing and compliance and ongoing threat monitoring. In future, it will track emerging security vectors such as data poisoning, model inversions and adversarial attacks. Innovations like homomorphic encryption, confidential machine learning and privacy protection solutions such as federated learning and differential privacy will all help enterprises navigate the critical intersection of innovation and trust.
Above all, it's great to see the industry beginning to tackle this imminent problem now. Matilda Rhode, Senior Cybersecurity Researcher at Airbus, perhaps captures this best when she states, "AI is increasingly used in industry; it is vital to look ahead to securing this technology, particularly to understand where feature space attacks can be realized in the problem space. The release of open-source tools for security practitioners to evaluate the security of AI systems is both welcome and a clear indication that the industry is taking this problem seriously".
I look forward to tracking how enterprises progress in this critical field in the months ahead.
Nick McQuire, Chief of Enterprise Research, CCS Insight
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Hardening AI: Is machine learning the next infosec imperative? - ITProPortal
Koreas Riiid raises $175M from SoftBank to expand its AI-based learning platform to global markets – TechCrunch
AI is eating the world of education, Riiid co-founder and CEO YJ Jang notes in his biographical description on his LinkedIn profile. Today his startup which builds AI-based personalized learning, including test prep, for students is announcing a major funding round to help it position itself as a player in that process.
Seoul-based Riiid has closed a funding round of $175 million, an equity round coming from a single backer, SoftBanks Vision Fund 2.
The funding is coming at a high-watermark moment for edtech with the shift to remote learning in the last year of pandemic living highlighting the opportunity to build better tools to serve that market, and a number of startups in the category subsequently raising hundreds of millions of dollars to tackle the opportunity. Riiid plans to use the investment both to expand its footprint internationally, a well as to expand its products.
Riiid is not disclosing its valuation, but this round is its biggest yet and brings the total raised by the startup to $250 million, a significant sum in the world of edtech.
Riiid has primarily made a name for itself through Santa, a test prep app geared toward people in non-English-language countries to practice and prepare to take the TOEIC English language proficiency exam (often a requirement to apply to English-language universities if youre not a native English speaker), which has been used by more than 2.5 million students in Korea and Japan.
It has also been partnering with third parties to expand into test prep for other exams. These have included the GMAT (in partnership with Kaplan) for Korean students; an app, in partnership with ConnecME Education (a company that tailors educational services specifically to cater to international audiences) to help people in Egypt, UAE, Turkey, Saudi Arabia and Jordan prepare for the ACT; and adeal to build AI-based tools for students in Latin America to prepare for their college entrance exams. The ACT development comes after Riiid said that the former CEO of ACT, Marten Roorda, was joining its international arm Riiid Labs as its executive in residence, so that could point to more ACT prep applications for other markets, too.
Beyond university entrance tests, Riiid has also been building apps for vocational education, with Santa Realtor for preparing for real estate agency exams, and a test preparation tool for insurance agent exams, both in Korea.
The company has been growing at a time when edtechs are seeing more business and a rise in overall credibility and urgency to fill the gap left by the temporary cessation of in-person learning. The extra element of bringing artificial intelligence into the equation is not unique: A number of companies are bringing in advances in computer vision, natural language processing and machine learning to bring more personalized experiences into what might otherwise appear like a one-size-fits-all model. What is notable here is that Riiid has also been anchoring a lot of its R&D in IP. The company says it has applied for 103 domestic and international patents, and has so far had 27 of them issued.
Riiid wants to transform education with AI, and achieve a true democratization of educational opportunities, said Riiid CEO YJ Jang in a statement. This investment is only the beginning of our journey in creating a new industry ecosystem and we will carry out this mission with global partnerships.
For SoftBank, this is one of the firms bigger edtech investments others have included Kahoot ($215 million), Unacademy in India and Descomplica in Brazil. Riiid said that this round is SoftBanks first specifically in the area of AI built for educational applications.
Riiid is driving a paradigm shift in education, from a one size fits all approach to personalized instruction. Powered by AI and machine learning, Riiids platform provides education companies, schools and students with personalized plans and tools to optimize learning potential, said Greg Moon, managing partner at SoftBank Investment Advisers. We are delighted to partner with YJ and the Riiid team to support their ambition of democratizing quality education around the world.
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Koreas Riiid raises $175M from SoftBank to expand its AI-based learning platform to global markets - TechCrunch