Credit: Janelle Barone

When more than 155,000 students from all over the world signed up to take free online classes in electronics in 2012, offered through the fledgling US provider edX, they set in motion an explosion in the popularity of online courses.

The edX platform, created by the Massachusetts Institute of Technology (MIT) and Harvard University, both in Cambridge, Massachusetts, was not the first attempt at teaching classes online but the number of participants it attracted was unusual. The activity created a massive amount of information on how people interact with online education, and presented researchers with an opportunity to garner answers to questions such as What might encourage people to complete courses?, and What might give them a reason to drop out?.

We had a tonne of data, says Kalyan Veeramachaneni, a data scientist at MITs Laboratory for Information and Decision Systems. Although the university had long dealt with large data sets generated by others, that was the first time that MIT had big data in its own backyard, says Veeramachaneni.

Hoping to take advantage, Veeramachaneni assigned 20 MIT students to run analyses of the information. But he soon ran into a roadblock: legally, the data had to be private. This wealth of information was held on a single computer in his laboratory, with no connection to the Internet to prevent hacking. The researchers had to schedule a time to use it. It was a nightmare, Veeramachaneni says. I just couldnt get the work done because the barrier to the data was very high.

Why artificial intelligence needs to understand consequences

His solution, eventually, was to create synthetic students computer-generated versions of edX participants that shared characteristics with real students using the platform, but that did not give away private details. The team then applied machine-learning algorithms to the synthetic students activity, and in doing so discovered several factors associated with a person failing to complete a course1. For instance, students who tended to submit assignments right on a deadline were more likely to drop out. Other groups took the findings of this analysis and used them to help create interventions to help real people complete future courses2.

This experience of building and using a synthetic data set led Veeramachaneni and his colleagues to create the Synthetic Data Vault, a set of open-source software that allows users to model their own data and then use those models to generate alternative versions of the data3. In 2020, he co-founded a company called DataCebo, based in Boston, Massachusetts, which helps other companies to do this.

The desire to preserve privacy is one of the driving forces behind synthetic-data research. Because artificial intelligence (AI) and machine learning have expanded rapidly, finding their way into areas as diverse as health care, art and financial analysis, concerns about the data used to train the systems is also growing. To learn, these algorithms must consume vast amounts of information much of which relates to individuals. The system could reveal private details, or be used to discriminate against people when making decisions on hiring, lending or housing, for example. The data fed to these machines might also be owned by an individual or company that does not want the information to be used to create a tool that might then compete with them or at least, might not want to give the data away for free.

Some researchers think that the answer to these concerns could lie in synthetic data. Getting computers to manufacture data that is close enough to the real thing without recycling real information could help to address privacy problems. But it could also do much more. I want to move away from just privacy, says Mihaela van der Schaar, a machine-learning researcher and director of the UK Cambridge Centre for AI in Medicine. I hope that synthetic data could help us create better data.

All data sets come with issues that go beyond privacy considerations. They can be expensive to produce and maintain. In some cases for example, trying to diagnose a rare medical condition using imaging there simply might not be enough real-world data available to train a system to do the task reliably. Bias is also a problem both social biases, which might cause systems to favour one group of people over another, and subtler issues such as a training set of photos that includes only a handful taken at night. Synthetic data, its proponents say, can get around these problems by adding absent information to data sets faster and more cheaply than gathering it from the real world, assuming it were possible to obtain the real thing at all.

To me, its about making data this living, controllable object that you can change towards your application and your goals, says Phillip Isola, a computer scientist at MIT who specializes in machine vision. Its a fundamental new way of working with data.

There are several ways to synthesize data, but they all invoke the same concept. A computer, using a machine-learning algorithm or a neural network, analyses a real data set and learns about the statistical relationships within it. It then creates a new data set containing different data points than the original, but retaining the same relationships. A familiar example is ChatGPT, the text generation engine. ChatGPT is based on a large language model, Generative Pre-trained Transformer, which pored over billions of examples of text written by humans, analysed the relationships between the words and built a model of how they fit together. When given a prompt Write me an ode to ducks ChatGPT takes what it has learnt about odes and ducks and produces a string of words, with each word choice informed by the statistical probability of it following the previous one:

Oh ducks, feathered and free,

Paddling in ponds with such glee,

Your quacks and waddles are a delight,

A joy to behold, day or night.

With the right training, machines can produce not only text but also images, audio or the rows and columns of tabular data. The question is, how accurate is the output? Thats one of the challenges in synthetic data, says Thomas Strohmer, a mathematician who directs the Center for Data Science and Artificial Intelligence Research at the University of California, Davis (UC Davis).

Jason Adams, Thomas Strohmer and Rachael Callcut (left to right) are part of the synthetic data research team at UC Davis Health.

You first have to figure out what you mean by accuracy, he says. To be useful, a synthetic data set must retain the aspects of the original that are relevant to the outcome the all-important statistical relationships. But AI has accomplished many of its impressive feats by identifying patterns in data that are too subtle for humans to notice. If we could understand the data well enough to easily identify the relationships in medical data that suggest someone is at risk of a disease, we would have no need for a machine to find those relationships in the first place, Strohmer says.

This catch-22 means that the clearest way to know whether a synthetic data set has captured the important nuances of the original is to see if an AI system trained on the synthetic data makes similarly accurate predictions to a system trained on the original. The more capable the machine, the harder it is for humans to distinguish the real from the fake. AI-generated images and text are already at the point where they seem realistic to most people, and the technology is advancing rapidly. Were getting close to the level where, even to the expert, the imagery looks correct, but it still might not be correct, Isola says. It is therefore important that users treat synthetic data with some caution, and dont lose sight of the fact that it isnt real data, he says. It still might be misleading.

Last April, Strohmer and two of his colleagues at UC Davis Health in Sacramento, California, won a four-year, US$1.2-million grant from the US National Institutes of Health to work out ways to generate high-quality synthetic data that could help physicians to predict, diagnose and treat diseases. As part of the project, Strohmer is developing mathematical methods of proving just how accurate synthetic data sets are.

He also wants to include a mathematical guarantee of privacy, especially given the stringent laws around medical privacy around the world, such as the Health Insurance Portability and Accountability Act in the United States and the European Unions General Data Protection Regulation. The difficulty is that the utility and privacy of data are in tension; increasing one means decreasing the other.

To increase privacy in data, scientists add statistical noise to a data set. If, for instance, one of the data points collected is a persons age, they throw in some random ages to make individuals less identifiable. Its easier to pinpoint a 45-year-old man with diabetes than a person with diabetes who might be 38, or 51, or 62. But, if the age of diabetes onset is one of the factors being studied, this privacy-protecting measure will lead to less accurate results.

Abandoned: the human cost of neurotechnology failure

Part of the difficulty of guaranteeing privacy is that scientists are not completely sure how synthetic data reveals private information or how to measure how much it reveals, says Florimond Houssiau, a computer scientist at the Alan Turing Institute in London. One way in which secrets could be spilled is if the synthetic data are too similar to the original data. In a data set that contains many pieces of information associated with an individual, it can be hard to grasp the statistical relationships. In this case, the system generating the synthetic version is more likely to replicate what it sees rather than make up something entirely new. Privacy is not actually that well understood, Houssiau says. Scientists can assign a numerical value to the privacy level of a data set, but we dont exactly know which values should be considered safe or not. And so its difficult to do that in a way that everyone would agree on.

The varied nature of medical data sets also makes generating synthetic versions of them challenging. They might include notes written by physicians, X-rays, temperature measurements, blood-test results and more. A medical professional with years of training and experience might be able to put those factors together and come up with a diagnosis. Machines, so far, cannot. We just dont know enough, in terms of machine learning, to extract information from different modalities, Strohmer says. Thats a problem for analysis tools, but its also a problem for machines tasked with creating synthetic data sets that retain the all-important relationships. We dont understand yet how to automatically detect these relationships, he says.

There are also fundamental theoretical limits to how much improvement data can undergo, says Isola. Information theory contains a principle called the data-processing inequality, which states that processing data can only reduce the amount of information available, not add to it4. And all synthetic data must have real data at its root, so all the problems with real data privacy, bias, expense and more still exist at the start of the pipeline. Youre not getting something for free youre still ultimately learning from the world, from data. Youre just reformatting that into an easier-to-work-with format that you can control better, Isola says. With synthetic data, data comes in and a better version of the data comes out.

Although synthetic data in medicine havent yet made their way into clinical use, there are some areas where such data sets have taken off. They are being widely used in finance, Strohmer says, with many companies springing up to help financial institutions create new data that protect privacy. Part of the reason for this difference might be that the stakes are lower in finance than in medicine. If in finance you get it wrong, it still hurts, but it doesnt lead to death, so they can push things a little bit faster than in the medical field, Strohmer says.

In 2021, the US Census Bureau announced that it was looking at creating synthetic data to enhance the privacy of people who respond to its annual American Community Survey, which provides detailed information about households in subsections of the country. Some researchers have objected, however, on the grounds that the move could undermine the datas usefulness. In February, Administrative Data Research UK, a partnership that enables the sharing of public-sector data, announced a grant to study the value of synthetic versions of data sets that have been created by the Office of National Statistics and the UK Data Service.

Bioinspired robots walk, swim, slither and fly

Some people are also using synthetic data to test software that they hope to eventually use on real data that they do not yet have access to, says Andrew Elliott, a statistician at the University of Glasgow, UK. These fake data have to look something like the real data, but they can be meaningless, because they only exists for testing the code. A scientist who wants to analyse a sensitive data set that they are granted only limited access to can perfect the code first with synthetic data, and not have to waste time when they get hold of the real data.

For now, synthetic data are a relatively niche pursuit. van der Schaar thinks that more people should be talking about synthetic data and their potential impact and not just scientists. Its important that not only computer scientists understand, but also the general public, she says. People need to wrap their heads around this technology because it could affect everyone.

The issues around synthetic data not only raise interesting research questions for scientists but also important issues for society at large, Strohmer says. Data privacy is so important in the age of surveillance capitalism, he says. Creating good synthetic data that both preserve privacy and reflect diversity, and that are made widely available, has the potential not just to improve the performance of AI and expand its uses, but also to help democratize AI research. A lot of data is owned by a few big companies, and that creates an imbalance. Synthetic data could help to re-establish this balance a little bit, Strohmer says. I think thats an important, bigger goal behind synthetic data.

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Synthetic data could be better than real data - Nature.com

Artificial Intelligence & Machine Learning would be leveraged to analyse data generated through smart meters to detect the theft cases - Union Power & NRE Minister R. K. Singh  India Education Diary

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Artificial Intelligence & Machine Learning would be leveraged to analyse data generated through smart meters to detect the theft cases - Union...

Raipur Government School To Teach Robotics, AI and Machine Learning To Students  Times Now

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Raipur Government School To Teach Robotics, AI and Machine Learning To Students - Times Now

The National Geospatial-Intelligence Agency is well known for analysis of imagery and maps, but text, or written language, is a key part of the process. In a year-long study, members of the NGA workforce reported that text reading and generation occupy up to 80% of their average workflow, whether in conducting research, reviewing documents, tipping imagery or generating reports.

NGA conducted the study of natural language processing through a federally funded research and development center, with hopes to significantly raise awareness of the potential time savings and intelligence gains made possible through greater access to text analytics software.

If a picture is worth a thousand words, NGA is in the business of countless words, says Monica Lipscomb of NGA Research, who serves as the NLP program manager. Map reading, legend generation, and image notation are obvious examples.

Natural language processing, also known as human language technology, enables the automated sifting, sorting, translating, comprehending and sensemaking of billions of words.In addition to speeding the analytic workflow, NLP has applicability to workflows involving security, finance, policy, records management and safety of navigation alerts, according to Lipscomb. The Source Maritime Automated Processing System, launched in early 2022, is driven by natural language processing and basic machine learning. SMAPS has reportedly cut in half the time needed to process incoming incident messages and generate alerts.

Lipscomb says the agency wants to facilitate mission advancement in other NGA workflows akin to those achieved through SMAPS.

Many NGA employees know that NLP resources are available, but they have difficulty knowing where to find them or how to orient them towards NGA topics of interest, she said.

As a next step, NGA will discuss natural language processing resources available throughout the Intelligence Community and generate an enterprise-wide community of interest.

Read more at NGA

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NGA Puts Machine Learning to Work to Speed Mission, Further Research - HS Today - HSToday

Nearly 20% of pregnancies in people with lupus result in an adverse pregnancy outcome (APO). In a new study, scientists were able to improve prediction accuracy of APOs using machine learning. Machine learning refers to the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.

Using a previously developed APO prediction model utilizing data from a larger multi-center, multi-ethnic study of lupus pregnancies known as the Predictors of pRegnancy Outcome: bioMarkers In Antiphospholid Antibody Syndrome and the Systemic Lupus Erythematosus (PROMISSE) study, and statistical analysis coupled with machine learning, researchers analyzed data from 385 women in their first trimester of pregnancy. They identified lupus anticoagulant positivity, disease assessment score, diastolic blood pressure or resting heartbeat, current use of antihypertension medication, and platelet count as significant baseline predictors of APO.

Researchers suggest that the ability to identify, lupus patients at high risk of APO early in pregnancy, could enhance the capacity to manage these patients and conduct trials of new treatments to prevent pre-eclampsia and placental insufficiency.

Further studies to identify new biomarkers and risk factors for APO are still needed. The Lupus Foundation of America provided the study author, Jane Salmon, MD, with a three-year grant for her IMPACT study, the first trial of a biologic therapy to prevent adverse pregnancy outcomes in high-risk pregnancies in patients with antiphospholipid syndrome (APS) with or without systemic lupus erythematosus (SLE), which also helped support this new research. Learn more about lupus and pregnancy.

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Machine Learning Can Be Used to Improve the Ability to Predict Adverse Pregnancy Outcomes in Women with Lupus - Lupus Foundation of America

The singularity is coming for us, day by creeping day. Artificial intelligence is starting to write about cycling. It is starting to create pictures of cycling. And now, it is starting to predict the results of races that havent even happened yet.

There are humans involved at some point there always are, before the end of everything. In this case, it is a data and analytics consultancy called Decision Inc., Australia. The humans developed the modelling, fed it to their machine learning tool, let it marinate for a bit [that may be creative license] and then, the magic happened.

Machine Learning is a form of Artificial Intelligence which uses advanced data analytics [to] solve complex issues, explained Decision Inc, Australia CEO, Aiden Heke. It uses algorithms to best imitate how humans solve problems or predict outcomes.

Since the technology has evolved so much over the past few decades, we thought: why not use it to predict the outcome of the UCI World Championships?

First up, the womens road race:

A caveatthe Machines were crunching their numbers before Annemiek van Vleuten crashed out of the mixed team time trial, putting her start at risk. Also, apparently The Machines dont rate Grace Brown as a top 10 favourite. But all that aside? Those are certainly some credible names.

To the men:

Again, some curiosities in here for me. The podium seems credible, but I think Van der Poel is a bit more of a dark horse than this is letting on. Pogaar seems low; Almeida seems high. Im also furious about the Juraj Sagan erasure, but that is a me thing, not a you thing, and certainly not an AI thing.

Decision Inc. is likening their cycling foray to Deep Blue, an early machine learning venture from the mid-1990s that famously vanquished chess grandmaster Garry Kasparov. Its why were putting it to the test, to see just how far its come, said Decision Inc. CEO Aiden Heke. Were keen for everyone who fancies themselves as a bit of an expert on cycling to see if they can win where Kasparov couldnt: against the Machine.

If you want to show that you know more about this weekends cycling than a series of computer calculations, you can head to the companys Instagram account where you could win some signed cycling goodies.

Or, you can just wade into the comments here and tell us who your pick is. Thatd be fun too.

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Machine learning has predicted the winners of the Worlds - CyclingTips

ARLINGTON, Va. U.S. military researchers are asking industry to develop computers able not only to analyze large amounts of data automatically, but also communicate and cooperate with humans to resolve ambiguities and improve performance over time.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0052) on Thursday for the Environment-driven Conceptual Learning (ECOLE) project.

From industry, the DARPA ECOLE project seeks proposals in five areas: human language technology; computer vision; artificial intelligence (AI); reasoning; and human-computer interaction.

ECOLE will create AI agents able to learn from linguistic and visual input to enable humans and computers to work together to analyze image, video, and multimedia documents quickly in missions where reliability and robustness are essential.

Related: Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage

ECOLE will develop algorithms that can identify, represent, and ground the attributes that form the symbolic and contextual model for a particular object or activity through interactive machine learning with a human analyst. Knowledge of attributes and affordances, learned dynamically from data encountered within an analytic workflow, will enable joint reasoning with a human partner.

This acquired knowledge also will enable the machine to recognize never-before-seen objects and activities without misclassifying them as a member of a previously learned class, detect changes in known objects, and report these changes when they are significant.

System interaction with human intelligence analysts is expected to be symbiotic, with the systems augmenting human cognitive capabilities while simultaneously seeking instruction and correction to achieve accuracy.

Industry proposals should specify how symbolic knowledge representations will be acquired from unlabeled data, including the specifics of the learning mechanism; how these representations will be associated and reasoned within a growing body of knowledge; how the representations will be applied to human-interpretable object and activity recognition; and how the framework will permit collaboration with several analysts to resolve ambiguity, extend the set of known representations, and provide greater recognitional accuracy and coverage.

Related: Artificial intelligence (AI) to enable manned and unmanned vehicles adapt to unforeseen events like damage

The four-year ECOLE project with three phases; this solicitation concerns only the first and second phases. The first phase will create prototype agents that can pull relevant information out of unlabeled multimedia data, supplemented with human interaction.

These prototypes will demonstrate not only the ability to learn new concepts, but also to recombine previously learned attributes to recognize never-before-seen objects and activities. Systems also will be able to reason over similarities and differences in objects and activities.

The second phase of the ECOLE project will scale-up the framework to include several AI agents and human analysts to help deal with uncertain or contradictory information.

Computer interaction with human analysts will enable the system to learn to name and describe objects, actions, and properties to verify and augment their representations, and to acquire complex knowledge quickly and accurately from potentially sparse observations.

Related: Wanted: artificial intelligence (AI) and machine autonomy algorithms for military command and control

Humans and computers will work together primarily through the English language -- including words with several different meanings -- in a way that is readily understandable. The ECOLE project also will have two technical areas: distributed curriculum learning; and human-machine collaborative analysis.

Distributed curriculum learning involves multimedia data, and will use human partners provide feedback on the learning process. human-machine collaborative analysis will involve a human-machine interface (HMI) to improve ECOLE representations and analyze data such as multimedia and social media.

Companies interested should upload abstracts no later than 29 Sept. 2022, and full proposals by 14 Nov. 2022 to the DARPA BAA website at https://baa.darpa.mil.

Email questions or concerns to DARPA at ECOLE@darpa.mil. More information is online at https://sam.gov/opp/fd50cb65daf5493d886fa1ddc2c0dd77/view.

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Wanted: artificial intelligence (AI) and machine learning to help humans and computers work together - Military & Aerospace Electronics

Using AI, machine learning and advanced analytics to protect and optimize business | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Using AI, machine learning and advanced analytics to protect and optimize business - Security Magazine

Researchers found bronchopulmonary dysplasia (BPD) was one of the premature births most common and significant consequences. It was critical to make a timely diagnosis by utilizing prediction techniques so that action could be taken quickly to mitigate any negative impacts. The study aimed to use machine learning and the idea that BPD has a developmental genesis to create a tool for predicting whether or not a person would acquire BPD. Preliminary model development made use of datasets including prenatal variables and early postnatal respiratory assistance; subsequent model combinations made use of logistic regression to yield an ensemble model. The simulation of medical situations was carried out. Results from 689 newborns were included. For model building, investigators randomly chose data from 80% of newborns, while data from 20% was used for validation. Receiver operating characteristic curves used to evaluate the final models performance yielded values of 0.921 (95% CI: 0.899-0.943) for the training dataset and 0.899 (95% CI]: 0.848-0.949) for the validation dataset. Compared to NIPPV, extubation to CPAP appears to improve BPD-free survival in simulations. Successful extubation may also be defined as the absence of the need for reintubation within 9 days of the initial extubation. Clinical utility of machine learning-based BPD prediction using perinatal characteristics and respiratory data may exist to facilitate early targeted intervention in high-risk infants.

Source: bmcpediatr.biomedcentral.com/articles/10.1186/s12887-022-03602-w

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The Prediction of Bronchopulmonary Dysplasia Free Survival in Very Preterm Infants Using Machine Learning - Physician's Weekly

There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems.

For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects.

In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process. Working solo on the project also makes you better at problem-solving.

mRNA Degradation project is a complex regression problem. The challenge in this project is to predict degradation rates that can help scientists design more stable vaccines in the future.

The project is 2 years old, but you will learn a lot about solving regression problems using complex 3D data manipulation and deep learning GRU models. Furthermore, we will be predicting 5 targets: reactivity, deg_Mg_pH10, deg_Mg_50C, deg_pH10, deg_50C.

Automatic Image Captioning is the must-have project in your resume. You will learn about computer vision, CNN pre-trained models, and LSTM for natural language processing.

In the end, you will build the application on Streamlit or Gradio to showcase your results. The image caption generator will generate a simple text describing the image.

You can find multiple similar projects online and even create your deep learning architecture to predict captions in different languages.

The primary purpose of the portfolio project is to work on a unique problem. It can be the same model architecture but a different dataset. Working with various data types will improve your chance of getting hired.

Forecasting using Deep Learning is a popular project idea, and you will learn many things about time series data analysis, data handling, pre-processing, and neural networks for time-series problems.

The time series forecasting is not simple. You need to understand seasonality, holiday seasons, trends, and daily fluctuation. Most of the time, you dont even require neural networks, and simple linear regression can provide you with the best-performing model. But in the stock market, where the risk is high, even a one percent difference means millions of dollars in profit for the company.

Having a Reinforcement Learning project on your resume gives you an edge during the hiring process. The recruiter will assume that you are good at problem-solving and you are eager to expand your boundaries to learn about complex machine learning tasks.

In the Self-Driving car project, you will train the Proximal Policy Optimization (PPO) model in the OpenAI Gym environment (CarRacing-v0).

Before you start the project, you need to learn the fundamentals of Reinforcement Learning as it is quite different from other machine learning tasks. During the project, you will experiment with various types of models and methodologies to improve agent performance.

Conversational AI is a fun project. You will learn about Hugging Face Transformers, Facebook Blender Bot, handling conversational data, and creating chatbot interfaces (API or Web App).

Due to the huge library of datasets and pre-trained models available on Hugging Face, you can basically finetune the model on a new dataset. It can be Rick and Morty conversation, your favorite film character, or any celebrity that you love.

Apart from that you can improve the chatbot for your specific use case. In case of medical application. The chatbot needs technical knowledge and understands the patient's sentiment.

Automatic Speech Recognition is my favorite project ever. I have learned everything about transformers, handling audio data, and improving the model performance. It took me 2 months to understand the fundamentals and another two to create the architecture that will work on top of the Wave2Vec2 model.

You can improve the model performance by boosting Wav2Vec2 with n-grams and text pre-processing. I have even pre-processed the audio data to improve the sound quality.

The fun part is that you can fine-tune the Wav2Vec2 model on any type of language.

End-to-end machine learning project experience is a must. Without it, your chance of getting hired is pretty slim.

You will learn:

The main purpose of this project is not about building the best model or learning new deep learning architecture. The main goal is to familiarize the industry standards and techniques for building, deploying, and monitoring machine learning applications. You will learn a lot about development operations and how you can create a fully automated system.

After working on a few projects, I will highly recommend you create a profile on GitHub or any code-sharing site where you can share your project findings and documentation.

The principal purpose of working on a project is to improve your odds of getting hired. Showcasing the projects and presenting yourself in front of a potential recruiter is a skill.

So, after working on a project, start promoting it on social media, create a fun web app using Gradio or Streamlit, and write an engaging blog. Dont think about what people are going to say. Just keep working on a project and keep sharing. And I am sure in no time multiple recruiters will approach you for the job.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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7 Machine Learning Portfolio Projects to Boost the Resume - KDnuggets

The module, aimed entirely at professional investors, consists of six chapters and is valid for one hour of training for re-certification from EFPA Spain or 1 CPD credit from CFA Society Spain.

When people talk about data intelligence, they often use the terms machine learning (ML), artificial intelligence (AI), natural language processing (NLP), and deep learning (DL) interchangeably. But what is the difference between these methods? Why is it increasing in importance in asset management? How does quantitative investing apply to the investment process? Many doubts that we will solve in the six chapters in which the module is structured Machine learning and quantitative investing: how to incorporate them into portfolio buildingWhich is supported by Goldman Sachs AM.

You already know the system of adding continuing education hours. Once the test is completed and answered, module can be validated as 1 cpd credit CFA Society Spain How One Hour Recertification Training EIA, EIP, EFA and EFP D EFPA Spain, provided you successfully complete the knowledge test at the end of this module.

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New FundsPeople Learning Module! Machine Learning And Quantitative Investing: How To Incorporate Them Into A Portfolio With Goldman Sachs AM - World...

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See the rest here:
Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder |...

Not familiar with BizarroMath? Youre in luck; Ive launched a web site for it where you can get an explanation of the numbers and browse the data.

Week 1

Week 2

Week 3

All lines courtesy of DraftKings Sportsbook as of 8:00am, Monday, September 19, 2022.

Iowa football continues to be the #1 supplier of high-quality material for the Sickos Committee.

This week, BizarroMath went 4-8 ATS and 5-7 O/U. Combined with the prior record of 11-8 and 6-13, respectively, the algorithm is 15-16 ATS and 11-20 O/U on the season after three full weeks of play. Not a great outing in the second straight strange week of Division I NCAA Football, but were still learning about these teams.

Vegas Says: MI -46.5, U/O 57.5

BizarroMath Says: MI -64.10 (MI cover), 71.66 (over)

Actual Outcome: MI 59, UCONN 0 (ATS hit, O/U hit)

One Sentence Recap: Michigan aint played nobody.

Vegas Says: OU -11.5, O/U 64.5

BizarroMath Says: OU -7.90 (NE cover), 60.03 (under)

Actual Outcome: OU 49, NE 14 (ATS miss, O/U hit)

One Sentence Recap: We should all be rooting for Mickey Josephs no-nonsense, just play the damn game style, which is a welcome departure from Scott Frosts chesty preening, but Nebraska still seems mired in a deep hole of undisciplined play and softness at the point of attack.

Vegas Says: n/a

BizarroMath Says: n/a

Actual Outcome: SILL 31, NU 24

One Sentence Recap: I watched the Salukis play many at a game at the UNI Dome in Cedar Falls over the years and I was probably less surprised than many that they pulled off this upset.

Vegas Says: Pk, O/U 58.5

BizarroMath Says: PUR -2.22 (Purdue win), O/U 47.9 (under)

Actual Outcome: SYR 32, PUR 29 (ATS miss, O/U miss)

One Sentence Recap: Much like the Penn State game, this game was there for the taking and Purdue simply refused, and I want to reiterate that Ive been skeptical since before the season began that the 2022 Edition of Purdue would be able to maintain the momentum from last year.

Vegas Says: IN -6.5, O/U 59.0

BizarroMath Says: WKY -12.90 (WKY cover), 65.99 (over)

Actual Outcome: IND 33, WKY 30 (ATS hit, O/U hit)

One Sentence Recap: BizMas prediction of a WKY upset damn near came true, but Tom Allens sweeping, must win now changing in the offseason seem to be paying dividends as the Hoosiers are figuring some things out and finding ways to win.

Vegas Says: RUT -17.5, O/U 44

BizarroMath: RUT -23.74 (RUT cover), 44.62 (over)

Actual Outcome: RUT 16, TEM 14 (ATS miss, O/U miss)

One Sentence Recap: Im pretty sure Rutgers is close to its pre-season O/U win total already, as the Scarlet Knights, like the other Eastern Red Team, keep finding ways to win.

Vegas Says: PSU -3, O/U 49

BizarroMath: PSU -2.71 (Auburn cover), 44.70 (under)

Actual Outcome: PSU 41, AUB 12 (ATS miss, O/U miss)

One Sentence Recap: It just means more.

Vegas Says: MN -27.5, O/U 46.5

BizarroMath: MN -23.95 (CO cover), 44.20 (under)

Actual Outcome: MN 49, CO 7 (ATS miss, O/U miss)

One Sentence Recap: Minnesota aint played nobody.

Vegas Says: WI -37.5, O/U 46.5

BizarroMath: WI -38.71 (WI cover), 50.1 (over)

Actual Outcome: WI 66, NMSU 7 (ATS hit, O/U hit)

One Sentence Recap: Theres nothing interesting about this game other than two observations: (1) this is the most points Wisconsin has scored in the Paul Chryst era; (2) Wisconsin has the same problem as Iowa in that Chryst has probably hit his ceiling and isnt going to elevate the program any further, but he wins too much to let him go.

Vegas Says: OSU -31.5, O/U 61

BizarroMath: OSU -28.36 (Toledo cover), 66.12 (over)

Actual Outcome: OSU 77, TOL 21 (ATS miss, O/U hit)

One Sentence Recap: OSUs opponent-adjusted yards surrendered this year is an absurd 2.28, which could be more a function of the small sample size we have for their opponents than anything, but this is why I blend data, folks.

Vegas Says: -MSU 3, O/U 57.5

BizarroMath: MSU -8.44 (MSU cover), 50.28 (under)

Actual Outcome: WA 39, MSU 28 (ATS miss, O/U miss)

One Sentence Recap: Ive told you my numbers dont like the Spartans, and Washington just showed us why.

Vegas Says: IA -23, O/U 40

BizarroMath: IA -2.48 (Nevada cover), 47.22 (over)

Actual Outcome: IA 27, NE 0 (ATS miss, O/U miss)

One Sentence Recap: Weird how when you inject a bunch of scholarship players back into your line-up, and play a defense of dubious quality, you can kind of, sort of, move the ball a little bit, even with an historically incompetent offense.

Vegas Says: MD -3.5, O/U 69.5

BizarroMath: SMU -1.31 (SMU cover), 75.22 (over)

Actual Outcome: MD 34, SMU 27 (ATS hit, O/U miss)

One Sentence Recap: First Four Games Maryland has scored 121 points through 3 games; I put the O/U on how many more games before Iowa breaks that mark at 5.5.

Now that I have the http://www.BizarroMath.com web site up and running, you can take a look at Iowas game-by-game projections and season projections yourself. Im going to not post the images this week and leave it to you to visit the site if you want to see the data. This is not a clickbait money scheme. There are no ads on that site, I wrote the HTML by hand because Im old and thats how I roll, and I make $0 off you visiting that site.

If you prefer to have the data presented in-line here, let me know, I will do that next week. Please answer the poll below to help me figure out how best to do this.

5%

21%

54%

13%

4%

1%

Also Caveat: If you come back to these links in the future, they will be updated with the results of future games, which also is a reason to post the data here for posterity, I suppose. Anyway, I may change the web site in the future to provide week-by-week updates showing the net changes. If youre interested in that, please let me know.

On to the analysis.

We finally have two FBS games worth of data on Iowa and can we start jumping to conclusions. Iowas raw PPG against D1 competition are at 17.0, which is good for #108 in the country. Iowas raw YPG are 243.50, which puts the Hawkeyes at #115. Iowas raw YPP are 4.20, ranking the Black and Old Gold at #110. The team is very slowly crawling out of the Division 1 cellar, but didnt exactly light the world on fire Saturday in a wet, frequently-interrupted outing against a Nevada team widely regarded as being Not Very Good.

We dont have enough data for opponent-adjustments for Iowa at this point (I require at least 3 adjustable games). Iowas blended data is what is used for the projections, and you can review that on the BizarroMath.com web site. Suffice it to say that Iowas outing against Nevada was similar in profile to what the team looked like last year. But, the schedule is a bit tougher this year, and Iowa needed some good fortune last year to make the Big 10 Championship game. I know nobody wants to hear it, but if this offense can climb up out of the triple digit rankings and get even to the 80th-90th range, that just might be enough to stay in the conference race.

But this season may simply boil down to schedule. Wisconsins cross-over games are @Ohio State, @MSU, and Maryland at home. Thats about as hard as it gets without playing either Michigan or Penn State. Minnesotas cross-over games are @Penn State, @Michigan State, and Rutgers. Iowas are @Rugers, Michigan, @Ohio State. From most to least difficult, Id say Iowa has the worst draw, then Wisconsin, then Minnesota. The Gophers also get Purdue and Iowa at home, and have Nebraska, Wisconsin, and Illinois on the road. The Badgers have Illinois and Purdue at home and go on the road to play Nebraska, Iowa, and (dont laugh) Northwestern. The Badgers are 1-6 at Northwestern this Century. The schedule generally favors the Gophers, and with Iowa playing Michigan and Ohio State in October, we shouldnt be surprised if the Hawkeyes are out of the division race before November.

That said, Iowas game-by-game odds are moving in the right direction. Iowa is a significant underdog vs. Michigan and Ohio State as expected, and a slight dog to Wisconsin and (stop traffic) Illinois. Perhaps most alarming is that the Hawkeyes have only a 37.92% chance to beat Minnesota. But! Recall that I am not doing opponent adjustments to the 2022 data yet for Minnesota, so their gaudy numbers are being taken at face value, and theyll drop after the Gophers play Michigan State this weekend.

To give you an idea of how that works, consider Michigan, which has played enough adjustable games that I can run opponent adjustments. Their opposition has been so terrible that BizarroMath discounts the Wolverines raw 55.33 PPG by a whopping 22.54 points. This means that this Wolverine team is expected to put up just 32.80 points against an average D1 defense, to say nothing of what they can do against a Top 5 defense, which Iowa just so happens to have (again, before opponent-adjustments). Michigans adjusted data is thus actually worse than last year, whose offense was, opponent-adjusted, worth 42.17 PPG.

Minnesotas adjustments will come soon enough, and well see them return to deep below the Earth, where filthy rodents belong. But, so, too, will Iowas, and of Iowas three adjustable opponents after this coming weekend - Rutgers, Nevada, and Iowa State - the Cyclones are by far the best team.

Iowa Season Projections

The Nevada win and swing in the statistics towards something more similar to last years putrid but still-better-than-this-crap offensive performance has brightened Iowas season outlook somewhat. Iowas most likely outcome now is 7-5 (27.13% chance), with 6-6 being more likely (25.89%) than 8-4 (17.52%). There is a 92.11% chance that Iowa doesnt reach 9.3, and a 78.42% chance that the Hawkeyes get bowl eligible this year.

The Gilded Rodents flashy numbers have pulled them almost even with Wisconsin, as the Badgers and Gophers are both in the 35-40% range for a division championship. Purdues continued struggles drops the Boilermakers to the four spot, elevating hapless Iowa to the third place in the West, though the Hawkeyes chances of actually winning the damn thing drop to 8.40%, Iowas climb up the division ladder from 5th to 3rd is more a function of the poor play of the teams now ranked lower than anything Iowa is doing on the field.

Im a bit puzzled by the conference race in the East, where Ohio State shot from last weeks 21.53% to this weeks 64.18% chance, but I think its because BizMa now has the Buckeyes with a 77.74% chance of winning The Game, which is the main shift that accounts for this change. Why? Well, this week we have opponent-adjustments for both teams and OSU has played a tougher schedule, so the Buckeyes numbers are not being discounted nearly as much as Michigans.

For example, on offense, OSU is putting up a raw 8.49 YPP, which BizMa is actually adjusting up to 9.58. Michigan, by comparison, is putting up 7.97 YPP, but BizMa is adjusting it down to 6.36 YPP based on the competition. As we move into the conference slate and the quality of each teams opposition evens out, well probably see those numbers flatten out a bit.

I love week 4. Because the number of games I have to track is cut in half.

Vegas Says: n/a

BizarroMath: n/a

One Sentence Prediction: Your Fighting Illini are going to be 3-1 going into conference play, and they have been competitive, if a bit raggety.

Vegas Says: MI -17, O/U 62.5

BizarroMath: MI -3.81, O/U 58.01 (MD cover, under)

One Sentence Prediction: BizMa sees this game as being much closer than Vegas does, and I think the difference might be a function of where we are in the season, as I just dont see Marylands defense holding Michigan down, and I dont buy that under for even a second, folks.

Vegas Says: PSU -26, O/U 60.5

BizarroMath: PSU -30.47, O/U 58.97 (PSU cover, under)

One Sentence Prediction: I dont know a thing about Central Michigan this year but a final along the lines of 45-13 sounds about right.

Vegas Says: MN -2, O/U 51.0

BizarroMath: MN -8.75, O/U 45.49 (MN cover, under)

One Sentence Prediction: Well soon know if the Gilded Rodents are fools gold, but not this week, as I think Minnesota is going to put up some points here in something like a 42-23 affair.

Vegas Says: CIN -15.5, O/U 54.0

BizarroMath: CIN -25.04, O/U 53.90 (CIN cover, under)

One Sentence Prediction: The Hoosiers either crash hard back down to Terra Firma in an embarrassing road rout, or this winds up being an unexpectedly knotty game.

Vegas Says: IA -7.5, O/U 35.5

BizarroMath: IA -9.85, O/U 32.09 (IA cover, under)

One Sentence Prediction: In Assy Football, the MVP is from one of two separate, yet equally important, groups: the punt team, which establishes poor field position for the opposition; and the punt return team, who try to field the ball outside of the 15 yard line without turning it over; this is their magnum opus.

Vegas Says: OSU -17.5, O/U 56.5

Read more:
Machine Learning Week 4 - Updated Iowa Game by Game Projections, Season Record, and Championship Odds - Black Heart Gold Pants

Google-Powered Julian Teaches and Learns at Walden University

Steven Tom, chief customer officer atAdtalem Global Education, was at a conference several years ago and saw a demonstration of an AI tutor that left him thinking bigger. The tool he saw took student questions and answered them according to what he called a script, following a preset path programmed by a human on the back end that, the idea was, eventually took the student to the right answer. Itsounded a lot like adaptive learning, a concept that has been around for decades and has failed to take off, in part because it takes a lot of effort to program and is fairly inflexible in how it responds.

Somebody had to spend hours and hours and days and days creating the questions, scripting how the tutor was going to interact with the student, Tom says. And then the AI portion of it was like when you read a news article and give it a thumbs-up, thumbs-down the kind of learning based on whether you liked the question or not. In that sense, it really wasnt dynamic, and it really wasnt scalable.

That experience set Tom on a mission to solve for both of those weaknesses.

His first step was to reach out to the teams atGoogleworking in AI and higher education. Toms team atWalden University, an online university recently acquired by Adtalem, worked with Google to build the AI tutor that wouldbe introduced to students and faculty as Julianin the spring of 2021.

We wanted to see if we could tackle this challenge of creating a truly dynamic, truly unscripted AI tutor that could essentially ingest content on its own, make sense of it, pull the key concepts out of it and then foster with the student a real tutoring session where it can generate its own questions and assess the students answers completely on its own, Tom says. At the time, it was kind of a pipe dream.

Today, Tom says, that dream has at least partly come true.

Julian is more dynamic than most AI tutors. By design, it was rolled out in courses where its not easy for a machine to tell if the answer is right or wrong (as opposed to, say, a math course). At Walden, Julians first courses were in early childhood education and sociology.

DISCOVER:Successful AI examples in higher education that can inspire our future.

And while Julian is not exactly judging right or wrong it doesnt do any grading, for example it is ingesting information and learning as it goes. Its using the same course materials the students are given, and it directs them back to those sources in response to the questions they ask. Then it takes those questions and answers and feeds them back into itself to learn what students are asking about and grow even smarter for next time, Tom says.

Because it was developed with Google, Julian lives in theGoogle Cloud, meaning it has taken little to no infrastructure investment on Waldens part. Tom says programmers opted to use an application programming interface to drive the tool, with an eye on its future use.

Since it is API-driven, Tom says, Julian can potentially appear in many environments: While its currently a chatbot embedded in the universitys learning management system, it could easily exist as an avatar in an augmented or virtual reality environment, or as a voice on the phone, in the future.

The future is also where the innovative team at Georgia Tech is focused. The university that first brought Jill Watson to life has since rolled out two new tools related to AI tutoring:AskJillandAgent Smith. The Atlanta-based university also recently helped establish theNational AI Institute for Adult Learning and Online Education(AI-ALOE) thanks to a five-year, $20 milliongrant from the National Science Foundation.

The grant will fund large-scale investments in technology infrastructure, according to Ashok Goel, a computer science professor at Georgia Tech and the executive director of AI-ALOE. The investments will go towarddata storage, compliance andcybersecurity.

AI-ALOE is interested particularly in adult learners, as the name suggests. Goel says he anticipates millions of Americans will need to be reskilled or upskilled in the coming decade as automation changes the way we work, and those people wont be traditional college students. They will have families, jobs and other responsibilities, and they may not get to their coursework until the wee hours of the night or on the weekends, when professors, teaching assistants and human tutors are not available, increasing the need for AI to help.

READ MORE:Improve online learning and more with artificial intelligence.

As for the initiative itself, Goel wants to bring AI tutors to the world on a massive scale. Jill Watson is great, he says, but the effort it takes to create one Jill Watson makes it nearly impossible to replicate at other higher education institutions or in K12 environments. And the programs other goal improving the AI capabilities itself will go hand in hand with expanded use of a tool like Jill Watson.

Goel says Georgia Techs AI tools have learned from more than 40,000 user questions over the years. That may seem impressive at first, but Goel says 4 million questions would be a much nicer base to learn from.

Even as the AI is getting smarter, the latest version of the AI is designed to help expand its reach. Agent Smith (named after the self-cloning antagonist inThe Matrixmovies) offers the most intriguing potential for large-scale growth. Agent Smith is a cloner, capable of replicating a Jill Watson AI tutor for courses and classrooms around the country in as little as five hours. Thats still too long, Goel says, and the AI tutor interface is still a little clunky, but the team is making progress.

Why cant we offer AI tutors to every teacher and every learner and class in the world? Goel asks. If Jill Watson remains solely the provenance of 30 classes, thats interesting, but its not a game changer. It becomes a game changer only if anyone can use it.

See the original post here:
The Future of AI Tutors in Higher Education - EdTech Magazine: Focus on K-12

--(BUSINESS WIRE)--Astera Labs Inc. :

WHEN:

Wednesday, September 21, 2022, from 9:30 a.m.-11:30 a.m. PDT

WHERE:

Astera Labs Vancouver4370 Dominion StreetBurnaby, BC V5G 4L7Canada

WHO:

WHAT:

Astera Labs welcomes the Mayor of Burnaby and the Burnaby Board of Trade President and CEO to celebrate the grand opening of its new state-of-the-art design center and lab in the Greater Vancouver Area.

Astera Labs Vancouver will support the companys development of cutting-edge interconnect technologies for Artificial Intelligence and Machine Learning architectures in the Cloud. The rapidly growing semiconductor company chose the Vancouver area to tap into the regions rich technology talent base to drive product development, customer support and marketing. The Vancouver location increases the companys operations in Canada, which already includes the new Research and Development Design Center in Toronto, and adds to its global footprint with headquarters in Santa Clara, California and offices around the globe.

Astera Labs is actively hiring across multiple engineering and marketing disciplines to support end-to-end product and application development and overall go-to-market operations. Open positions can be found at http://www.AsteraLabs.com/Careers/.

The ribbon cutting and photo opportunity with Burnaby Officials and Astera Labs Executives will be held outdoors. Below is an overview of the event agenda:

Event Schedule

Formal Remarks

9:30 a.m. 10:00 a.m. PDT

Ribbon Cutting / Photo Op / Media Q&A

10:00 a.m. 10:30 a.m. PDT

Indoor Reception

10:30 a.m. 11:30 a.m. PDT

For onsite assistance, contact Dave Nelson at (604) 418-9930.

About Astera Labs

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Mark Packer, MD, sat down with Sheryl Stevenson, Group Editorial Director,Ophthalmology Times, to discuss his presentation on machine learning and predicting vision outcomes after cataract surgery at the ASCRS annual meeting in San Diego

Editors note:This transcript has been edited for clarity.

We're joined by Dr. Mark Packer, who will be presenting at this year's ASCRS. Hello to Dr. Packard. Great to see you again.

Good to see you, Sheryl.

Sure, tell us a little bit about your talk about machine learning, and visual, predicting vision outcomes after cataract surgery.

Sure, well, as we know, humans tend to be fallible, and even though surgeons don't like to admit it, they have been prone to make errors from time to time. And you know, one of the errors that we make is that we always extrapolate from our most recent experience. So if I just had a patient who was very unhappy with a multifocal IOL, all of a sudden, I'm going to be a lot more cautious with my next patient, and maybe the one after that, too.

And, the reverse can happen as well. If I just had a patient who was absolutely thrilled with their toric multifocal, and they never have to wear glasses again, and they're leaving for Hawaii in the morning, you know, getting a full makeover, I'm going to think, wow, that was the best thing I ever did. And now all of a sudden, everyone looks like a candidate. and even for someone like me, who has been doing multifocal IOL for longer than I care to admit, you know, this can still pose a problem. That's just human nature.

And, so what we're attempting to do with the oculotics program is to bring a little objectivity into the mix. Now, of course, we already do that, when we talked about IOL power calculations, we, we leave that up to algorithms and let them do the work. One of the things that we've been able to do with oculotics is actually improve upon the way that power calculations are done. So rather than just looking at the Dioptric power of a lens, for example, we're actually looking at the real optical properties of the lens, the modulation transfer function, in order to help correlate that with what a patient desires in terms of spectacle independence.

But the real brainchild here is the idea of incorporating patient feedback after surgery into the decision making process. So part of this is actually to give our patients and app that they can use to then provide feedback on their level of satisfaction, essentially, by filling out the VFQ-25, which is a simply, a 25 item questionnaire that was developed in the 1990s by RAND Corporation, to look at visual function and how satisfied people are with their vision, whether they have to worry about it, and how they feel about their vision, that sort of thing, whether they can drive at night comfortably and all that.

So if we can incorporate that feedback into our decision making, now instead of my going into the next room, you know, with fresh in my mind just what happened today, actually, I'll be incorporating the knowledge of every patient that I've operated on since I started using this system, and how they fared with these different IOLs.

So the machine learning algorithm can actually take this patient feedback and put that together with the preoperative characteristics such as, you know, personal items, such as hobbies, what they do for recreation, what their employment is, what kind of visual demands they have. And also anatomic factors, you know, the axial length, anterior chamber depth, corneal curvature, all of that, put that all together, and then we can begin to match inter ocular lens selection, actually to patients based not only on their biometry, but also on their personal characteristics, and how they actually felt about the results of their surgery.

So that's how I think machine learning can help us, and hopefully bring surgeons up to speed with premium IOLs more quickly because, you know, it's taken some of us years and years to gain the experience to really become confident in selecting which patients are right for premium lenses, particularly multifocal extended depth of focus lenses and that sort of thing where, you know, there are visual side effects, and there are limitations, but there also are great advantages. And so hopefully using machine learning can bring young surgeons up more quickly increase their confidence and allow them to increase the rate of adoption among their patients for these premium lenses.

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ASCRS 2023: Predicting vision outcomes in cataract surgery with ... - Ophthalmology Times

SAN JOSE, Calif., Sept. 23, 2022 /PRNewswire/ -- RealPars, the leader in cutting-edge industrial education, and Edge Impulse, the best-in-class edge machine learning platform, today announce that they are teaming up to provide new and groundbreaking content to further development of advanced manufacturing tools and techniques.

The initial partnership focuses on predictive maintenance programming, leveraging RealPars' innovative training platform to show how Edge Impulse's machine learning tools can optimize maintenance cycles on industrial equipment. Predictive maintenance is quickly becoming a highly sought-after method of upkeep in industrial settings. Through the use of ML algorithms, embedded sensors, and onboard computing, the technique can be used to detect anomalies in machinery before breakdowns occur, allowing for just-in-time repair or replacement in order to maximize uptime and minimize costly shut downs. The partnership between Edge Impulse and RealPars will help build awareness and advancement of the practice.

Edge Impulse, the leading development platform for ML on edge devices, allows developers to quickly and easily create and optimize solutions with real-world data. The company's platform streamlines the entire process of collecting and structuring datasets, designing ML algorithms with ready-made building blocks, validating the models with real-time data, and deploying the fully optimized production-ready result to an edge target. The Edge Impulse development platform, already in use by thousands of companies, stands to unlock massive value across manufacturing and many other industries, with millions of developers making billions of devices smarter.

The two firms will kick off the partnership with a workshop on predictive maintenance; more information can be found on the partnership landing page, and will be announced at Imagine, Edge Impulse's ML conference, this September 28. Register at edgeimpulse.com/imagine

Edge Impulse is the leading machine learning platform, enabling all enterprises to build smarter edge products. Their technology empowers developers to bring more ML products to market faster, and helps enterprise teams rapidly develop industry-specific solutions in weeks instead of years. The Edge Impulse platform provides powerful automation and low-code capabilities to make it easier to build valuable datasets and develop advanced ML with streaming data. With over 40,000 developers, and partnerships with the top silicon vendors, Edge Impulse offers a seamless integration experience to validate and deploy with confidence across the largest hardware ecosystem. To learn more, visit edgeimpulse.com.

RealPars is the world's largest online learning platform for cutting-edge industrial technologies.

Their goal is to give anyone in the world the ability to learn new skills they need to succeed in their career as an engineer in the industrial space. RealPars is set out to create a new, easy-to-follow way of learning - making it engaging, flexible, and accessible for as many people as possible. Explaining complicated engineering concepts in an easy-to-follow format is what sets RealPars apart from any other learning platform. Since launching the platform in 2018, RealPars has helped millions of people around the globe unlock modern technical skills and reach their full potential. To learn more, head on over to realpars.com.

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Edge Impulse and RealPars Announce Automation Technology Content Partnership - PR Newswire

In a first-of-its-kind effort, social media researchers from Drexel University, Vanderbilt University, Georgia Institute of Technology and Boston University are turning to young social media users to help build a machine learning program that can spot unwanted sexual advances on Instagram. Trained on data from more than 5 million direct messages annotated and contributed by 150 adolescents who had experienced conversations that made them feel sexually uncomfortable or unsafe the technology can quickly and accurately flag risky DMs.

The project, which was recently published by the Association for Computing Machinery in its Proceedings of the ACM on Human-Computer Interaction, is intended to address concerns that an increase of teens using social media, particularly during the pandemic, is contributing to rising trends of child sexual exploitation.

In the year 2020 alone, the National Center for Missing and Exploited Children received more than 21.7 million reports of child sexual exploitation which was a 97% increase over the year prior. This is a very real and terrifying problem, said Afsaneh Razi, PhD, an assistant professor in Drexels College of Computing & Informatics, who was a leader of the research.

Social media companies are rolling out new technology that can flag and remove sexually exploitative images and helps users to more quickly report these illegal posts. But advocates are calling for greater protection for young users that could identify and curtail these risky interactions sooner.

The groups efforts are part of a growing field of research looking at how machine learning and artificial intelligence be integrated into platforms to help keep young people safe on social media, while also ensuring their privacy. Its most recent project stands apart for its collection of a trove of private direct messages from young users, which the team used to train a machine learning-based program that is 89% accurate at detecting sexually unsafe conversations among teens on Instagram.

Most of the research in this area uses public datasets which are not representative of real-word interactions that happen in private, Razi said. Research has shown that machine learning models based on the perspectives of those who experienced the risks, such as cyberbullying, provide higher performance in terms of recall. So, it is important to include the experiences of victims when trying to detect the risks.

Each of the 150 participants who range in age from 13- to 21-years-old had used Instagram for at least three months between the ages of 13 and 17, exchanged direct messages with at least 15 people during that time, and had at least two direct messages that made them or someone else feel uncomfortable or unsafe. They contributed their Instagram data more than 15,000 private conversations through a secure online portal designed by the team. And were then asked to review their messages and label each conversation, as safe or unsafe, according to how it made them feel.

Collecting this dataset was very challenging due to sensitivity of the topic and because the data is being contributed by minors in some cases, Razi said. Because of this, we drastically increased the precautions we took to preserve confidentiality and privacy of the participants and to ensure that the data collection met high legal and ethical standards, including reporting child abuse and the possibility of uploads of potentially illegal artifacts, such as child abuse material.

The participants flagged 326 conversations as unsafe and, in each case, they were asked to identify what type of risk it presented nudity/porn, sexual messages, harassment, hate speech, violence/threat, sale or promotion of illegal activities, or self-injury and the level of risk they felt either high, medium or low.

This level of user-generated assessment provided valuable guidance when it came to preparing the machine learning programs. Razi noted that most social media interaction datasets are collected from publicly available conversations, which are much different than those held in private. And they are typically labeled by people who were not involved with the conversation, so it can be difficult for them to accurately assess the level of risk the participants felt.

With self-reported labels from participants, we not only detect sexual predators but also assessed the survivors perspectives of the sexual risk experience, the authors wrote. This is a significantly different goal than attempting to identify sexual predators. Built upon this real-user dataset and labels, this paper also incorporates human-centered features in developing an automated sexual risk detection system.

Specific combinations of conversation and message features were used as the input of the machine learning models. These included contextual features, like age, gender and relationship of the participants; linguistic features, such as wordcount, the focus of questions, or topics of the conversation; whether it was positive, negative or neutral; how often certain terms were used; and whether or not a set of 98 pre-identified sexual-related words were used.

This allowed the machine learning programs to designate a set of attributes of risky conversations, and thanks to the participants assessments of their own conversations, the program could also rank the relative level of risk.

The team put its model to the test against a large set of public sample conversations created specifically for sexual predation risk-detection research. The best performance came from its Random Forest classifier program, which can rapidly assign features to sample conversations and compare them to known sets that have reached a risk threshold. The classifier accurately identified 92% of unsafe sexual conversations from the set. It was also 84% accurate at flagging individual risky messages.

By incorporating its user-labeled risk assessment training, the models were also able to tease out the most relevant characteristics for identifying an unsafe conversation. Contextual features, such as age, gender and relationship type, as well as linguistic inquiry and wordcount contributed the most to identifying conversations that made young users feel unsafe, they wrote.

This means that a program like this could be used to automatically warn users, in real-time, when a conversation has become problematic, as well as to collect data after the fact. Both of these applications could be tremendously helpful in risk prevention and the prosecution of crimes, but the authors caution that their integration into social media platforms must preserve the trust and privacy of the users.

Social service providers find value in the potential use of AI as an early detection system for risks, because they currently rely heavily on youth self-reports after a formal investigation had occurred, Razi said. But these methods must be implemented in a privacy-preserving matter to not harm the trust and relationship of the teens with adults. Many parental monitoring apps are privacy invasive since they share most of the teen's information with parents, and these machine learning detection systems can help with minimal sharing of information and guidelines to resources when it is needed.

They suggest that if the program is deployed as a real-time intervention, then young users should be provided with a suggestion rather than an alert or automatic report and they should be able to provide feedback to the model and make the final decision.

While the groundbreaking nature of its training data makes this work a valuable contribution to the field of computational risk detection and adolescent online safety research, the team notes that it could be improved by expanding the size of the sample and looking at users of different social media platforms. The training annotations for the machine learning models could also be revised to allow outside experts to rate the risk of each conversation.

The group plans to continue its work and to further refine its risk detection models. It has also created an open-source community to safely share the data with other researchers in the field recognizing how important it could be for the protection of this vulnerable population of social media users.

The core contribution of this work is that our findings are grounded in the voices of youth who experienced online sexual risks and were brave enough to share these experiences with us, they wrote. To the best of our knowledge, this is the first work that analyzes machine learning approaches on private social media conversations of youth to detect unsafe sexual conversations.

This research was supported by the U.S. National Science Foundation and the William T. Grant Foundation.

In addition to Razi, Ashwaq Alsoubai and Pamela J. Wisniewski, from Vanderbilt University; Seunghyun Kim and Munmun De Choudhury, from Georgia Institute of Technology; and Shiza Ali and Gianluca Stringhini, from Boston University, contributed to the research.

Read the full paper here: https://dl.acm.org/doi/10.1145/3579522

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Sliding Out of My DMs: Young Social Media Users Help Train ... - Drexel University

May 3, 2023 by Diana Ambolis

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Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and

Non-fungible tokens (NFTs) have exploded in popularity over the past year, with many investors seeking to capitalize on this emerging market. However, with NFT values often fluctuating rapidly, it can be difficult for investors to know when to buy or sell. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help inform investment decisions and maximize returns.

Machine learning algorithms can be trained to analyze a range of data points and variables that are relevant to NFT value. This could include factors such as the artists reputation, the rarity of the NFT, the size of the NFT market, and even social media sentiment around a particular NFT. By analyzing this data, machine learning algorithms can identify patterns and correlations that can be used to predict the future value of a given NFT.

Determining the true value of an NFT can be challenging, with many factors to consider, including the artists reputation, the rarity of the NFT, and social media sentiment around a particular NFT. Machine learning offers a potential solution to this problem, providing investors with insights and predictive models that can help determine the value of NFTs. In this article, well explore the top 10 benefits of using machine learning to determine NFT value.

Machine learning offers a range of benefits for investors seeking to determine NFT value. By providing accurate predictions, improving efficiency, and reducing bias, machine learning can help investors make more informed decisions about NFT investments. As the NFT market continues to evolve, it is likely that machine learning will become an increasingly important tool for investors seeking to capitalize on this emerging market.

Also, read The Top 5 Best NFT Products So Far: A Closer Look

One of the key benefits of using machine learning for NFT investment is that it can help investors make more informed decisions about which NFTs to buy or sell. By providing insights and predictions about future value, machine learning algorithms can help investors identify undervalued NFTs that have strong potential for growth, as well as overvalued NFTs that may be at risk of declining in value.

Another benefit of using machine learning for NFT investment is that it can help investors manage risk. By providing predictive models and insights, machine learning algorithms can help investors understand the potential risks and rewards associated with a given NFT investment, allowing them to make more informed decisions about how to allocate their resources.

There are also potential drawbacks to using machine learning for NFT investment. For example, the accuracy of predictive models can be influenced by a range of factors, including the quality and quantity of data used to train the algorithm. In addition, the NFT market is still relatively new and untested, making it difficult to predict how the market will behave over time.

Despite these potential drawbacks, many investors are turning to machine learning as a way to inform their NFT investment decisions. As the NFT market continues to grow and evolve, machine learning is likely to become an increasingly important tool for investors seeking to capitalize on this emerging market.

Machine learning has the potential to revolutionize the world of NFT investment, providing investors with new insights and predictive models that can inform investment decisions and maximize returns. By analyzing a range of data points and variables, machine learning algorithms can identify patterns and correlations that can be used to predict NFT value and manage risk. While there are potential drawbacks to using machine learning in this context, the benefits are significant, and it is likely that this technology will become an increasingly important tool for investors seeking to capitalize on the emerging NFT market.

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Machine Learning And NFT Investment: Predicting NFT Value And ... - Blockchain Magazine

Econometric models and Machine Learning models in the Banking Industry from Regulatory Perspective  Global Banking And Finance Review

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Econometric models and Machine Learning models in the Banking Industry from Regulatory Perspective - Global Banking And Finance Review