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We have been overhyping deep learning for too long. Its time to start embracing it

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. It is focused on improving the AI process of having machines learn things. The core of deep learning lies in fast enough computers and enough data to train large neural networks. Deep learning became the focus of a hype cycle. Many companies use deep learning and advanced artificial intelligence to solve problems and their product services.

But deep learning is overhyped for too long a period to revert back. Meanwhile, media outlets often carried stories about artificial intelligence and deep learning that were misinformed. They were written by people who did not have a proper understanding of how the technology works. Many experts believe that DL is overhyped. Other prominent experts admit that deep learning has hit a wall, and this includes some of the researchers who were among the pioneers of deep learning and were involved in some of the most important achievements of the field.

Some people say that deep learning is just another name for machine learning, but its not correct. Deep learning is a subset of machine learning. People should stop trying to make ML/DL the solution to problems that might be more easily resolved by simple math. ML techniques have been in use for a long time, but deep learning is far superior to its peers.

An ML project needs data and a robust pipeline to support the data flows. And most of all, it needs high-quality labels. This last point highlights the need to get to know data. To label, it needs to understand the data to some degree. All of this needs to happen before starting throwing random data into a deep learning algorithm and praying for results.

As such, it would help to stop overselling the future of deep learning, machine learning, and artificial intelligence and instead, focus on the present need to better integrate human ingenuity with brute-force and machine-driven pattern matching.

Deep learning is essentially a way to do pattern matching at scale. Most importantly, deep learning has had limited success in particular areas only. These areas include reinforcement learning, adversarial models, and anomaly detection.

Some experts believe reinforcement learning involves developing AI models without providing them with a huge amount of labeled data. While deep reinforcement learning is one of the more interesting areas of AI research, it has limited success in solving real-world problems.

There have been several efforts to harden deep learning models against adversarial attacks, but so far, there has been limited success. Part of the challenge stems from the fact that artificial neural networks are very complex and hard to interpret.

Conclusion: It is important to remain tempered in our expectations of deep learning. As the world seemingly scrambles for The Master Algorithm one must keep in mind that deep learning is not machine learning; its a subset. While deep neural networks have their place, they wont solve all of humanitys woes. While deep learning is making waves, and deservedly so, keep in mind that it is but another effective tool to be used in appropriate situations. Even so, people will have opinions running the gamut from it being overhyped, to being the solution to every problem they will ever experience, to somewhere more moderate in between.

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Is Deep Learning a Real Big Thing! Or is it Overhyped Among Users - Analytics Insight

WASHINGTON, March 16, 2022 (GLOBE NEWSWIRE) -- The American government is beginning to focus on fusion in early 2022, recognizing the potential of this clean, extremely abundant energy source. With China currently in the lead with this science, keeping the American economic and strategic edge throughout the twenty-first century depends strongly on catching up to and surpassing this major rival's capabilities. The algorithms and machine learning solutions developed by Kronos Fusion Energy Defense Systems match up to the fusion energy goals to be laid out in the White House's March 17 summit. Kronos' technology offers an effective way of bringing the administration's plans swiftly and efficiently into reality.

Kronos' neural networks and sophisticated simulations operate on quantum computers. This combination enables analysis of data in multiple dimensions at immense speed, rather than following just a single thread of data at a time. The network can, therefore, learn from its mistakes, increasing its predictive accuracy on the fly. This enables turning wide-ranging research data into innovative design solutions meeting the White House and Department of Energy's objectives in a practical tokamak, or reactor, design.

Besides designing the next generation of tokamaks, Kronos says its algorithms can reduce, and eventually eliminate, the instability that has prevented the construction of successful large fusion reactors up to the current day. Its machine learning can predict plasma disruptions and instability, then engage safety measures, such as temporarily cooling the plasma, preventing damage to the tokamak's machinery. The Kronos simulation system can achieve almost 95% accuracy in disruption prediction within 30 milliseconds and may achieve 99% this year. These predictive levels greatly reduce the risk of the reactor damaging itself through runaway plasma processes.

Affordability is another advantage Kronos' simulations bring to any near-future U.S. tokamak reactor development program. In the midst of inflation and other significant economic upheavals generated by the Ukraine conflict, petroleum disruptions, and supply chain issues, among other causes, controlling costs is likely to be an important consideration. Reducing fusion development expense will help make a rapid timetable more viable, enabling bringing fusion energy's benefits to the U.S. faster.

Kronos' simulations are well suited not only to allow the USA to leap ahead of the current fusion baseline, but to build a superior tokamak at a lesser cost. The initial reactor is projected to be 17% to 20% cheaper than competing systems. The building and operation of this reactor will give Kronos' quantum computers a wealth of new data to input into the simulations, cutting costs by an extra 10% for subsequent tokamaks constructed after the first.

Given the Department of Energy's urgent call to develop America's fusion capabilities for the future, Kronos is in the right place at the right time to jumpstart the program with its algorithms and simulation systems. The company's Fusion Energy Commercialization Center will provide a central hub where powerful quantum computing at the heart of the project can be put to use.

For further information:

Kronos Fusion Energy1122 Colorado StAustin, TX 78701https://www.kronosfusionenergy.com/PR Contact - Erin Pendleton - pr@kronosfusionenergy.com

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How Fusion Energy Algorithms and Machine Learning Simulations Are Key to the White House's Goals for Commercializing Fusion Energy - GlobeNewswire

Huge amounts of data are needed to train machine-learning models to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, these data are not always easy to come by. Datasets may cost millions of dollars to generate, if usable data exist in the first place, and even the best datasets often contain biases that negatively impact a models performance.

To circumvent some of the problems presented by datasets, MIT researchers developed a method for training a machine learning model that, rather than using a dataset, uses a special type of machine-learning model to generate extremely realistic synthetic data that can train another model for downstream vision tasks.

Their results show that a contrastive representation learning model trained using only these synthetic data is able to learn visual representations that rival or even outperform those learned from real data.

This special machine-learning model, known as a generative model, requires far less memory to store or share than a dataset. Using synthetic data also has the potential to sidestep some concerns around privacy and usage rights that limit how some real data can be distributed. A generative model could also be edited to remove certain attributes, like race or gender, which could address some biases that exist in traditional datasets.

We knew that this method should eventually work; we just needed to wait for these generative models to get better and better. But we were especially pleased when we showed that this method sometimes does even better than the real thing, says Ali Jahanian, a research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper.

Jahanian wrote the paper with CSAIL grad students Xavier Puig and Yonglong Tian, and senior author Phillip Isola, an assistant professor in the Department of Electrical Engineering and Computer Science. The research will be presented at the International Conference on Learning Representations.

Generating synthetic data

Once a generative model has been trained on real data, it can generate synthetic data that are so realistic they are nearly indistinguishable from the real thing. The training process involves showing the generative model millions of images that contain objects in a particular class (like cars or cats), and then it learns what a car or cat looks like so it can generate similar objects.

Essentially by flipping a switch, researchers can use a pretrained generative model to output a steady stream of unique, realistic images that are based on those in the models training dataset, Jahanian says.

But generative models are even more useful because they learn how to transform the underlying data on which they are trained, he says. If the model is trained on images of cars, it can imagine how a car would look in different situations situations it did not see during training and then output images that show the car in unique poses, colors, or sizes.

Having multiple views of the same image is important for a technique called contrastive learning, where a machine-learning model is shown many unlabeled images to learn which pairs are similar or different.

The researchers connected a pretrained generative model to a contrastive learning model in a way that allowed the two models to work together automatically. The contrastive learner could tell the generative model to produce different views of an object, and then learn to identify that object from multiple angles, Jahanian explains.

This was like connecting two building blocks. Because the generative model can give us different views of the same thing, it can help the contrastive method to learn better representations, he says.

Even better than the real thing

The researchers compared their method to several other image classification models that were trained using real data and found that their method performed as well, and sometimes better, than the other models.

One advantage of using a generative model is that it can, in theory, create an infinite number of samples. So, the researchers also studied how the number of samples influenced the models performance. They found that, in some instances, generating larger numbers of unique samples led to additional improvements.

The cool thing about these generative models is that someone else trained them for you. You can find them in online repositories, so everyone can use them. And you dont need to intervene in the model to get good representations, Jahanian says.

But he cautions that there are some limitations to using generative models. In some cases, these models can reveal source data, which can pose privacy risks, and they could amplify biases in the datasets they are trained on if they arent properly audited.

He and his collaborators plan to address those limitations in future work. Another area they want to explore is using this technique to generate corner cases that could improve machine learning models. Corner cases often cant be learned from real data. For instance, if researchers are training a computer vision model for a self-driving car, real data wouldnt contain examples of a dog and his owner running down a highway, so the model would never learn what to do in this situation. Generating that corner case data synthetically could improve the performance of machine learning models in some high-stakes situations.

The researchers also want to continue improving generative models so they can compose images that are even more sophisticated, he says.

This research was supported, in part, by the MIT-IBM Watson AI Lab, the United States Air Force Research Laboratory, and the United States Air Force Artificial Intelligence Accelerator.

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When it comes to AI, can we ditch the datasets? - MIT News

The utilization of machine learning has skyrocketed over the past few years. The advanced technology has made high-performance computing accessible to almost all businesses out there. Businesses now use machine learning in cybersecurity, social networks, e-commerce websites, search engines, video streaming platforms and more. As organizations and users increasingly rely on machine learning-based applications, security experts have begun warning about adversaries abusing the technology.

Attackers can use data poisoning to severely affect machine learning systems. Machine learning systems are extremely vulnerable to data manipulation. Cybersecurity experts refer to malicious activities by attackers as adversarial machine learning. Adversarial machine learning can be a massive threat to business operations in an organization. Affected machine learning-based applications could produce inaccurate results, affecting business processes drastically. Business leaders need to be mindful of data poisoning on machine learning systems to create proactive strategies to prevent and mitigate such attacks.

Before creating effective strategies to protect machine learning systems, it is essential to understand what data poisoning is and how it can affect businesses. Data poisoning attacks contaminate a machine learning models training data. Such attacks severely impact the machine learning models ability to produce accurate predictions. To achieve this, attackers insert custom-made adversarial data into data sets used to train a machine learning model and the manipulated data is almost undetectable. The length of a data poisoning attack varies based on a models training cycle. In some cases, it may take weeks for a successful data poisoning attack.

Data poisoning attacks can be performed in a black box scenario as well as a white box scenario. In a black box scenario, an attacker uses classifiers in a machine learning model that depend on user feedback to learn. In a white box scenario, an attacker illegally gets access to the model and all the private data from some point in the supply chain, if the data is gathered from many sources.

Data poisoning attacks can allow attackers to get access to confidential information in the training data using corrupted data samples. Attackers can also disguise inputs to trick a machine learning model into evading accurate classification. Along with these, data poisoning attacks enable adversaries to reverse-engineer a machine learning model, assisting them in replicating and analyzing it locally to prepare for more advanced attacks.

Attackers are already targeting big players in the tech industry that use machine learning in cybersecurity with the help of data poisoning. A few years ago, Google had revealed that Gmails spam filter was compromised at least four times, where several spam emails were not marked as spam. Attackers sent millions of emails to throw off the classifier and alter how it defines a spam email. This technique allowed attackers to send several undetected malicious emails containing malware or other cybersecurity threats.

Another example of data poisoning includes Microsofts Twitter chat bot, Tay. Tay was programmed to learn and engage in casual conversation on Twitter. However, cyber criminals fed offensive tweets into Tays algorithm, turning the innocent chat bot offensive. As a result, Microsoft had to shut down Tay just 16 hours after launch.

Preventing and mitigating data poisoning can be extremely tricky. Contaminated data is almost impossible to detect and machine learning models are retrained with data sets at specific intervals depending on their use cases. Since data poisoning is a gradual process that happens over a certain number of training cycles, it is difficult to identify when the accuracy of a machine learning model has begun reducing.

Mitigating the damage done by data poisoning requires a time-consuming process that includes a historical analysis of all inputs for various classifiers to recognize all bad data samples and eliminate them. After this process, an organization would need to begin retraining the machine learning model from a version before the data poisoning attack. However, this entire procedure can be incredibly complicated and expensive when dealing with a large amount of data as well as a large number of data poisoning attacks. As a result, the affected machine learning model may never get fixed.

Considering the time-consuming and complicated process for detecting and mitigating data poisoning, businesses need to develop a proactive approach to protect machine learning models. Business leaders have to focus on vulnerabilities of machine learning in cybersecurity strategies for their organization. Business leaders can consult cybersecurity experts to design strategies that include machine learning in cybersecurity measures of their business.

Countering the Underrated Threat of Data Poisoning Facing Your Organization

Organizations can consider the following techniques to protect machine learning models from data poisoning:

Machine learning engineers and developers have to focus on steps to block attempts at attacking the model and detect polluted data inputs before the next training cycle begins. For this, developers can perform regression testing, input validity checking, manual moderation, anomaly detection and rate limiting. This approach is simpler and more effective compared to fixing compromised models.

Developers can restrict how many inputs can be provided by each unique user for the training data and they can also define the value of each input. A small group of users should not account for the majority of machine learning model training data. Along with these, developers can compare newly trained classifiers to the older ones by rolling them out to a small set of users only.

Attackers need access to a lot of confidential information to execute a successful data poisoning attack. Therefore, organizations should be careful about sharing sensitive data and have strong access control measures in place for the machine learning model as well as data. To do this effectively, business leaders need to design methods to safeguard models of machine learning in cybersecurity strategy that is used across the organization. The protection of machine learning models and data is tied to how an organization generally handles cybersecurity. Businesses can also restrict permissions of several users, enable multi-factor logins, and utilize data and file versioning to keep data sets safer.

Organizations regularly perform penetration tests against their systems and networks to identify vulnerabilities as part of their cybersecurity strategy. They can conduct similar tests on machine learning models to integrate machine learning into cybersecurity measures. Developers need to attack their own machine learning models to understand their vulnerabilities. Based on the insights gained from this technique, they can build defensive strategies to protect training data sets. Such attacks would also help developers identify what poisoned data points look like, allowing them to design mechanisms to discard contaminated data points.

In a recent talk at the USENIX Enigma conference, Hyrum Anderson, Microsofts principal architect of Trustworthy Machine Learning, presented a red team exercise where his team reverse-engineered a machine learning model that was used by a resource provisioning service. Although the team didnt have direct access to the model, they found enough information about how the machine learning model gathered necessary data, and they developed a local model replica to test attacks without being detected by the actual system. This entire process allowed the team to understand how they could attack the live system. After gathering all the essential information, the team managed to execute a successful attack that compromised the live system.

Businesses can perform similar processes to identify weaknesses in their machine learning systems and develop effective security measures. Regularly testing machine learning models will help organizations protect their models against several existing cyber attacks as well as new attacks created by adversaries.

Developers and engineers can occasionally alter machine learning algorithms that use classifiers. These changing algorithms as well as models can be kept secret, and they would be harder to recognize and attack. This is considered as a moving target strategy against attackers, which can help in protecting machine learning models. To effectively execute this strategy, businesses may need to hire more developers and cybersecurity experts to alter machine learning models and test them for vulnerabilities.

Adversarial machine learning may not seem like an immediate threat right now. But as machine learning gets adopted in various industries, it could be a force to reckon with. Data poisoning can prove to be extremely threatening in machine learning-based self-driving cars where human lives can be at risk. Hence, it is essential to start integrating machine learning into cybersecurity workflow to ensure the safety of data sets used in machine learning systems. Currently, there arent any sophisticated tools to protect machine learning models against data poisoning, since cybersecurity experts have started pointing out such threats in recent years. For now, businesses have to rely on creating holistic cybersecurity strategies that focus on the safety of machine learning models. Cybersecurity experts will soon launch far more sophisticated tools that can be deployed to protect machine learning models and data sets.

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Countering The Underrated Threat Of Data Poisoning Facing Your Organization - Forbes

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Jeff Pummill and Han Hu

Two University of Arkansas researchers have been awarded access to the supercomputer Neocortex, a platform created to accelerate scientific discovery by running artificial intelligence and machine learning models more quickly. This gives scientists and engineers a practical way to test theories in days or weeks rather than months or years.

Funded by a $5 million National Science Foundation grant and located at the Pittsburgh Supercomputing Center, Neocortex facilitates researchers' abilities to handle massive data sets by shortening computer "training," the most time-consuming step in artificial intelligence data analysis. This training teaches a program to recognize specific characteristics in data and leverage them for well-defined tasks; Neocortex enables this step being done exponentially more quickly.

"We are very excited about getting access to Neocortex at the Pittsburgh Supercomputing Center," said Han Hu, assistant professor of mechanical engineering. "This powerful, groundbreaking AI system will accelerate our research on data-driven modeling of thermal transport processes."

Hu's research into thermal transport processes is critical to the development of high-performance cooling technologies for microelectronics, hybrid vehicles, data centers and other applications. Neocortex will help Hu develop and evolve his theories and research.

Jeff Pummill, co-director of the Arkansas High Performance Computing Center, said a key component of the center's work is identifying and evaluating new computing systems such as Neocortex that may provide significant research advantages to computational scientists.

"We are seeing increased interest among researchers across campus to use machine learning and neural networks, and it's critical to be aware of new technologies that can potentially increase our competitiveness. Dr. Hu's project is an ideal opportunity to benchmark capabilities between the current systems and new custom supercomputers designed specifically for certain types of problems," Pummill said.

Supporting the research in cooling technologies for microelectronics advanced by Hu and Pummill with Neocortex is highly exciting, said Paola S. Buitrago, Neocortex principal investigator and project director, and director of AI and big data at the Pittsburgh Supercomputing Center.

"We look forward to continue democratizing access to game-changing hardware that can and will enable the next breakthroughs powered by deep learning and artificial intelligence," Buitrago said.

Pummill said he and Hu will have access to Neocortex for a year, although extensions are often granted to researchers who are making significant progress. Access to Neocortex was granted as part of a competitive proposal process, though there is no monetary cost for use as the system was funded by the NSF.

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Supercomputer Access Will Accelerate Research Progress on Cooling Technologies for Microelectronics - University of Arkansas Newswire