Microsoft and OpenAI say Iran, North Korea, Russia, and China have started arming their US cyberattack efforts with generative artificial intelligence (GenAI).

The companies said in a blog post on Microsofts website Wednesday that they jointly detected and stopped attacks using their AI technologies. The companies listed several examples of specific attacks using large language models to enhance malicious social engineering efforts -- leading to better deepfakes and voice cloning attempting to crack US systems.

Micosoft said North Koreas Kimsuky cyber group, Irans Revolutionary Guard, Russias military, and a Chinese cyberespionage called Aquatic Panda, all used the companies large language model tools for potential attacks and malicious activity. The attack from Iran included phishing emails pretending to come from an international development agency and another attempting to lure prominent feminists to an attacker-built website on feminism.

Cyberattacks from foreign adversaries have been steadily increasing in severity and complexity. This month, the Cybersecurity and Infrastructure Agency (CISA) said China-backed threat actor Volt Typhoon targeted several western nations critical infrastructure and have had access to the systems for at least five years. Experts fear such attacks will only increase in severity as nation-states use GenAI to enhance their efforts.

Related:Firms Arm US Against AI Cyberattacks

Nazar Tymoshyk, CEO at cybersecurity firm UnderDefense, tells InformationWeek in a phone interview that even as threats become more sophisticated through GenAI, the fundamentals for cybersecurity should stay the same. The onus for safeguarding, he said, is on the company producing AI. Every product is AI-enabled, so its now a feature in every program, he says. It becomes impossible to distinguish between whats an AI attack. So, its the company who is responsible to put additional controls in place.

Microsoft called the attack attempts early stage, and our research with OpenAI has not identified significant attacks employing the LLMs we monitor closely. At the same time we feel this is important research to expose early stage, incremental moves that we observe well-known threat actors attempting, and share information on how we are blocking and countering them with the defender community.

The companies say hygiene practices like multifactor authentication and zero-trust defenses are still vital weapons against attacks -- AI-enhanced or not. While attackers will remain interested in AI and probe technologies current capabilities and security controls, its important to keep these risks in context.

Related:What CISOs Need to Know About Nation-State Actors

In a separate blog post, OpenAI says it will continue to work with Microsoft to identify potential threats using GenAI models.

Although we work to minimize potential misuse by such actors, we will not be able to stop every instance. But by continuing to innovate and investigate, collaborate, and share, we make it harder for malicious actors to remain undetected across the digital ecosystem and improve the experience for everyone else.

OpenAI declined to make an executive available for comment.

While Microsoft and OpenAIs report was focused on how threat actors are using AI tools for attacks, AI can also be a vector for attack. Thats an important thing to remember with businesses implementing GenAI tools at a feverish pace, Chris Tito Sestito, CEO and co-founder of adversarial AI firm HiddenLayer tells InformationWeek in an email.

Artificial intelligence is, by a wide margin, the most vulnerable technology ever to be deployed in production systems, Sestito says. Its vulnerable at a code level, during training and development, post-deployment, over networks, via generative outputs and more. With AI being rapidly implemented across sctors, there has also been a substantial rise in intentionally harmful attacks providing why defensive solutions to secure AI are needed.

Related:Microsoft IDs Russia-Backed Actor Behind Leadership Email Hacks

He adds, Security has to maintain pace with AI to accelerate innovation. Thats why its imperative to safeguard your most valuable assets from development to implementation companies must regularly update and refine their AI-specific security program to address new challenges and vulnerabilities.

See the rest here:
Microsoft, OpenAI: US Adversaries Armed with GenAI - InformationWeek

The stock of operational robots around the globe hit a new record of about 3.9 million units. This demand is driven by a number of exciting technological innovations. The International Federation of Robotics reports about the top 5 automation trends in 2024:

The trend of using Artificial Intelligence in robotics and automation keeps growing. The emergence of generative AI opens-up new solutions. This subset of AI is specialized to create something new from things its learned via training, and has been popularized by tools such as ChatGPT. Robot manufacturers are developing generative AI-driven interfaces which allow users to program robots more intuitively by using natural language instead of code. Workers will no longer need specialized programming skills to select and adjust the robots actions.

Another example is predictive AI analyzing robot performance data to identify the future state of equipment. Predictive maintenance can save manufacturers machine downtime costs. In the automotive parts industry, each hour of unplanned downtime is estimated to cost US$1.3m - the Information Technology & Innovation Foundation reports. This indicates the massive cost-saving potential of predictive maintenance. Machine learning algorithms can also analyze data from multiple robots performing the same process for optimization. In general, the more data a machine learning algorithm is given, the better it performs.

Human-robot collaboration continues to be a major trend in robotics. Rapid advances in sensors, vision technologies and smart grippers allow robots to respond in real-time to changes in their environment and thus work safely alongside human workers.

Collaborative robot applications offer a new tool for human workers, relieving and supporting them. They can assist with tasks that require heavy lifting, repetitive motions, or work in dangerous environments.

The range of collaborative applications offered by robot manufacturers continues to expand.

A recent market development is the increase of cobot welding applications, driven by a shortage of skilled welders. This demand shows that automation is not causing a labor shortage but rather offers a means to solve it. Collaborative robots will therefore complement not replace investments in traditional industrial robots which operate at much faster speeds and will therefore remain important for improving productivity in response to tight product margins.

New competitors are also entering the market with a specific focus on collaborative robots. Mobile manipulators, the combination of collaborative robot arms and mobile robots (AMRs), offer new use cases that could expand the demand for collaborative robots substantially.

Mobile manipulators so called MoMas - are automating material handling tasks in industries such as automotive, logistics or aerospace. They combine the mobility of robotic platforms with the dexterity of manipulator arms. This enables them to navigate complex environments and manipulate objects, which is crucial for applications in manufacturing. Equipped with sensors and cameras, these robots perform inspections and carry out maintenance tasks on machinery and equipment. One of the significant advantages of mobile manipulators is their ability to collaborate and support human workers. Shortage of skilled labor and a lack of staff applying for factory jobs is likely to increase demand.

Digital twin technology is increasingly used as a tool to optimize the performance of a physical system by creating a virtual replica. Since robots are more and more digitally integrated in factories, digital twins can use their real-world operational data to run simulations and predict likely outcomes. Because the twin exists purely as a computer model, it can be stress-tested and modified with no safety implications while saving costs. All experimentation can be checked before the physical world itself is touched. Digital twins bridge the gap between digital and physical worlds.

Robotics is witnessing significant advancements in humanoids, designed to perform a wide range of tasks in various environments. The human-like design with two arms and two legs allows the robot to be used flexibly in work environments that were actually created for humans. It can therefore be easily integrated e.g. into existing warehouse processes and infrastructure.

The Chinese Ministry of Industry and Information Technology (MIIT) recently published detailed goals for the countrys ambitions to mass-produce humanoids by 2025. The MIIT predicts humanoids are likely to become another disruptive technology, similar to computers or smartphones, that could transform the way we produce goods and the way humans live.

The potential impact of humanoids on various sectors makes them an exciting area of development, but their mass market adoption remains a complex challenge. Costs are a key factor and success will depend on their return on investment competing with well-established robot solutions like mobile manipulators, for example.

The five mutually reinforcing automation trends in 2024 show that robotics is a multidisciplinary field where technologies are converging to create intelligent solutions for a wide range of tasks, says Marina Bill, President of the International Federation of Robotics. These advances continue to shape the merging industrial and service robotics sectors and the future of work.

Read more:
Top 5 Robot Trends 2024 International Federation of Robotics reports | RoboticsTomorrow - Robotics Tomorrow

Predicting ETAs

Ubers main goal in predicting ETA was to be reliable. This means that the estimated time of arrival should be very close to the actual time, and this accuracy should be consistent across different places and times.

The simplest approach that comes to mind to find the predicted ETA is to use map data, such as the haversine distance (shortest distance between two points), and add a scaler for speed. However, this method is not sufficient, as there can be a significant gap between the predicted and the actual ETA calculation since people dont travel in a straight line between two points.

To address this issue, Uber has incorporated additional layers such as routing, traffic information, map matching, and machine learning algorithms to enhance the reliability of the predicted ETA.

Lets deep dive into the additional layers.

Problem statement : Build a large-scale system that computes the route from origin to destination with the least cost and low latency.

To achieve this they represent the physical map as a graph

Every road intersection represents a node, and each road segment is represented as a directed edge.

To determine the ETA, they need to find the shortest path in this directed weighted graph. Dijkstras algorithm is commonly used for this purpose, but its time complexity is O(n log n), where n is the number of road intersections or nodes in the graph.

Considering the vast scale of Ubers operations, such as the half a million road intersections in the San Francisco Bay Area alone, Dijkstras algorithm becomes impractical.

To address this issue, Uber partitions the graph and precomputes the best path within each partition.

Interacting with the boundaries of graph partitions alone is sufficient to discover the optimal path.

Picture a dense graph represented on a circular map.

To find the best path between two points in a circle, traditionally, every single node in the circle needs to be traversed, resulting in a time complexity proportional to the area of the circle ( * r).

However, by partitioning and precomputing, efficiency is improved. It becomes possible to find the best path by interacting only with the nodes on the circles boundary, reducing the time complexity to the perimeter of the circle (2 * * r).

In simpler terms, this means that the time complexity for finding the best path in the San Francisco Bay Area has been reduced from 500,000 to 700.

Once we have the route, we need to determine the travel time. To do that, we require traffic information.

Consider traffic conditions when determining the fastest route between two points.

Traffic depends on factors like time of day, weather, and the number of vehicles on the road.

They used traffic information to determine the edge weights of the graph, resulting in a more accurate ETA.

They integrated historical speed data with real-time speed information to enhance the accuracy of traffic updates, as the inclusion of additional traversal data contributes to more precise traffic information.

Before moving forward, there were two critical questions that needed addressing:

1. Validity of Real-time Speed: Too short a duration might imply a lack of understanding of the current road conditions. Conversely, if its too long, the data becomes outdated.

2. Integrating Historical and Real-time Speeds: Striking a balance here involves a tradeoff between bias and variance. Prioritizing real-time data yields less bias but more variance. Emphasizing historical data introduces more bias but reduces variance. The challenge lies in finding the optimal balance between the two

GPS signals can be less reliable and less frequent, especially when a vehicle enters a tunnel or an area with many tall buildings that can reflect the GPS signals.

Also, mobile GPS signals are usually close to the street segments but not perfectly on it, which makes it difficult to get the exact street coordinates.

Map matching is like connecting the dots! Imagine you have red dots representing raw GPS signals.

Now, the goal is to figure out which road segments these dots belong to. Thats where map matching comes in it links those red dots to specific road segments.

The resulting blue dots show exactly where those GPS signals align with the road segments. Its like fitting the puzzle pieces together to see the actual path on the map.

They use the Kalman filter for map matching. It takes GPS signals and matches them to road segments.

Besides they use the Viterbi algorithm to find the most probable road segments. Its a dynamic programming approach.

Ubers initial aim was to provide reliable ETA information universally. Reliability has been discussed above; now, lets shift the focus to how Uber ensures availability everywhere.

Uber has observed that ETA predictions in India are less accurate compared to North America due to systematic biases or inefficiencies. This is where Machine Learning (ML) can play a crucial role by capturing variations in: 1. Regions 2. Time 3. Trip types 4. Driver behavior etc

By leveraging ML, Uber aims to narrow the gap between predicted ETAs and actual arrival times, thereby enhancing the overall reliability and user experience.

Lets define a few terms, and then we will better understand their decisions.

1. Linear Model: Definition: A linear model assumes a linear relationship between the input variables (features) and the output variable. It follows the equation (y = mx + b), where (y) is the output, (x) is the input, (m) is the slope, and (b) is the intercept. Example: Linear regression is a common linear model used for predicting a continuous outcome.

2. Non-linear Model: Definition: A non-linear model does not assume a linear relationship between the input and output variables. It may involve higher-order terms or complex mathematical functions to capture the patterns in the data. Example: Decision trees, neural networks, and support vector machines with non-linear kernels are examples of non-linear models.

3. Parametric Model: Definition: A parametric model makes assumptions about the functional form of the relationship between variables and has a fixed number of parameters. Once the model is trained, these parameters are fixed. Example: Linear regression is parametric since it assumes a linear relationship with fixed coefficients.

4. Non-parametric Model: Definition: A non-parametric model makes fewer assumptions about the functional form and the number of parameters in the model. It can adapt to the complexity of the data during training. Example: k-Nearest Neighbors (KNN) is a non-parametric algorithm, as it doesnt assume a specific functional form and adapts to the data during prediction based on the local neighbourhood of points.

Since ETA is influenced by factors like location and time of day, and there is no predefined relationship between variables, they opted for non-linear and non-parametric machine learning models like

In their terms, With great (modelling) power comes great (reliability) responsibility! So, they have fallback ETAs to avoid system downtime situations.

They also monitor ETA to prevent issues for both internal and external consumers.

Link:
Cracking the Code: How Uber Masters ETA Calculation on a Massive Scale - Medium

Behrooz Tahmasebi an MIT PhD student in the Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) was taking a mathematics course on differential equations in late 2021 when a glimmer of inspiration struck. In that class, he learned for the first time about Weyls law, which had been formulated 110 years earlier by the German mathematician Hermann Weyl. Tahmasebi realized it might have some relevance to the computer science problem he was then wrestling with, even though the connection appeared on the surface to be thin, at best. Weyls law, he says, provides a formula that measures the complexity of the spectral information, or data, contained within the fundamental frequencies of a drum head or guitar string.

Tahmasebi was, at the same time, thinking about measuring the complexity of the input data to a neural network, wondering whether that complexity could be reduced by taking into account some of the symmetries inherent to the dataset. Such a reduction, in turn, could facilitate as well as speed up machine learning processes.

Weyls law, conceived about a century before the boom in machine learning, had traditionally been applied to very different physical situations such as those concerning the vibrations of a string or the spectrum of electromagnetic (black-body) radiation given off by a heated object. Nevertheless, Tahmasebi believed that a customized version of that law might help with the machine learning problem he was pursuing. And if the approach panned out, the payoff could be considerable.

He spoke with his advisor, Stefanie Jegelka an associate professor in EECS and affiliate of CSAIL and the MIT Institute for Data, Systems, and Society who believed the idea was definitely worth looking into. As Tahmasebi saw it, Weyls law had to do with gauging the complexity of data, and so did this project. But Weyls law, in its original form, said nothing about symmetry.

He and Jegelka have now succeeded in modifying Weyls law so that symmetry can be factored into the assessment of a datasets complexity. To the best of my knowledge, Tahmasebi says, this is the first time Weyls law has been used to determine how machine learning can be enhanced by symmetry.

The paper he and Jegelka wrote earned a Spotlight designation when it was presented at the December 2023 conference on Neural Information Processing Systems widely regarded as the worlds top conference on machine learning.

This work, comments Soledad Villar, an applied mathematician at Johns Hopkins University, shows that models that satisfy the symmetries of the problem are not only correct but also can produce predictions with smaller errors, using a small amount of training points. [This] is especially important in scientific domains, like computational chemistry, where training data can be scarce.

In their paper, Tahmasebi and Jegelka explored the ways in which symmetries, or so-called invariances, could benefit machine learning. Suppose, for example, the goal of a particular computer run is to pick out every image that contains the numeral 3. That task can be a lot easier, and go a lot quicker, if the algorithm can identify the 3 regardless of where it is placed in the box whether its exactly in the center or off to the side and whether it is pointed right-side up, upside down, or oriented at a random angle. An algorithm equipped with the latter capability can take advantage of the symmetries of translation and rotations, meaning that a 3, or any other object, is not changed in itself by altering its position or by rotating it around an arbitrary axis. It is said to be invariant to those shifts. The same logic can be applied to algorithms charged with identifying dogs or cats. A dog is a dog is a dog, one might say, irrespective of how it is embedded within an image.

The point of the entire exercise, the authors explain, is to exploit a datasets intrinsic symmetries in order to reduce the complexity of machine learning tasks. That, in turn, can lead to a reduction in the amount of data needed for learning. Concretely, the new work answers the question: How many fewer data are needed to train a machine learning model if the data contain symmetries?

There are two ways of achieving a gain, or benefit, by capitalizing on the symmetries present. The first has to do with the size of the sample to be looked at. Lets imagine that you are charged, for instance, with analyzing an image that has mirror symmetry the right side being an exact replica, or mirror image, of the left. In that case, you dont have to look at every pixel; you can get all the information you need from half of the image a factor of two improvement. If, on the other hand, the image can be partitioned into 10 identical parts, you can get a factor of 10 improvement. This kind of boosting effect is linear.

To take another example, imagine you are sifting through a dataset, trying to find sequences of blocks that have seven different colors black, blue, green, purple, red, white, and yellow. Your job becomes much easier if you dont care about the order in which the blocks are arranged. If the order mattered, there would be 5,040 different combinations to look for. But if all you care about are sequences of blocks in which all seven colors appear, then you have reduced the number of things or sequences you are searching for from 5,040 to just one.

Tahmasebi and Jegelka discovered that it is possible to achieve a different kind of gain one that is exponential that can be reaped for symmetries that operate over many dimensions. This advantage is related to the notion that the complexity of a learning task grows exponentially with the dimensionality of the data space. Making use of a multidimensional symmetry can therefore yield a disproportionately large return. This is a new contribution that is basically telling us that symmetries of higher dimension are more important because they can give us an exponential gain, Tahmasebi says.

The NeurIPS 2023 paper that he wrote with Jegelka contains two theorems that were proved mathematically. The first theorem shows that an improvement in sample complexity is achievable with the general algorithm we provide, Tahmasebi says. The second theorem complements the first, he added, showing that this is the best possible gain you can get; nothing else is achievable.

He and Jegelka have provided a formula that predicts the gain one can obtain from a particular symmetry in a given application. A virtue of this formula is its generality, Tahmasebi notes. It works for any symmetry and any input space. It works not only for symmetries that are known today, but it could also be applied in the future to symmetries that are yet to be discovered. The latter prospect is not too farfetched to consider, given that the search for new symmetries has long been a major thrust in physics. That suggests that, as more symmetries are found, the methodology introduced by Tahmasebi and Jegelka should only get better over time.

According to Haggai Maron, a computer scientist at Technion (the Israel Institute of Technology) and NVIDIA who was not involved in the work, the approach presented in the paper diverges substantially from related previous works, adopting a geometric perspective and employing tools from differential geometry. This theoretical contribution lends mathematical support to the emerging subfield of Geometric Deep Learning, which has applications in graph learning, 3D data, and more. The paper helps establish a theoretical basis to guide further developments in this rapidly expanding research area.

See the original post here:
How symmetry can come to the aid of machine learning - MIT News

Our economys financial sector is using machine learning (ML) more often to support lending decisions that affect our daily lives. While technologies such as these pose new risks, they also have the potential to make lending fairer. Current regulation limits lenders use of ML and aims to reduce discrimination by preventing the use of variables correlated with protected class membership, such as race, age, or neighborhood, in any aspect of the lending decision. This research explores an alternative approach that would use an applicants neighborhood to consciously reduce fairness concerns between LMI and non-LMI applicants. Since this approach is costly to lenders and borrowers, we propose concurrent use with more advanced ML models that soften some of these costs by improving model predictions of default. The combination of embracing ML and setting explicit fairness goals may help address current disparities in credit access and ensure that the gains from innovations in ML are more widely shared. To successfully achieve these goals, a broad conversation should continue with stakeholders such as lenders, regulators, researchers, policymakers, technologists, and consumers.

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Advancing Fairness in Lending Through Machine Learning - Federal Reserve Bank of Philadelphia