Author Archives: Danzig

Artificial Intelligence and Machine Learning in Packaging Robotics … – Fagen wasanni

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At the forefront of the fourth industrial revolution, Artificial Intelligence (AI) and Machine Learning (ML) have become significant trends in packaging robotics and automation. AI refers to software that is trained to perform specific tasks, while ML uses algorithms to learn insights and patterns from data. According to a report from PMMI, AI-based applications in the packaging industry are expected to grow at a CAGR of over 50% in the next five years.

One company making strides in this field is Deep Learning Robotics (DLRob). DLRob has developed groundbreaking robot control software that allows users to teach robots tasks by demonstrating them. Through advanced ML algorithms, robots can learn by observing and mimicking human actions. DLRobs software is user-friendly and adaptable to various robots and applications.

In April, DLRob announced a software update that enables its customers to connect and control a wider range of robotics devices, including Universal Robots UR series of cobots. This expansion increases the capabilities and versatility of DLRobs software.

In another collaboration, Intrinsic, an Alphabet company, and Siemens have partnered to explore integrations and interfaces between Intrinsics AI-based robotics software and Siemens Digital Industries portfolio for industrial production automation. The goal is to bridge the gaps between robotics, automation engineering, and IT development, making industrial robotics more accessible and usable for businesses, entrepreneurs, and developers. This collaboration aims to bring joint solutions to the market that can benefit more enterprises.

Both Intrinsic and Siemens emphasize the importance of combining robotics with the production environment to maximize value. By accelerating the development process and facilitating seamless operation, they aim to democratize access to robotics and automation technology.

This collaboration highlights the growing significance of AI and ML in the packaging industry, paving the way for innovative advancements in robotics and automation for various sectors.

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Artificial Intelligence and Machine Learning in Packaging Robotics ... - Fagen wasanni

How machine learning can expand the Landscape of Edge AI. | TDK – TDK Corporation

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Edge AI and the evolution of edge devices

In the context of edge computing, an edge device simply refers to a device that operates at the edges of networks, collecting, processing, and analyzing data. Examples include smartphones, security cameras, smart speakers, and a variety of other devices. In recent years, with the rise of edge AI, these devices have evolved even smarter due to the machine learning functions.

Edge AI*2 is a collective term for technologies related to on-device collection, processing, and analysis of data for artificial intelligence purposes. Commonly, implementing AI requires vast amounts of data and computing power, which is why they are typically run on cloud-based servers. With edge AI, however, data is processed internally on the devices, reducing delays and costs related to data transmission, as well as improving privacy.

Cloud Computing and Edge Computing Compared

The coupling of edge devices with edge AI is broadening the realm of IoT (Internet of Things). Self-driving vehicles, factory automation, and medical device management are examples of edge devices already playing vital roles where real-time data processing and decision-making are required.

Edge AI has traditionally been implemented on devices with robust processing power, such as smartphones and tablets. With the proliferation of IoT, however, interest is growing in a technology known as TinyML (Tiny Machine Learning)*3, which enables small devices with only modest capabilities to execute machine learning functions onboard.

Generally, machine learning is performed on high-performance computers or cloud servers, requiring large amounts of memory and fast processors, incurring commensurate electrical power consumption. This permits the execution of large-scale machine learning models based on vast datasets, resulting in highly accurate image recognition, natural language processing, and more. However, every step of the workflowincluding data collection, model development, and validationusually requires handling by seasoned engineers specialized in each area.

TinyML is a machine learning technology designed for small devices, enabling edge AI to be implemented even on microcontrollers (MCUs), which only possess limited processing muscle. This, in turn, is expected to engender smaller IoT devices with low power consumption. It is now possible to run machine learning inference on almost any device with a sensor and marginal computing power, endowing it with intelligence.

Qeexo, a Silicon Valley startup that joined the TDK Group in 2023, specializes in machine learning solutions for edge devices, with a particular focus on TinyML. Qeexo AutoML, is an end-to-end, no-code (i.e., not requiring code to be hand-written in a programming language) platform that empowers non-engineers to implement machine learning on lightweight edge devices. Working in an intuitive, web-based interface, users can easily perform all the steps necessary to build a machine learning systembeginning with collecting and pre-processing raw data, followed by training and refining recognition models, then finally creating and installing the finished package onto edge devices where the machine learning-based intelligence comes to life.

TDK is currently developing i3 Micro Module, an ultracompact sensor module with onboard edge AI designed to be used for predictive maintenancethe practice of foreseeing and preempting anomalies in machinery and equipment at factories and similar facilities. Sensors, including those for vibration, temperature, and barometric pressure, as well as edge AI and mesh networking capabilities, are all integrated into a compact package, allowing equipment conditions to be monitored without having to rely on manpower, thereby helping minimize downtime and improve productivity. (Photo: Ultracompact sensor module i3 Micro Module)

Related Stories Predicting Anomalies Before Breakdowns Occur: Ultracompact Sensor Module Redefines the Status Quo of Equipment Maintenance

Michael A. Gamble, Director, Product Management for Qeexo, explained the significance of Qeexo AutoML. Conventionally, machine learning for embedded devices is a lengthy, complex process requiring highly specialized engineering skills. Qeexo AutoML enables almost anyoneincluding those not technically inclinedto accomplish the same, using an end-to-end, streamlined web interface. Similar to the way digital design tools and audio workstation software opened up graphic arts and music production to just about anyone with a creative spark, AutoML levels the playing field for machine learning. Put simply, we think of Qeexo AutoML as democratizing machine learning.

Advances in edge device technologies have spurred the development of numerous IoT devices and microcontrollers featuring sophisticated machine learning capabilities. With the advent of tools like Qeexo AutoML, it is now possible to create complex machine learning models that run on edge devices in short order.

Letting edge AI process data collected from sensors in edge devices substantially expands the range of possible solutions. Gamble continued, Pairing Qeexos machine learning solutions with TDKs sensor devices will allow us to provide customers with integrated, one-stop solutions. We look forward to a synergistic partnership in developing and delivering smart edge solutions that leverage each others strengths.

Today, edge devices are evolving into intelligent systems that learn by themselves, going well beyond merely gathering and transmitting data. Advanced manufacturing facilities, sometimes referred to as smart factories, will begin equipping almost every piece of machinery and equipment with edge devices. Edge devices are also becoming prevalent among consumers in the form of mobility products and smartphones. Propelled by tools like AutoML, TinyML and edge AI are expected to become increasingly familiar and commonplace. This will all have a significant positive impact on our daily lives, businesses, and industry as a whole.

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How machine learning can expand the Landscape of Edge AI. | TDK - TDK Corporation

Use cases of Stereo Matching part9(Machine Learning + AI) – Medium

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Author : : Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Tom Drummond, Zhiyong Wang, Zongyuan Ge

Abstract : The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use of the transformer for the problem of stereo matching remains relatively unexplored. In this paper, we comprehensively investigate the use of the transformer for the problem of stereo matching, especially for laparoscopic videos, and propose a new hybrid deep stereo matching framework (HybridStereoNet) that combines the best of the CNN and the transformer in a unified design. To be specific, we investigate several ways to introduce transformers to volumetric stereo matching pipelines by analyzing the loss landscape of the designs and in-domain/cross-domain accuracy. Our analysis suggests that employing transformers for feature representation learning, while using CNNs for cost aggregation will lead to faster convergence, higher accuracy and better generalization than other options. Our extensive experiments on Sceneflow, SCARED2019 and dVPN datasets demonstrate the superior performance of our HybridStereoNet.

2. EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching(arXiv)

Author : Qiang Wang, Shaohuai Shi, Kaiyong Zhao, Xiaowen Chu

Abstract : Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction tasks, such as image classification and object detection. However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capabilities. To this end, we propose to train an elastic and accurate network for stereo matching (EASNet) that supports various 3D architectural settings on devices with different computing capabilities. Given the deployment latency constraint on the target device, we can quickly extract a sub-network from the full EASNet without additional training while the accuracy of the sub-network can still be maintained. Extensive experiments show that our EASNet outperforms both state-of-the-art human-designed and NAS-based architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy and inference speed. Particularly, deployed on an inference GPU, EASNet achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is 4.5 faster than LEAStereo with a better quality model

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Use cases of Stereo Matching part9(Machine Learning + AI) - Medium

Harnessing the Power of AI and Machine Learning for Enhanced … – Fagen wasanni

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Harnessing the Power of AI and Machine Learning for Enhanced Security Screening and Detection: A Comprehensive Guide

In the rapidly evolving world of technology, artificial intelligence (AI) and machine learning are increasingly being harnessed to enhance security screening and detection. These advanced technologies are revolutionizing the way security checks are conducted, offering unprecedented levels of accuracy and efficiency.

AI and machine learning are subsets of computer science that mimic human intelligence. They are capable of learning from experience, adjusting to new inputs, and performing tasks that normally require human intelligence. In the context of security screening and detection, these technologies can be trained to identify potential threats or anomalies with a high degree of precision.

One of the key areas where AI and machine learning are making a significant impact is in airport security. Traditional methods of security screening at airports, which rely heavily on human intervention, are often time-consuming and prone to errors. However, with the advent of AI and machine learning, the process has become more streamlined and effective. These technologies can analyze vast amounts of data in real-time, identify patterns, and flag potential security threats. This not only enhances the accuracy of security checks but also significantly reduces the time taken for screening.

Moreover, AI and machine learning are also being used to improve cybersecurity. With cyber threats becoming increasingly sophisticated, traditional methods of detection and prevention are often inadequate. AI and machine learning algorithms can analyze network traffic, detect unusual patterns, and identify potential cyber threats. They can also predict future attacks based on historical data, enabling organizations to take proactive measures to safeguard their systems.

In addition to airports and cybersecurity, AI and machine learning are also being utilized in other areas of security screening and detection. For instance, they are being used in facial recognition systems, biometric scanners, and surveillance cameras to enhance security in public places and prevent criminal activities. These technologies can accurately identify individuals, detect suspicious activities, and alert authorities in real-time, thereby enhancing public safety.

However, while the benefits of AI and machine learning in security screening and detection are immense, there are also challenges that need to be addressed. One of the key challenges is the risk of false positives, where innocent individuals or activities are flagged as potential threats. This can lead to unnecessary investigations and potential infringements on privacy. Therefore, it is crucial to ensure that these technologies are used responsibly and ethically.

Another challenge is the need for continuous learning and adaptation. AI and machine learning algorithms are only as good as the data they are trained on. Therefore, it is essential to continuously update these algorithms with new data to ensure their accuracy and effectiveness.

In conclusion, AI and machine learning hold great promise for enhancing security screening and detection. They offer the potential to significantly improve the accuracy and efficiency of security checks, detect potential threats in real-time, and predict future attacks. However, it is also important to address the challenges associated with their use to ensure that they are used responsibly and effectively. As these technologies continue to evolve, they are set to play an increasingly important role in ensuring our safety and security.

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Use cases of Stereo Matching part8(Machine Learning + AI) – Medium

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Author : Andrea Pilzer, Yuxin Hou, Niki Loppi, Arno Solin, Juho Kannala

Abstract : We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of feature points characterizes a scene. To improve stereo matching, we propose to elevate 2D hints to 3D points. These sparse and unevenly distributed 3D visual hints are expanded using a 3D random geometric graph, which enhances the learning and inference process. We evaluate our proposal on multiple widely adopted benchmarks and show improved performance without access to additional sensors other than the image sequence. To highlight practical applicability and symbiosis with visual odometry, we demonstrate how our methods run on embedded hardware.

2.Comparison of Stereo Matching Algorithms for the Development of Disparity Map (arXiv)

Author : Hamid Fsian, Vahid Mohammadi, Pierre Gouton, Saeid Minaei

Abstract : Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and compared. The stereo images used in this study were from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. Results show that the selection of matching function is quite important and also depends on the images properties. Results showed that the BP algorithm in most cases provided better results getting accuracies over 95%

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Use cases of Stereo Matching part8(Machine Learning + AI) - Medium

Use cases of Stereo Matching part7(Machine Learning + AI) – Medium

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Author : Philippe Weinzaepfel, Thomas Lucas, Vincent Leroy, Yohann Cabon, Vaibhav Arora, Romain Brgier, Gabriela Csurka, Leonid Antsfeld, Boris Chidlovskii, Jrme Revaud

Abstract : Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow. The application of selfsupervised concepts, such as instance discrimination or masked image modeling, to geometric tasks is an active area of research. In this work, we build on the recent crossview completion framework, a variation of masked image modeling that leverages a second view from the same scene which makes it well suited for binocular downstream tasks. The applicability of this concept has so far been limited in at least two ways: (a) by the difficulty of collecting realworld image pairs in practice only synthetic data have been used and (b) by the lack of generalization of vanilla transformers to dense downstream tasks for which relative position is more meaningful than absolute position. We explore three avenues of improvement: first, we introduce a method to collect suitable real-world image pairs at large scale. Second, we experiment with relative positional embeddings and show that they enable vision transformers to perform substantially better. Third, we scale up vision transformer based cross-completion architectures, which is made possible by the use of large amounts of data. With these improvements, we show for the first time that stateof-the-art results on stereo matching and optical flow can be reached without using any classical task-specific techniques like correlation volume, iterative estimation, image warping or multi-scale reasoning, thus paving the way towards universal vision models.

2. Self-Supervised Intensity-Event Stereo Matching(arXiv)

Author : Jinjin Gu, Jinan Zhou, Ringo Sai Wo Chu, Yan Chen, Jiawei Zhang, Xuanye Cheng, Song Zhang, Jimmy S. Ren

Abstract : Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption. Despite these advantages, event cameras cannot be directly applied to computational imaging tasks due to the inability to obtain high-quality intensity and events simultaneously. This paper aims to connect a standalone event camera and a modern intensity camera so that the applications can take advantage of both two sensors. We establish this connection through a multi-modal stereo matching task. We first convert events to a reconstructed image and extend the existing stereo networks to this multi-modality condition. We propose a self-supervised method to train the multi-modal stereo network without using ground truth disparity data. The structure loss calculated on image gradients is used to enable self-supervised learning on such multi-modal data. Exploiting the internal stereo constraint between views with different modalities, we introduce general stereo loss functions, including disparity cross-consistency loss and internal disparity loss, leading to improved performance and robustness compared to existing approaches. The experiments demonstrate the effectiveness of the proposed method, especially the proposed general stereo loss functions, on both synthetic and real datasets. At last, we shed light on employing the aligned events and intensity images in downstream tasks, e.g., video interpolation application.

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Use cases of Stereo Matching part7(Machine Learning + AI) - Medium

Research Analyst/ Associate/ Fellow in Machine Learning and … – Times Higher Education

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The Role

The Sustainable and Green Finance Institute (SGFIN) is a new university-level research institute in the National University of Singapore (NUS), jointly supported by the Monetary Authority of Singapore (MAS) and NUS. SGFIN aspires to develop deep research capabilities in sustainable and green finance, provide thought leadership in the sustainability space, and shape sustainability outcomes across the financial sector and the economy at large.

This role is ideally suited for those wishing to work in academic or industry research in quantitative analysis, particularly in the area of machine learning and artificial intelligence. The responsibilities of the role will include designing and developing various analytical frameworks to analyze structure, unstructured and non-traditional data related to corporate financial, environmental, and social indicators.

There are no teaching obligations for this position, and the candidate will have the opportunity to develop their research portfolio.

Duties and Responsibilities

The successful candidate will be expected to assume the following responsibilities:

Requirements

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Research Analyst/ Associate/ Fellow in Machine Learning and ... - Times Higher Education

AI and Machine Learning: The New Frontier in Global Anti-Money … – Fagen wasanni

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The New Frontier in Global Anti-Money Laundering Efforts: AI and Machine Learning

The world of finance is no stranger to the nefarious activities of money laundering, a global menace that has proven to be a tough nut to crack for financial institutions and regulatory bodies. However, the advent of Artificial Intelligence (AI) and Machine Learning (ML) is heralding a new frontier in global anti-money laundering efforts, offering promising solutions to this age-old problem.

Money laundering, the process of making illegally-gained proceeds appear legal, is a complex and sophisticated crime. It often involves multiple transactions, used to disguise the origin of financial assets so that they appear to have originated from legitimate sources. Traditional methods of detecting and preventing money laundering have often fallen short, due to the sheer volume of financial transactions that occur daily and the clever tactics employed by money launderers.

Enter AI and ML, two technological advancements that are revolutionizing various sectors, including finance. These technologies are now being harnessed to combat money laundering, and early indications suggest they could be game-changers.

AI, with its ability to mimic human intelligence, and ML, a subset of AI that involves the science of getting computers to learn and act like humans, are being used to analyze vast amounts of financial data. They can sift through millions of transactions in a fraction of the time it would take a human, identifying patterns and anomalies that could indicate suspicious activity.

Moreover, these technologies are not just faster; they are also more accurate. Traditional anti-money laundering systems often generate a high number of false positives, leading to wasted time and resources. AI and ML, on the other hand, can learn from past data and improve their accuracy over time, reducing the number of false positives and allowing financial institutions to focus their resources on genuine threats.

The use of AI and ML in anti-money laundering efforts is not without its challenges. For one, these technologies require vast amounts of data to function effectively. This raises privacy concerns, as financial institutions must balance the need for effective anti-money laundering measures with the need to protect their customers personal information. Additionally, the use of AI and ML requires significant investment in technology and skilled personnel, which may be beyond the reach of smaller financial institutions.

Despite these challenges, the potential benefits of AI and ML in combating money laundering cannot be overstated. Regulatory bodies around the world are recognizing this potential and are beginning to incorporate these technologies into their anti-money laundering frameworks. For instance, the Financial Action Task Force (FATF), an intergovernmental body that sets standards for combating money laundering, has acknowledged the role of digital innovation in enhancing the effectiveness of anti-money laundering measures.

In conclusion, AI and ML represent a new frontier in global anti-money laundering efforts. While there are challenges to overcome, the potential of these technologies to revolutionize the fight against money laundering is immense. As they continue to evolve and improve, they promise to be powerful tools in the hands of financial institutions and regulatory bodies, helping to make the world of finance a safer place for all.

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AI and Machine Learning: The New Frontier in Global Anti-Money ... - Fagen wasanni

Harnessing the Power of AI and Machine Learning: Growth … – Fagen wasanni

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Harnessing the Power of AI and Machine Learning: Growth Opportunities in Database Management and SaaS for the Telecommunications Industry

The telecommunications industry is on the cusp of a significant transformation, driven by the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML). These technologies are not only reshaping the way telecom companies operate but also creating unprecedented growth opportunities in database management and Software as a Service (SaaS) sectors.

AI and ML are proving to be game-changers in the telecom industry, enabling companies to streamline operations, enhance customer experience, and drive revenue growth. They are particularly instrumental in managing the vast amounts of data generated by telecom networks. With AI and ML, telecom companies can automate the process of collecting, storing, and analyzing data, thereby improving efficiency and reducing operational costs.

Database management, a critical aspect of telecom operations, is one area where AI and ML are making a significant impact. Traditional database management systems are often unable to handle the sheer volume of data generated by telecom networks. However, AI and ML-powered systems can not only manage large data sets but also provide valuable insights that can help telecom companies make informed business decisions. For instance, predictive analytics, powered by ML, can help telecom companies anticipate customer behavior and tailor their services accordingly, thereby enhancing customer satisfaction and loyalty.

Moreover, AI and ML are also driving growth in the SaaS sector within the telecom industry. SaaS, which allows users to access software over the internet, is becoming increasingly popular among telecom companies due to its scalability and cost-effectiveness. AI and ML are enhancing the capabilities of SaaS solutions, enabling telecom companies to offer more personalized and efficient services. For example, AI-powered chatbots can provide instant customer support, while ML algorithms can optimize network performance in real-time.

The integration of AI and ML into database management and SaaS is also opening up new revenue streams for telecom companies. By offering AI and ML-powered solutions, telecom companies can not only improve their own operations but also provide valuable services to other industries. For instance, telecom companies can offer AI-powered data analytics services to businesses in sectors such as retail, healthcare, and finance, thereby creating additional revenue opportunities.

However, harnessing the power of AI and ML is not without challenges. Telecom companies need to invest in the necessary infrastructure and skills to implement these technologies effectively. Data privacy and security are also major concerns, as AI and ML systems often require access to sensitive information. Therefore, telecom companies need to ensure robust data protection measures are in place.

In conclusion, AI and ML are set to revolutionize the telecommunications industry, offering significant growth opportunities in database management and SaaS. By embracing these technologies, telecom companies can not only enhance their operations but also tap into new revenue streams. However, to fully harness the power of AI and ML, telecom companies need to overcome the associated challenges and invest in the necessary resources. As the telecom industry continues to evolve, the role of AI and ML will undoubtedly become increasingly important.

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Harnessing the Power of AI and Machine Learning: Growth ... - Fagen wasanni

Forget artificial intelligence, its about robots in the Bronx – The Riverdale Press

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By STACY DRIKS

A pair of robots from the Bronx High School of Science that weigh about 125 pounds and are controlled by a simple X-Box remote control showed off their abilities earlier this year during a New York city competition. And came away with some awards.

Behind the remotes were Bronx Science students. And the challenge is simple pick up cones and cubes with their arms and bring them to the other side of the arena.

The teams were competing to advance to the world championships in Houston. At the regional in Manhattan teams from other states, India, Turkey and Azerbaijan competed with their industrialized-size robots.

During the regional two Bronx Science teams were competing: the all-girls FeMaidens and the co-educational team, the Sciborgs, where students spent seven to eight weeks building with coding and testing. The FeMaidens finished third and took home the Team Spirit Award for their enthusiasm. The Sciborgs took home an honorable mention.

Each robot has a battery that looks similar to a car battery, but this one weighs between eight to 12 pounds.

We will go through one of these in every match we can drain this entire battery in three minutes, said Charlie Peskay, one of the main student strategists for the Sciborgs and part of the construction of robots.

Their drive team consists of three people.

Operator: Responsible for movements such as arms and spins. Driver: Drives the robot Coach: Directs operator and driver to work together and says what to pick up and where to place them.

Each game lasts three minutes, and they go through at least five minutes for the playoffs. Then there are more games that would need to be completed for the semi-finals and then finals.

Even though both teams did not make the regional finals, they were awarded and honored by Optimum and parent company Altice USA. The sponsor gave $2,500 to first-place winners; $1,500 to runners-up, and $500 for honorable mentions .

Optimum provides internet, phone services, and more in most households; they are built on innovation, said Rafaella Mazzella of Optimum. The company has long supported the competition and sponsored high school teams and regional competitions throughout its service area.

The money is used often for tools like a portable belt sander and a drill press, said chemistry teacher and robotics adviser Katherine Carr.

FeMaidens took first place for the Excellence in Technology Award. Whereas the Sciborgs received an honorable mention.

It was the gracious professionalism where students wanted to win, but there was not much animosity between the teams.

During the games, opposing teams would need to join an alliance and work together. This year the FeMaidens were aligned with High Voltage Robotics from William Grady in Brooklyn and RoHawks from Hunter College High School.

Its a very interesting dynamic, Carr said. When I first thought of it, I was like, so were friends, were also against each other sometimes.

In one match, the teams will be against each other, and in the next, theyll work together. But the students agree that its more fun that way.

One alliance had used all their timeouts, but they needed time to fix something. And then the other team the other alliance used one of their timeouts to help them fix it, Peskay said.

Both alliances are not competitive with each other, as some might think. They just want every match to be a fair match, Peskay continued.

But this year, students changed it up. And it sounds simple. New wheels.

Our swerve modules are pretty new; in the past, we never did swerve because swerve is a newer version and costs a lot of money, said FeMaidens captain and head of engineering Melody Jiang.

Robotics are many different types of drives, which are used to move and steer the robot. The best part of this new module is that there is a lot more mobility. However, it isnt straightforward to code and build.

For example, their previous wheels were movements to that of a car. The robot would need to be at a complete stop to make a turn. Whereas now, they can move simultaneously.

Warren Yun, Sciborgs captain, said one of the drives is similar to that of a shopping cart going forward and backward.

Theyre really large, and theyre heavy, too, she said.

However, its downfall is the quality of it. If another teams robot pushes a robot to prevent them from scoring with this module, the mobility will help it move.

Thats another part of robotics, Jiang said. Theres a lot of strategies involved because you cant really do everything; you kind of have to debate what you want to prioritize. For example, the drive you sacrifice, like how much you get pushed for that mobility.

The teams always need to trade off on things. Thats why there is a strategy department. Shinyoung Kang is the head of engineering and strategy for the Femaidens. She said she needed to be the salesperson of the match.

Not only does Kangs department needs to convince other teams how they will work well in an alliance together. They need to show off what their robot does and promote themselves.

And even during the competition, the strategy team will meet to find ways to proceed with a game and who to work with.

Both teams have five departments.

Engineering and construction: They make the robot. Electronics: They work with the wires and motors. which can be noticeable for some. Marketing: They communicate with sponsors like Optimum, which provides awards. Programing: They programs the robots. Strategy: They do the challenging part of it, Jiang said.

But getting onto the team can be quite challenging. The students say it has a lower acceptance rate than Harvard.

Approximately 350 people are interested across both clubs, but they only have 10 available spots each year.

We lose a lot of great potential robotics people inspired to do engineering, Carr said.

The two current teams have been around since the early 2000s, and now they are about to start another team but with a different type of robot. The new team will be able to create robots like the two current teams but on a smaller scale. Carr mentioned it should be starting in the fall.

Anthony, founder of the new Apiero team and its senior captain, did not have an opportunity to work with robotics because of Covid. Everything was remote. He hopes expanding a new team will help more people learn about robotics.

Eventually, the schools goal is to have multiple smaller robotic teams. But they need to find more resources, space, and money. Im like (I told assistant principal of physical science and math) we have 20 plus problems. Where do you want to start, Anthony said.

However, Bronx Science is where most of these students started with robotics. Others started with Mindstorms programmed robots made from Lego when they were in elementary and middle school.

Last year, Peskay worked with an elementary school in Manhattan once a week to help their Lego team. His job was to help them with designs.

A lot of this gets us into our career paths personally, I was really into biology before engineering, but now Im going into engineering completely, Jiang said.

This is what kind of led me into the path of engineering, and Im planning on majoring in engineering (in college).

Its a completely student-led program. We make all the curriculums ourselves, we determine the kind of timing of everything, a lot of it is time management, how to communicate with others, communicate with our sponsors and even things such as like forming lifelong friendships,

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Forget artificial intelligence, its about robots in the Bronx - The Riverdale Press

Bridging the Digital Divide: How Artificial Intelligence Services are … – Fagen wasanni

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Bridging the Digital Divide: How Artificial Intelligence Services are Expanding Global Internet Access

The digital divide, a term coined to describe the gap between those who have access to the internet and digital technologies and those who do not, has been a persistent issue globally. However, recent advancements in artificial intelligence (AI) services are playing a pivotal role in bridging this divide, expanding global internet access, and fostering digital inclusivity.

AI, with its transformative potential, is revolutionizing various sectors, and the realm of internet connectivity is no exception. The technology is being harnessed to address the challenges of internet accessibility, particularly in remote and underprivileged regions. AI-powered predictive models are being used to identify areas with low internet penetration, enabling service providers to strategically expand their networks and reach.

One of the key ways AI is facilitating this expansion is through the optimization of network deployment. Traditional methods of network expansion are often time-consuming and expensive, involving extensive groundwork and physical infrastructure. AI, on the other hand, can analyze vast amounts of data to predict the optimal locations for network towers and satellites, significantly reducing costs and accelerating deployment.

Moreover, AI is also enhancing the quality of internet services. Machine learning algorithms can monitor network performance in real-time, identifying and rectifying issues before they impact users. This not only improves the user experience but also increases the efficiency of network maintenance, further contributing to the expansion of internet services.

In addition to network optimization and maintenance, AI is also instrumental in developing innovative solutions for internet access. For instance, AI-powered drones and balloons are being deployed to provide internet connectivity in remote areas. These solutions are particularly beneficial in disaster-stricken regions where traditional network infrastructure may be damaged or non-existent.

Furthermore, AI is playing a crucial role in making the internet more accessible and user-friendly. AI-driven applications such as voice recognition and translation services are making digital platforms more inclusive, enabling individuals with varying levels of literacy and language proficiency to navigate the digital world with ease.

However, while AI is undoubtedly a powerful tool in bridging the digital divide, it is not without its challenges. Concerns around data privacy, security, and the ethical use of AI are paramount. As AI services expand, it is crucial to establish robust regulatory frameworks to ensure that these technologies are used responsibly and that the benefits of increased internet access are not overshadowed by potential risks.

In conclusion, AI services are playing a significant role in expanding global internet access and bridging the digital divide. By optimizing network deployment, enhancing service quality, and developing innovative connectivity solutions, AI is helping to bring the internet to remote and underprivileged regions. At the same time, AI-driven applications are making the digital world more accessible and inclusive. As we move forward, it is essential to address the challenges associated with AI to ensure that its potential is harnessed responsibly and effectively for the benefit of all.

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Protecting Passwords in the Age of Artificial Intelligence – Fagen wasanni

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Passwords remain a critical tool for safeguarding personal information, despite the availability of new security measures. However, the rise of artificial intelligence (AI) poses new challenges and risks to password security. AIs ability to process vast amounts of data and employ advanced machine learning algorithms allows it to analyze patterns, detect correlations, and make countless attempts at cracking passwords within seconds. Unfortunately, cybercriminals are taking advantage of these capabilities.

AI applications designed for password guessing can evade detection and rapidly crack complex passwords. For example, the AI tool PassGAN can decrypt any 7-digit password, even one with symbols, numbers, and mixed cases, in less than 6 minutes. These developments highlight the weaknesses that exist in password security.

AI employs various methods to crack passwords. Enhanced brute force attacks leverage neural networks and machine learning algorithms to test numerous password combinations rapidly. Optimized dictionary attacks analyze leaked password data to create more effective keyword lists, increasing the chances of success. Automated social engineering uses AI to glean personal information from social media profiles and other public sources to facilitate password guessing. Additionally, AI can generate fake passwords and simulate login attempts to confuse intrusion detection systems and gain unauthorized access. Keystroke analysis, utilizing machine learning techniques, can infer passwords accurately by analyzing patterns in keystrokes.

To defend against AI-powered attacks, it is essential to use strong, complex passwords consisting of a combination of numbers, uppercase and lowercase letters, and symbols. Cybersecurity experts recommend passwords of at least 12 characters, if not 15. Implementing multi-factor authentication (MFA) provides an additional layer of security by requiring an additional form of authentication alongside the password. It is crucial to avoid reusing passwords across different accounts and instead use password managers to securely manage multiple passwords. Regularly updating passwords helps minimize the risk of discovery. Education and awareness about online security practices, as well as phishing attacks and social engineering tactics, are vital for both individuals and organizations.

Companies and platforms should invest in advanced security measures, including anomalous behavior detection systems and other technologies, to detect and prevent AI attacks. Importantly, AI algorithms can also contribute to password security by generating strong and unique passwords that are difficult to crack, and by learning users normal behavior to detect any anomalous activity.

While the advancements in AI pose challenges to password security, implementing strong security practices and utilizing advanced protection technologies can enhance defense against potential AI attacks and ensure the safety of personal information.

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Protecting Passwords in the Age of Artificial Intelligence - Fagen wasanni

DNV and KIRIA Extend Collaboration in Cybersecurity and Artificial … – Fagen wasanni

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DNV and the Korea Institute for Robot Industry Advancement (KIRIA) have extended their Memorandum of Understanding (MoU) to collaborate in the fields of cybersecurity and artificial intelligence in the robotics industry. The purpose of this extension is to support the international development of Koreas growing robotics industry and facilitate its entry into the European Union (EU) market.

Under the extended MoU, DNV and KIRIA will share technical and regulatory information about robots and relevant components. They will also cooperate in exchanging technical visits to review safety standards and explore the option of jointly providing advisory services to the Korean robot industry regarding safety standards. Additionally, they will have the opportunity to participate in the standardization process for robots.

The European Commission has recently implemented new legislation, the Machinery Regulation and the Artificial Intelligence Act, to enhance the safety and performance of machinery, including robots. Manufacturers of machinery, including robots, will need to comply with stricter product safety and sustainability requirements to access the European market. They will also need to address emerging risks in areas such as cybersecurity, human-machine interaction, and traceability of safety components and software behavior.

DNV, as an independent assurance and risk management provider, brings their expertise in technical standards development, assessments, certifications, and training to support the Korean robotics industry. KIRIAs goal is to access regulated markets worldwide and ensure that appropriate standards are in place for manufacturers to meet.

By combining DNVs capabilities in artificial intelligence assurance, functional safety, and cybersecurity with KIRIAs ambition, this collaboration aims to drive the maturity and global growth of the Korean robotics industry.

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How Artificial Intelligence is Shaping the Future – Fagen wasanni

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Artificial intelligence (AI) is rapidly transforming various aspects of our daily lives. It has revolutionized the way we shop, access news, and interact with the world around us. As AI continues to advance, its influence will only become more profound.

One major way that AI is expected to change the world is through automation. Already, AI is being used to automate tasks that were once carried out by humans, such as data entry, customer service, and even driving. As AI technology continues to progress, we can anticipate even more automation, which may result in job displacements. However, this evolving technology is also predicted to create new job opportunities in AI development and maintenance.

AI is also being harnessed for personalization purposes. Recommender systems powered by AI algorithms can suggest products tailored to our interests, while AI-driven newsfeeds deliver news articles personalized to our preferences. As AI becomes more sophisticated, it is likely that personalization will become even more prevalent in our lives.

In addition, AI is increasingly making decisions across various fields including healthcare, finance, and business. For instance, AI-powered medical devices aid doctors in accurate disease diagnosis, and AI-powered trading algorithms assist investors in making informed decisions. As AI progresses, we can expect it to play an even larger role in complex decision-making processes.

Another intriguing aspect of AIs advancement is its ability to foster creativity. AI is already being used to generate new forms of art, music, and literature. AI-powered music generators can create original songs, and AI-powered writers can generate poems and stories. As AIs creativity evolves, we can anticipate even more astonishing works of art produced by this technology.

While AI offers potential benefits including increased productivity, improved decision-making, personalized experiences, new forms of art, and solutions to complex problems, it also poses certain risks. Job displacement, bias and discrimination, privacy concerns, security threats, and ethical implications are some of the potential pitfalls associated with AI.

Therefore, it is crucial to carefully consider the potential benefits and risks of AI. Proper planning and management can ensure its positive impact on the world. However, without vigilance, AI could pose a significant threat to our society.

As the future of AI remains uncertain, one thing is clear: it will have a substantial impact on our lives and the world. It is our responsibility to ensure that AI is utilized for the greater good, and safeguards are in place to prevent any harm it may cause.

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How Artificial Intelligence is Shaping the Future - Fagen wasanni

OpenAI Drops Hints About the Future of Artificial Intelligence – Fagen wasanni

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OpenAI, the leading AI research laboratory, has recently given some indications about its future plans. CEO Sam Altman confirmed that the company is indeed working on the development of a GPT-5 model, following Elon Musks call for a pause in AI advancement.

In a surprising move, OpenAI has taken steps to protect its intellectual property by filing a trademark application for the name GPT-5. However, it is important to note that the application is still under review and may take some time to be processed.

Based on the trademark application, we can infer some potential features of the upcoming GPT-5 model. It is expected to include programs and software for using language models, as well as artificial voice and text production. It may also involve language translation capabilities, natural language processing and analysis, and machine learning software.

While these hints provide some insight into what GPT-5 might offer, the full extent of its capabilities remains unknown. Nonetheless, the future of artificial intelligence appears to be headed towards even more exciting advancements. Stay tuned for updates and continue exploring the current language models offered by OpenAI.

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The Role of Artificial Intelligence in Clinical Decision Making – Fagen wasanni

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The integration of artificial intelligence (AI) tools into clinical practice, specifically clinical decision support (CDS) algorithms, is transforming the way physicians make critical decisions regarding patient diagnosis and treatment. However, for these technologies to be effective, physicians must have a thorough understanding of how to utilize them, a skill set that is currently lacking.

AI is increasingly becoming a vital part of medical decision-making, but physicians need to enhance their understanding of these tools to optimize their use. Experts recommend targeted training and a hands-on learning approach.

As AI systems like ChatGPT are being incorporated into everyday use, physicians will start to see these tools integrated into their clinical practice. These tools, known as CDS algorithms, assist healthcare providers in making important determinations such as prescribing antibiotics or recommending heart surgery.

The success of these technologies predominantly relies on how physicians interpret and act upon a tools risk predictions, which necessitates a unique set of skills that many physicians currently lack. According to a new perspective article, physicians need to learn how machines think and work before incorporating algorithms into their medical practice.

Although some clinical decision support tools are already included in electronic medical record systems, healthcare providers often find the current software cumbersome and challenging to use. Physicians dont need to be experts in math or computer science, but they do need a fundamental understanding of how algorithms work in terms of probability and risk adjustment.

To bridge this gap, medical education and clinical training should include explicit coverage of probabilistic reasoning tailored specifically to CDS algorithms. This training should encompass interpreting performance measures, evaluating algorithm output critically, and incorporating CDS predictions into clinical decision-making. Physicians should also engage in practice-based learning by applying algorithms to individual patients and exploring the impact of different inputs on predictions.

In response to these challenges, the University of Maryland, Baltimore, University of Maryland, College Park, and University of Maryland Medical System have launched plans for the Institute for Health Computing (IHC). The IHC will leverage AI and other computing methods to improve disease diagnosis, prevention, and treatment through the evaluation of medical health data. This institute will also provide healthcare providers with the necessary education and training on the latest technologies.

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The Role of Artificial Intelligence in Clinical Decision Making - Fagen wasanni

Opinions on Artificial Intelligence Vary in Finland – Fagen wasanni

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A recent survey conducted by the independent non-profit organization Foundation for Municipal Development revealed different perspectives among the Finnish population regarding the benefits and risks associated with artificial intelligence (AI).

The survey, which involved over 1,000 participants, found that 62% of respondents believed AI would enhance industrial production efficiency, while 50% thought it would increase work productivity. However, almost half of the participants expressed concerns about AI weakening privacy protection, and over a third believed it would have a negative impact on job opportunities and customer service. Furthermore, around a third of the respondents felt that accessing accurate, error-free information would become more difficult with the adoption of AI.

Regarding transportation safety, approximately 40% of those surveyed believed that AI would improve it, while others were unsure or believed it would have no significant effect. The opinions on the impact of AI on climate change, democracy, and social equality were also divided.

The survey participants had diverse views on the personal impact of AI in their lives. Around a fifth anticipated a positive impact, a similar number expected negative consequences, and the remainder were uncertain.

Political affiliation was found to shape perceptions of AI. Supporters of the National Coalition Party and the Greens were more likely to hold positive opinions about the technology, while those backing the Finns Party and the Centre Party expressed more negative views. Age was another influencing factor, as younger people tended to view AI more positively, while older individuals, rural residents, and those with lower education levels were more pessimistic.

The survey conducted by Kantar Public took place in June.

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Opinions on Artificial Intelligence Vary in Finland - Fagen wasanni

The Impact of Artificial Intelligence on Society – Fagen wasanni

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This summer, artificial intelligence (AI) demonstrated its remarkable capability by extracting John Lennons voice from a demo song recorded shortly before his death in 1980. By removing the electrical buzzing and piano accompaniment, AI successfully mixed Lennons voice into a final Beatles project led by Paul McCartney.

The ability of AI to recognize distinctive human voices has captivated the attention of many. However, it has also raised concerns about the potential impact of this powerful tool. Like any tool, the impact of AI depends on the intentions of the user. While it has many beneficial uses in our daily lives, such as grammar autocorrect and real-time navigation on smartphones, there is also the possibility of AI being manipulated for malicious purposes.

Instances of AI impersonating individuals for nefarious reasons have already occurred. For example, a mother in Arizona received a convincing AI-engineered recording of her daughter screaming that she had been kidnapped. The perpetrator threatened to harm the girl if a ransom was not paid. Fortunately, it was later discovered that the girl was safe at a skiing competition, but this incident highlights the potential dangers of AI.

These contrasting stories of AIs applications underscore the need for responsible use and regulation of this technology. While international gatekeepers work towards encouraging responsible AI utilization and preventing its abuses, it is essential for individuals to understand the implications and impact of AI in their daily lives.

Taking the time to understand ourselves and others on a deeper level through traditional means is crucial. A chance encounter between strangers, as witnessed during a family reunion, demonstrated how people from different backgrounds and worlds can connect through simple gestures. Moreover, taking the time to pay attention to nonverbal cues and support those with special needs, like the authors son, fosters true understanding and communication.

Additionally, AI can assist in organizing and finding relevant photos, as demonstrated by face recognition technology. However, there will always be a significant difference between recognizing someones face and cherishing the connection and memories associated with that individual.

In conclusion, while AI has undoubtedly shown its potential for innovation and discovery, it is crucial to exercise caution and responsible usage to prevent any negative consequences. Balancing the benefits of AI with human connection and understanding is key to ensuring a harmonious coexistence with this technology.

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The Impact of Artificial Intelligence on Society - Fagen wasanni

Artificial Intelligence and the Perception of Dogs’ Ears – Fagen wasanni

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The use of generative artificial intelligence in the world of art has sparked mixed reactions. Photographer Sophie Gamand recently explored how AI views dogs ears in her project featuring shelter dogs with cropped ears. Surprisingly, the AI algorithms leaned towards the belief that dogs should have floppy ears, despite the existence of breed standards and human preferences for cropped ears.

Using her own photographs of shelter dogs, many of which had severely shortened ears, Gamand aimed to restore their ears through AI technology. She utilized the DALL-E 2 program to understand how AI perceives a dogs appearance. Although the process was occasionally frustrating, Gamand wanted to minimize her interference to truly explore what the computer thought a dog should look like. It turned out that AI considers dogs to have intact ears.

Gamand believes that AI has the potential to separate genuine artists from those who rely too heavily on the technology. While AI can create stunning images, it is crucial for artists to consider their own artistic context, aesthetics, and the messages they want to convey. The use of AI should align with an artists overall vision and not solely rely on the work of others.

The ear cropping project is just one example of Gamand using AI in her work. She has also transformed AI interpretations of dogs into oil paintings and used ChatGTP to craft a letter from a shelter dog to its previous owner. Despite the benefits of AI, Gamand emphasizes the importance of ethical and honest artistic practices with this technology.

Gamands photography focuses on raising awareness for misunderstood dog breeds and animals in shelters. She has dedicated her time to volunteering at shelters across the United States and has successfully fundraised for animal shelters through her Instagram feed. Gamand believes that photographs have the power to create emotional connections between adoptable animals and potential pet owners.

Through her artwork, Gamand aims to reflect on humanity by observing dogs. However, sometimes the mirror reveals uncomfortable truths, such as the prevalence of ear cropping. She questions why certain breeds continue to undergo this procedure for aesthetic reasons, even though they are living safely as family pets. Gamand believes this reflects a broader issue in our relationship with dogs and the natural world, highlighting the need for better understanding and decision-making on behalf of our companions.

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The Elements of AI: Free Online Course on Artificial Intelligence – Fagen wasanni

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The field of artificial intelligence (AI) has revolutionized various aspects of our lives, enabling machines to perform tasks that were previously exclusive to human intelligence. However, along with the countless opportunities that this technological revolution has brought, there are also ethical, security, and regulatory challenges to navigate. To address this pressing need, an online initiative called Elements of AI has been created.

Elements of AI is a collaboration between Reaktor Inc. and the University of Helsinki, and it offers an online course that provides a solid foundation for understanding AI. The course is presented online and free of charge, making it accessible to anyone interested in delving into the fascinating world of AI.

The course is divided into two parts. The first section, Introduction to AI, introduces participants to the core concepts of AI. This module is designed for beginners who have no prior knowledge of AI. The second section, Creating AI, is aimed at individuals with basic programming skills in Python. In this phase of the course, participants explore how to build practical AI applications and delve into the capabilities of this disruptive technology.

Upon completing the course, participants receive an Artificial Intelligence certification, which not only enriches their knowledge but also adds professional credibility. In a competitive and rapidly evolving job market, this certification serves as a mark of quality and competence.

Since its launch in May 2018, Elements of AI has had over 140,000 subscriptions from more than 90 countries worldwide. The vision behind this course is to inspire, educate, and promote well-being through knowledge. It has been praised by Sundar Pichai, CEO of Google, as an inspiring example that levels the playing field and allows more people to benefit from the advances of AI.

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The Elements of AI: Free Online Course on Artificial Intelligence - Fagen wasanni