Category Archives: Machine Learning

Security industry needs to tackle nascent AI threats before its too late

As machine learning (ML) systems become a staple of everyday life, the security threats they entail will spill over into all kinds of applications we use, according to a new report.

Unlike traditional software, where flaws in design and source code account for most security issues, in AI systems, vulnerabilities can exist in images, audio files, text, and other data used to train and run machine learning models.

This is according to researchers from Adversa, a Tel Aviv-based start-up that focuses on security for artificial intelligence (AI) systems, who outlined their latest findings in their report, The Road to Secure and Trusted AI, this month.

This makes it more difficult to filter, handle, and detect malicious inputs and interactions, the report warns, adding that threat actors will eventually weaponize AI for malicious purposes.

Unfortunately, the AI industry hasnt even begun to solve these challenges yet, jeopardizing the security of already deployed and future AI systems.

Theres already a body of research that shows many machine learning systems are vulnerable to adversarial attacks, imperceptible manipulations that cause models to behave erratically.

BACKGROUND Adversarial attacks against machine learning systems everything you need to know

According to the researchers at Adversa, machine learning systems that process visual data account for most of the work on adversarial attacks, followed by analytics, language processing, and autonomy.

Machine learning systems have a distinct attack surface

With the growth of AI, cyberattacks will focus on fooling new visual and conversational Interfaces, the researchers write.

Additionally, as AI systems rely on their own learning and decision making, cybercriminals will shift their attention from traditional software workflows to algorithms powering analytical and autonomy capabilities of AI systems.

Web developers who are integrating machine learning models into their applications should take note of these security issues, warned Alex Polyakov, co-founder and CEO of Adversa.

There is definitely a big difference in so-called digital and physical attacks. Now, it is much easier to perform digital attacks against web applications: sometimes changing only one pixel is enough to cause a misclassification, Polyakov told The Daily Swig, adding that attacks against ML systems in the physical world have more stringent demands and require much more time and knowledge.

Read more of the latest infosec research news

Polyakov also warned about vulnerabilities in machine learning models served over the web such as API services provided by large tech companies.

Most of the models we saw online are vulnerable, and it has been proven by several research reports as well as by our internal tests, Polyakov. With some tricks, it is possible to train an attack on one model and then transfer it to another model without knowing any special details of it.

Also, you can perform CopyCat attack to steal a model, apply the attack on it and then use this attack on the API.

Most machine learning algorithms require large sets of labeled data to train models. In many cases, instead of going through the effort of creating their own datasets, machine learning developers search and download datasets published on GitHub, Kaggle, or other web platforms.

Eugene Neelou, co-founder and CTO of Adversa, warned about potential vulnerabilities in these datasets that can lead to data poisoning attacks.

Poisoning data with maliciously crafted data samples may make AI models learn those data entries during training, thus learning malicious triggers, Neelou told The Daily Swig. The model will behave as intended in normal conditions, but malicious actors may call those hidden triggers during attacks.

RELATED TrojanNet a simple yet effective attack on machine learning models

Neelou also warned about trojan attacks, where adversaries distribute contaminated models on web platforms.

Instead of poisoning data, attackers have control over the AI model internal parameters, Neelou said. They could train/customize and distribute their infected models via GitHub or model platforms/marketplaces.

Unfortunately, GitHub and other platforms dont yet have any safeguards in place to detect and defend against data poisoning schemes. This makes it very easy for attackers to spread contaminated datasets and models across the web.

Attacks against machine learning and AI systems are set to increase over the coming years

Neelou warned that while AI is extensively used in myriads of organizations, there are no efficient AI defenses.

He also raised concern that under currently established roles and procedures, no one is responsible for AI/ML security.

AI security is fundamentally different from traditional computer security, so it falls under the radar for cybersecurity teams, he said. Its also often out of scope for practitioners involved in responsible/ethical AI, and regular AI engineering hasn't solved the MLOps and QA testing yet.

Check out more machine learning security news

On the bright side, Polyakov said that adversarial attacks can also be used for good. Adversa recently helped one of its clients use adversarial manipulations to develop web CAPTCHA queries that are resilient against bot attacks.

The technology itself is a double-edged sword and can serve both good and bad, he said.

Adversa is one of several organizations involved in dealing with the emerging threats of machine learning systems.

Last year, in a joint effort, several major tech companies released the Adversarial Threat ML Matrix, a set of practices and procedures meant to secure the machine learning training and delivery pipeline in different settings.

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Machine learning security vulnerabilities are a growing threat to the web, report highlights - The Daily Swig

Thanks to advanced technology, consumers can now access, spend, and invest their money in safer ways. Lenders looking to win new business should apply technology to make processes faster and more efficient.

Artificial intelligence has transformed the way we handle money by giving the financial industry a smarter, more convenient way to meet customer demands.

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Machine learning helps financial institutions develop systems that improve user experiences by adjusting parameters automatically. It's become easier to handle the extensive amount of data related to daily financial transactions.

Machine learning and AI are changing how the financial industry does business in these ways:

Fraud Detection

The need to enhance fraud detection and cybersecurity is no longer an option. People pay bills, transfer money, trade stocks, and deposit checks through smartphone applications or online accounts.

Many businesses store their information online, increasing the risk of security breaches. Fraud is a major concern for companies that offer financial services--including banks--which lose billions of dollars yearly.

Machine learning and artificial intelligence technologies improve online finance security by scanning data and identifying unique activities. They then highlight these activities for further investigation. This technology can also prevent credential stuffing and credit application fraud.

Cognito is a cyber-threat detection and hunting software impacting the financial space positively. Its built by a company called Vectra. Besides detecting threats automatically, it can expose hidden attackers that target financial institutions and also pinpoint compromised information.

Making Credit Decisions

Having good credit can help you rent an apartment of your choice, land a great job, and explore different financing options. Now more than ever, many things depend on your credit history, even taking loans and credit cards.

Lenders and banks now use artificial intelligence to make smarter decisions. They use AI to accurately assess borrowers, simplifying the underwriting process. This helps save time and financial resources that would have been spent on humans.

Data--such as income, age, and credit behavior--can be used to determine if customers qualify for loans or insurance. Machine learning accurately calculates credit scores using several factors, making loan approval quick and easy.

AI software like ZestFinance can help you to easily find online lenders, all you do is type title loans near me. Its automated machine learning platform (ZAML) works with companies to assess borrowers without credit history and little to no credit information. The transparent platform helps lenders to better evaluate borrowers who are considered high risk.

Algorithmic Trading

Many businesses depend on accurate forecasts for their continued existence. In the finance industry, time is money. Financial markets are now using machine learning to develop faster, more exact mathematical models. These are better at identifying risks, showing trends, and providing advanced information in real time.

Financial institutions and hedge fund managers are applying artificial intelligence in quantitative or algorithmic trading. This trading captures patterns from large data sets to identify factors that may cause security prices to rise or fall, making trading strategic.

Tools like Kavout combine quantitative analysis with machine learning to simultaneously process large, complex, unstructured data faster and more efficiently. The Kai Score ranks stocks using AI to generate numbers. A higher Kai Score means the stock is likely to outperform the market.

Online lenders and other financial institutions can now streamline processes thanks to faster, more efficient tools. Consumers no longer have to worry about unnecessary delays and the safety of their transactions.

About The Author:

Aqib Ijaz is a content writingguru at Eyes on Solution. He is adept in IT as well. He loves to write on different topics. In his free time, he likes to travel and explore different parts of the world.

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3 Applications of Machine Learning and AI in Finance - TAPinto.net

The world today has joined hands with advanced technologies like AI and machine learning and is progressing at a rapid pace. The advent of these technologies has almost made a world without them, unimaginable. Artificial intelligence and machine learning have invaded every sector that can be thought of and have shown substantial transformation and revolution in them.

The pandemic today has left us no choice but to adapt to a digital and technological culture. While AI and ML hold the promises of changing the world for good, they also empower the skilled ones to mint money to the point of becoming a millionaire. Here is how:

Studying artificial intelligence and machine learning has become crucial to being a member of big IT sectors and Silicon Valley companies. Truth be told, the field of artificial intelligence and machine learning is not everybodys cup of tea owing to its complex operations and algorithms.

Besides the fact that the fields are fantasised, there are several other reasons why honing skills in AI and machine learning can make you economically rich and stable.

1. AI-driven gadgets are taking over human workforce

As already mentioned that the improvements, advancements and success are getting as higher as the sky, they have now started to replace human work force. The pandemic has mandated remote working for humans but someone has to be there in the office to look after the operations. This objective is now achieved with machines.

2. Use of automation in manufacturing and supply chain management

Automation is rising in fashion and is fished upon by manufacturing companies, and service providers of supply chain management programs.

The manufacturing sector and companies have suffered immensely when business operations where brought into a halt. It found itself drowning when work resumed. Employees assigned to the back office had to handle plethora of work simultaneously, inevitably being erroneous. Additionally, limitless responsibility tended to tire them out, hindering the quality and flow of work.

3. Robotics are taking over the world

Be it defence or any other sector or discipline, robotics play a significant role. Nations are excelling at designing humanoids that can not only mimic human intelligence but can also carry out appropriate business decisions.

The importance and need for artificial intelligence and machine learning cannot be re-iterated enough number of times. Their importance supports the fact why being strongly armed with skills in AI and ML can not only help one land in a promising career but also bring fat salaries as rewards.

There are varied ways to learn machine learning and artificial intelligence. These days, kids mostly enrol themselves into virtual courses that offer extensive training on machine learning and artificial intelligence.

Grasping an in-depth learning about artificial intelligence and machine learning cannot be done all by the self. It is suggested to opt for virtual courses available for a proper training in AI and ML.

Artificial intelligence companies and the companies engulfed in machine learning always have all eyes and ears for the ones who have mastered skills in these domains. The famous companies namely, Google, Apple and Microsoft believe that the AI and ML skilled personalities can renovate and improve the future of AI.

These companies are ready to disburse huge amounts in the accounts of their employees in exchange of efforts from them that are potent to burnish and eliminate the pain points, setting the company on the path of success.

Link:
AI and ML can Help to Turn Millionaire Dreams into Reality - Analytics Insight

"We are thrilled to have Andrey's leadership and experience and believe he will be instrumental in continuing to expand the use of systems and technology within the company," said Ajai Nair, CIO. "His extensive background in application development and business robotic automation software brings a wealth of knowledge to the team that is necessary to accelerate a successful digital transformation, allowing us to faster determine measurable business benefits and better serve our customers."

Andrey joins the JG Wentworth team from UiPath where he served as Director on their Competitive and Market Intelligence team. During his tenure at UiPath he utilized data mining techniques to analyze the marketplaces, enable sales and predict cashflows.

"I am excited to join a market leader focused on helping customers improve their financial health. I look forward to this unique opportunity to be part of the evolution of JG Wentworth by leveraging AI and automation to positively impact our customers' lives," said Andrey.

Andrey earned his Bachelor of Science in both Information & Systems Engineering and Analytical Finance from the Lehigh University and holds a Master of Science from The George Washington University and a Master of Business Administration from New York University, Leonard N. Stern School of Business.

About JG WentworthJG Wentworth is a financial services company that focuses on helping customers who are experiencing financial hardship or need to quickly access cash. Its services include debt relief, structured settlement payment purchasing, annuity payment purchasing, lottery and casino payment purchasing. J.G. Wentworth was founded in 1991 and currently has offices in Chesterbrook, Pennsylvania, Radnor, Pennsylvania and Rockville, Maryland. For more information about J.G. Wentworth visit http://www.jgwentworth.com or use the information provided below.

SOURCE The JG Wentworth Company

http://www.jgwentworth.com

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JG Wentworth Welcomes Andrey Zelenovsky as their Vice President of Artificial Intelligence and Machine Learning - PRNewswire

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.

The algorithm, devised by a scientist at the U.S. Department of Energys (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations, said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. What Im doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.

Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a serving algorithm, then made accurate predictions of the orbits of other planets in the solar system without using Newtons laws of motion and gravitation. Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data, Qin said. There is no law of physics in the middle.

PPPL physicist Hong Qin in front of images of planetary orbits and computer code. Credit: Elle Starkman / PPPL Office of Communications

The program does not happen upon accurate predictions by accident. Hong taught the program the underlying principle used by nature to determine the dynamics of any physical system, said Joshua Burby, a physicist at the DOEs Los Alamos National Laboratory who earned his Ph.D. at Princeton under Qins mentorship. The payoff is that the network learns the laws of planetary motion after witnessing very few training examples. In other words, his code really learns the laws of physics.

Machine learning is what makes computer programs like Google Translate possible. Google Translate sifts through a vast amount of information to determine how frequently one word in one language has been translated into a word in the other language. In this way, the program can make an accurate translation without actually learning either language.

The process also appears in philosophical thought experiments like John Searles Chinese Room. In that scenario, a person who did not know Chinese could nevertheless translate a Chinese sentence into English or any other language by using a set of instructions, or rules, that would substitute for understanding. The thought experiment raises questions about what, at root, it means to understand anything at all, and whether understanding implies that something else is happening in the mind besides following rules.

Qin was inspired in part by Oxford philosopher Nick Bostroms philosophical thought experiment that the universe is a computer simulation. If that were true, then fundamental physical laws should reveal that the universe consists of individual chunks of space-time, like pixels in a video game. If we live in a simulation, our world has to be discrete, Qin said. The black box technique Qin devised does not require that physicists believe the simulation conjecture literally, though it builds on this idea to create a program that makes accurate physical predictions.

The resulting pixelated view of the world, akin to what is portrayed in the movie The Matrix, is known as a discrete field theory, which views the universe as composed of individual bits and differs from the theories that people normally create. While scientists typically devise overarching concepts of how the physical world behaves, computers just assemble a collection of data points.

Qin and Eric Palmerduca, a graduate student in the Princeton University Program in Plasma Physics, are now developing ways to use discrete field theories to predict the behavior of particles of plasma in fusion experiments conducted by scientists around the world. The most widely used fusion facilities are doughnut-shaped tokamaks that confine the plasma in powerful magnetic fields.

Fusion, the power that drives the sun and stars, combines light elements in the form of plasma the hot, charged state of matter composed of free electrons and atomic nuclei that represents 99% of the visible universe to generate massive amounts of energy. Scientists are seeking to replicate fusion on Earth for a virtually inexhaustible supply of power to generate electricity.

In a magnetic fusion device, the dynamics of plasmas are complexand multi-scale, and the effective governing laws or computational models for a particular physical process that we are interested in are not always clear, Qin said. In these scenarios, we can apply the machine learning technique that I developed to create a discrete field theory and then apply this discrete field theory to understand and predict new experimental observations.

This process opens up questions about the nature of science itself. Dont scientists want to develop physics theories that explain the world, instead of simply amassing data? Arent theories fundamental to physics and necessary to explain and understand phenomena?

I would argue that the ultimate goal of any scientist is prediction, Qin said. You might not necessarily need a law. For example, if I can perfectly predict a planetary orbit, I dont need to know Newtons laws of gravitation and motion. You could argue that by doing so you would understand less than if you knew Newtons laws. In a sense, that is correct. But from a practical point of view, making accurate predictions is not doing anything less.

Machine learning could also open up possibilities for more research. It significantly broadens the scope of problems that you can tackle because all you need to get going is data, Palmerduca said.

The technique could also lead to the development of a traditional physical theory. While in some sense this method precludes the need of such a theory, it can also be viewed as a path toward one, Palmerduca said. When youre trying to deduce a theory, youd like to have as much data at your disposal as possible. If youre given some data, you can use machine learning to fill in gaps in that data or otherwise expand the data set.

Reference: Machine learning and serving of discrete field theories by Hong Qin, 9 November 2020, Scientific Reports.DOI: 10.1038/s41598-020-76301-0

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New Machine Learning Theory Raises Questions About the Very Nature of Science - SciTechDaily

The expectation is that in 2021, artificial intelligence and machine learning technologies will continue to become more mainstream. Businesses that havent traditionally viewed themselves as candidates for AI applications will embrace these technologies.

A great story of machine learning being used in an industry that is not known for its technology investments is the story of Makoto Koike. Using Googles TensorFlow, Makoto initially developed a cucumber sorting system using pictures that he took of the cucumbers. With that small step, a machine learning cucumber sorting system was born.

Getting started with AI and machine learning is becoming increasingly accessible for organizations of all sizes. Technology-as-a-service companies including Microsoft, AWS and Google all have offerings that will get most organizations started on their AI and machine learning journeys. These technologies can be used to automate and streamline manual business processes that have historically been resource-intensive.

An article on forbes.com claims that, as business leaders continue to refine their processes to support the new normal of the Covid-19 pandemic, they should be considering where these technologies might help reduce manual, resource-intensive or paper-based processes. Any manual process should be fair game for review for automation possibilities.

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Using AI and Machine Learning will increase in horti industry - hortidaily.com