Category Archives: Machine Learning
Machine learning techniques applied to crack CAPTCHAs – The Daily Swig
A newly released tool makes light work of solving human verification challenges
F-Secure says its achieved 90% accuracy in cracking Microsoft Outlooks text-based CAPTCHAs using its AI-based CAPTCHA-cracking server, CAPTCHA22.
For the last two years, the security firm has been using machine learning techniques to train unique models that solve a particular CAPTCHA, rather than trying to build a one-size-fits-all model.
And, recently, it decided to try the system out on a CAPTCHA used by an Outlook Web App (OWA) portal.
The initial attempt, according to F-Secure, was comparatively unsuccessful, with the team finding that after manually labelling around 200 CAPTCHAs, it could only identify the characters with an accuracy of 22%.
The first issue to emerge was noise, with the team determining that the greyscale value of noise and text was always within two distinct and constant ranges. Tweaks to the tool helped filter out the noise.
The team also realized that some of the test CAPTCHAs had been labelled incorrectly, with confusion between, for example, l and I (lower case L and upper case i). Fixing this shortcoming brought the accuracy up to 47%.
More challenging, though, was handling the CAPTCHA submission to Outlooks web portal.
There was no CAPTCHA POST request, with the CAPTCHA instead sent as a value appended to a cookie. JavaScript was used to keylog the user as the answer to the CAPTCHA was typed.
Instead of trying to replicate what occurred in JS, we decided to use Pyppeteer, a browsing simulation Python package, to simulate a user entering the CAPTCHA, said Tinus Green, a senior information security consultant at F-Secure
Doing this, the JS would automatically take care of the submission for us.
Green added: We could use this simulation software to solve the CAPTCHA whenever it blocked entries and once solved, we could continue with our conventional attack, hence automating the process once again.
We have now also refactored CAPTCHA22 for a public release.
CAPTCHAs are challenge-response tests used by many websites in an attempt to distinguish between genuine requests to sign-up to or access web services by a human user and automated requests by bots.
Spammers, for example, attempt to circumvent CAPTCHAs in order to create accounts they can later abuse to distribute junk mail.
CAPTCHAs are something of a magnet for cybercriminals and security researchers, with web admins struggling to stay one step ahead.
Late last year, for example, PortSwigger Web Security uncovered a security weakness in Googles reCAPTCHA that allowed it to be partially bypassed by using Turbo Intruder, a research-focused Burp Suite extension, to trigger a race condition.
Soon after, a team of academics from the University of Maryland was able to circumvent Googles reCAPTCHA v2s anti-bot mechanism using a Python-based program called UnCaptcha, which could solve its audio challenges.
Green said: There is a catch 22 between creating a CAPTCHA that is user friendly grandma safe as we call it and sufficiently complex to prevent solving through computers. At this point it seems as if the balance does not exist.
Web admins shouldnt, he says, give away half the required information through username enumeration, and users should be required to set strong pass phrases conforming to NIST standards.
And, he adds: Accept that accounts can be breached, and therefore implement MFA [multi-factor authentication] as an additional barrier.
RELATED New tool highlights shortcomings in reCAPTCHAs anti-bot engine
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Machine learning techniques applied to crack CAPTCHAs - The Daily Swig
This Bengaluru start-up is using machine learning and IoT to detect the freshness of your fruits and veggies – EdexLive
Srishti Batra and Rubal Chib, Founders, QZense (Pics: Rubal Chib)
While India is one of the largest producers of food crops in the world, a lot of this - 16 per cent to be exact - is wasted every year due to inadequate infrastructure and supply chain management. The cold storages aren't equipped to reduce food wastage even when a large number of India's population goes hungry and the country continues to slip lower in the Global Hunger Index. The need of the hour is some disruptive technology that can address this problem. That is whereQZensecomes in. Founded byRubal ChibandSrishti Batra, this Bengaluru-based start-up has developed a device, using machine learning and IoT, to detect the shelf life and ripeness of agricultural produce.
The start-up has developed two devices, which use the sense of smell and sight to detect the quality of produce. "The devices will study their internal composition and biochemical processes to assess the health of fresh food," says Rubal. The two devices include a handheld scanner and a data logger. "The scanner, called QScan, is portable and can be used to grade and sort the fresh produce. For example, it can tell the retailer about the level of sweetness in fruits. The loggers can be installed in cold storages or in the vehicles transporting produce. It has in-built sensors to detect the gases and compounds produced by the fruits and vegetables. It will alert the user when the rate of degradation is increasing so that the food can be distributed sooner," adds Rubal.
QZense will be accelerated by the Indian School of Business (ISB), Hyderabad's Atal Innovation Centre under their ESTAC programme, which works with and mentors start-ups in the field of agriculture. They will pilot their project with the Government of Punjab, which is set to begin next month. "Under the programme, ISB will mentor agricultural start-ups like us and help improve our model over the next three to four months," says Rubal. Srishti and Rubal first pitched their start-up idea in a Shark Tank-like scenario and received initial funding from Entrepreneur First, a UK-based international talentinvestor.
Rubal hopes that technology will help improve the supply chain and more produce will reach the consumers. "We lose around 14 billion dollars in food waste every year. If storage and grading are made more efficient, this will have a greater economic and social impact," she feels, "A lot of high-grade fruits and vegetables in the supermarkets have been imported since India doesn't have technology and infrastructure to store or grade fresh produce. Technology can improve exports and quality of food supplied in the Indian markets." The duo eventually wants to develop devices for farmers as well for them to understand the right time to harvest their crops.
But this isn't Rubal's first brush with handling a start-up. "I have worked in product development for a multinational company and also with several start-ups in their IoT teams," says Rubal. Her co-founder Srishti has a PhD in computational biology (a field of study that uses machine learning on biological data) and has specifically researched the sense of smell.
Machine Learning Takes UWB Localization to the Next Level – Eetasia.com
Article By : Nitin Dahad
Imec uses machine learning algorithms in chip design to achieve cm accuracy and low-power ultra-wideband (UWB) localization...
Imec this week said it has developed next generation ultra-wideband (UWB) technology that uses digital RF and machine learning to achieve a ranging accuracy of less than 10cm in challenging environments while consuming 10 times less power than todays implementations.
The research and innovation hub announced two new innovations from its secure proximity research program for secure and very high accuracy ranging technology. One is hardware-based, with a digital-style RF circuit design such as its all-digital phase locked loop (PLL), to achieve a low power consumption of less than 4mW/20mW (Tx/Rx), which it claims is up to 10 times better than todays implementations. The second is software-based enhancements which utilize machine learning based error correction algorithms to allow less than 10cm ranging accuracy in challenging environments.
Explaining the context imec said ultra-wideband technology is currently well suited to support a variety of high accuracy and secure wireless ranging use-cases, such as the smart lock solutions commonly being applied in automotive; it automatically unlocks a cars doors as its owner approaches, while locking the car when the owner moves away.
However, despite its benefits such as being inherently more difficult to compromise than some alternatives, its potential has largely remained untapped because of its higher power consumption and larger footprint. Hence imec said the hardware and software innovations it has introduced mark an important step to unlocking the technologys full potential, and opens up the opportunity for micro-localization services beyond the secure keyless access that its been widely promoted for so far, to AR/VR gaming, asset tracking and robotics.
Christian Bachmann, the program manager at imec, said, UWBs power consumption, chip size and associated cost have been prohibitive factors to the technologys adoption, especially when it comes to the deployment of wireless ranging applications. Imecs brand-new UWB chip developments result in a significant reduction of the technologys footprint based on digital-style RF-concepts: we have been able to integrate an entire transceiver including three receivers for angle-of-arrival measurements on an area of less than 1mm.
He added this is when implemented on advanced semiconductor process nodes applicable to IoT sensor node devices. The new chip is also compliant with the new IEEE 802.15.4z standard supported by high-impact industry consortia such as the Car Connectivity Consortium (CCC) and Fine Ranging (FiRa).
Complementing the hardware developments, researchers from IDLab (an imec research group at Ghent University) have come up with software-based enhancements that significantly improve UWBs wireless ranging performance in challenging environments. This is particularly in factories or warehouses where people and machines constantly move around, and with metallic obstacles causing massive reflection all of which impact the quality of UWBs localization and distance measurements.
Using machine learning, it has created smart anchor selection algorithms that detect the (non) line-of-sight between UWB anchors and the mobile devices that are being tracked. Building on that knowledge, the ranging quality is estimated, and ranging errors are corrected. The approach also comes with machine learning features that enable adaptive tuning of the networks physical layer parameters, which allows appropriate steps to then be initiated to mitigate those ranging errors for instance by tuning the anchors radios.
Professor Eli De Poorter from IDLab said, We have already demonstrated an UWB ranging accuracy of better than 10cm in such very challenging industrial environments, which is a factor of two improvement compared to existing approaches. Additionally, while UWB localization use-cases are typically custom-built and often depend on manual configuration, our smart anchor selection software works in any scenario as it runs in the application layer.
Through these adaptive configurations, the next-generation low power and high-accuracy UWB chips can be utilized in a wide range of other applications such as improved contact tracing during epidemics using small and privacy-aware devices.
In fact, imec has already licensed the technology to its spin-off Lopos, which hasreleased a wearable that enables enforcement of Covid-19 social distancingby warning employees through an audible or haptic alarm when they are violating safe distance guidelines while approaching each other.
Choosing UWB instead of Bluetooth, Lopos SafeDistance wearable operates as a standalone solution which weighs 75g and has a battery life of 2-5 days. The UWB-technology based device enables safe, highly accurate (< 15cm error margin) distance measurement. When two wearables approach each other, the exact distance between the devices (which is adjustable) is measured and an alarm is activated when a minimum safety distance is not respected.
Since it is standalone, no personal data is logged and there is no gateway, server or other infrastructure required. Lopos has already ramped up production to meet market demand, with multiple large-scale orders received over the last few weeks from companies active in a wide range of different sectors.
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Machine Learning Takes UWB Localization to the Next Level - Eetasia.com
New Research Claims to Have Found a Solution to Machine Learning Attacks – Analytics Insight
AI has been making some major strides in the computing world in recent years. But that also means they have become increasingly vulnerable to security concerns. Just by examining the power usage patterns or signatures during operations, one may able to gain access to sensitive information housed by a computer system. And in AI, machine learning algorithms are more prone to such attacks. The same algorithms are employed in smart home devices, cars to identify different forms of images and sounds that are embedded with specialized computing chips.
These chips rely on using neural networks, instead of a cloud computing server located in a data center miles away. Due to such physical proximity, the neural networks can perform computations, at a faster rate, with minimal delay. This also makes it simple for hackers to reverse-engineer the chips inner workings using a method known as differential power analysis (DPA). Thereby, it is a warning threat for the Internet of Things/edge devices because of their power signatures or electromagnetic radiation signage. If leaked, the neural model, including weights, biases, and hyper-parameters, can violate data privacy and intellectual property rights.
Recently a team of researchers of North Carolina State University presented a preprint paper at the 2020 IEEE International Symposium on Hardware Oriented Security and Trust in San Jose, California. The paper mentions about the DPA framework to neural-network classiers. First, it shows DPA attacks during inference to extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the rst countermeasures against these attacks by augmenting masking. The resulting design uses novel masked components such as masked adder trees for fully connected layers and masked Rectier Linear Units for activation functions. The team is led by Aydin Aysu, an assistant professor of electrical and computer engineering at North Carolina State University in Raleigh.
While DPA attacks have been successful against targets like the cryptographic algorithms that safeguard digital information and the smart chips found in ATM cards or credit cards, the team observes neural networks as possible targets, with perhaps even more profitable payoffs for the hackers or rival competitors. They can further unleash adversarial machine learning attacks that can confuse the existing neural network
The team focused on common and simple binarized neural networks (an efcient network for IoT/edge devices with binary weights and activation values) that are adept at doing computations with less computing resources. They began by demonstrating how power consumption measurements can be exploited to reveal the secret weight and values that help determine a neural networks computations. Using random known inputs, for multiple numbers of time, the adversary computes the corresponding power activity on an intermediate estimate of power patterns linked with the secret weight values of BNN, in a highly-parallelized hardware implementation.
Then the team designed a countermeasure to secure the neural network against such an attack via masking (an algorithm-level defense that can produce resilient designs independent of the implementation technology). This is done by splitting intermediate computations into two randomized shares that are different each time the neural network runs the same intermediate computation. This prevents an attacker from using a single intermediate computation to analyze different power consumption patterns. While the process requires tuning for protecting specific machine learning models, they can be executed on any form of computer chip that runs on a neural network, viz., Field Programmable Gate Arrays (FPGA), and Application-specific Integrated Circuits (ASIC). Under this defense technique, a binarized neural network requires the hypothetical adversary to perform 100,000 sets of power consumption measurements instead of just 200.
However, there are certain main concerns involved in the masking technique. During initial masking, the neural networks performance dropped by 50 percent and needed nearly double the computing area on the FPGA chip. Second, the team expressed the possibility of attackers avoid the basic masking defense by analyzing multiple intermediate computations instead of a single computation, thus leading to a computational arms race where they are split into further shares. Adding more security to them can be time-consuming.
Despite this, we still need active countermeasures against DPA attacks. Machine Learning (ML) is a critical new target with several motivating scenarios to keep the internal ML model secret. While Aysu explains that research is far from done, his research is supported by both the U.S. National Science Foundation and the Semiconductor Research Corporations Global Research Collaboration. He anticipates receiving funding to continue this work for another five years and hopes to enlist more Ph.D. students interested in the effort.
Interest in hardware security is increasing because, at the end of the day, the hardware is the root of trust, Aysu says. And if the root of trust is gone, then all the security defenses at other abstraction levels will fail.
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New Research Claims to Have Found a Solution to Machine Learning Attacks - Analytics Insight
Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems – ScienceAlert
The chaos and uncertainty surrounding the coronavirus pandemic have claimed an unlikely victim: the machine learning systems that are programmed to make sense of our online behavior.
The algorithms that recommend products on Amazon, for instance, are struggling to interpret our new lifestyles, MIT Technology Review reports.
And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.
For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.
An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.
"The situation is so volatile," Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.
"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment."
While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.
"A pandemic like this is a perfect trigger to build better machine-learning models," Sharma said.
READ MORE: Our weird behavior during the pandemic is messing with AI models
This article was originally published by Futurism. Read the original article.
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Our Behaviour in This Pandemic Has Seriously Confused AI Machine Learning Systems - ScienceAlert
AI, machine learning, and blockchain are key for healthcare innovation – Health Europa
A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19.
Vural zdemir, MD, PhD, Editor-in-Chief ofOMICS, said: COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. This is very important.
Articles in the special issue include an interview on Responsible Innovation and Future Science in Australia byJustine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, Blockchain for Digital Health: Prospects and Challenges and Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.
In Blockchain for Digital Health: Prospects and Challenges the article explores the challenges that can be faced with the use of blockchain technology.
The article states: Although still faced with challenges, blockchain technology has an enormous potential to catalyse both technological and social innovation, turning the promise of digital health into a reality. By reshaping both the technological and social environment, the rise of blockchain in digital health can help reduce the disparity between the enormous technical progress and investments versus our currently inadequate understanding of the social dimensions of emerging technologies through commensurate investments in the latter knowledge domain.
A recent report by Market Study Report, Blockchain Technology in Healthcare Market, notes that blockchain technology in the healthcare market is anticipated to cross $1636.7m (1513.46m) by the year 2025.
Privacy is a major concern when it comes to storing and sharing health data, and with current healthcare data storage systems lacking top end security, blockchain can provide a solution to vulnerabilities such as hacking and data theft.
Blockchain technology in healthcare offers interoperability, which enables exchange of medical data securely among the different systems and personnel involved, offering a variety of benefits such as effective communication system, time reduction, and enhanced operational efficiency.
According to the report, the use of blockchain technology for claims adjudication and billing management application is predicted to register 66.5% growth by the year 2025, owing to several issues such as errors, duplications, and incorrect billing. All of these problems can be eliminated with blockchain.
Nearly 400 individuals including doctors were convicted for $1.3bn (1.2m) fraud in 2017 in the United States. The report highlights that the need to mitigate such frauds and fake drug supply will encourage the adoption of technology in this application segment.
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AI, machine learning, and blockchain are key for healthcare innovation - Health Europa
Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business…
Pune, May 18, 2020 (GLOBE NEWSWIRE) -- The global patent analytics market size is predicted to USD 1,668.4 million by 2027, exhibiting a CAGR of 12.4% during the forecast period. The increasing advancement and integration of machine learning, artificial intelligence, and the neural network by enterprises will have a positive impact on the market during the forecast period. Moreover, the growing needs of companies to protect intellectual assets will bolster healthy growth of the market in the forthcoming years, states Fortune Business Insights in a report, titled Patent Analytics Market Size, Share and Industry Analysis, By Component (Solutions and Services), By Services (Patent Landscapes/White Space Analysis, Patent Strategy and Management, Patent Valuation, Patent Support, Patent Analytics, and Others), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By Industry (IT and Telecommunications, Healthcare, Banking, Financial Services and Insurance (BFSI), Automotive, Media and Entertainment, Food and Beverages and, Others), and Regional Forecast, 2020-2027 the market size stood at USD 657.9 million in 2019. The rapid adoption of the Intellectual Property (IP) system to retain an innovation-based advantage in business will aid the expansion of the market.
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An Overview of the Impact of COVID-19 on this Market:
The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.
We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.
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Market Driver:
Integration of Artificial Intelligence to Improve Market Prospects
The implementation of artificial intelligence technology for analyzing patent data will support the expansion of the market. AI-based semantic search uses an artificial neural network to enhance patent discovery by improving accuracy and efficiency. For instance, in February 2018, PatSeer announced the unveiling of ReleSense, an AI-driven NLP engine. The engine utilizes 12 million+ semantic rules to gain from publically available patents, scientific journals, clinical trials, and associated data sources. ReleSense with its wide range of AI-driven capabilities offers search from classification, via APIs and predictive-analytics for apt IP solutions. The growing application of AI for domain-specific analytics will augur well for the market in the forthcoming years. Furthermore, the growing government initiatives to promote patent filing activities will boost the patent analytics market share during the forecast period. For instance, the Government of India introduced a new scheme named Innovative/ Creative India, to aware people of the patents and IP laws and support patent analytics. In addition, the growing preferment for language model and neural network intelligence for accurate, deep, and complete data insights will encourage the market.
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Regional Analysis:
Implementation of Advanced Technologies to Promote Growth in North America
The market in North America stood at USD 209.2 million and is expected to grow rapidly during the forecast period owing to the presence of major companies in the US such as IBM Corporation, Amazon.Com, Inc. The implementation of advanced technologies including IoT, big data, and artificial intelligence by major companies will aid growth in the region.
Considering this the U.S. is expected to showcase a higher growth in the patent filing. As per the World Intellectual Property, in 2018, the U.S. filed 230,085 patent applications across several domains. Asia Pacific is predicted to witness tremendous growth during the forecast period. The growth is attributed to China, which accounts for a major share in the global patent filings. According to WIPO, intellectual property (IP) office in China had accounted for 46.6% global share in patent registration, in 2018. The growing government initiatives concerning patents and IP laws in India will significantly enable speedy growth in Asia Pacific.
Key Development:
March 2018: Ipan GmbH announced its collaboration with Patentsight, Corsearch, and Uppdragshuset for the introduction of an open IP platform named IP-x-change platform. The platform enables prior art search, automatic data verification tools, smart docketing tools integrated in real-time to optimize IP management solution.
List of Key Companies Operating in the Patent Analytics Market are:
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Intellectual Property Software Market Size, Share and Global Trend By Deployment (On-premises & Cloud-based solutions), By Services (Development & Implementation Services, Consulting Services, Maintenance & Support Services), By Applications (Patent Management, Trademark Management and others), By Industry Vertical (Healthcare, Electronics and others) and Geography Forecast till 2025
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The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 – News Distinct
Global Machine Learning in Healthcare Cybersecurity Market Analysis 2020 with Top Companies, Production, Consumption, Price and Growth Rate
The Machine Learning in Healthcare Cybersecurity Market 2020 report includes the market strategy, market orientation, expert opinion and knowledgeable information. The Machine Learning in Healthcare Cybersecurity Industry Report is an in-depth study analyzing the current state of the Machine Learning in Healthcare Cybersecurity Market. It provides a brief overview of the market focusing on definitions, classifications, product specifications, manufacturing processes, cost structures, market segmentation, end-use applications and industry chain analysis. The study on Machine Learning in Healthcare Cybersecurity Market provides analysis of market covering the industry trends, recent developments in the market and competitive landscape.
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It takes into account the CAGR, value, volume, revenue, production, consumption, sales, manufacturing cost, prices, and other key factors related to the global Machine Learning in Healthcare Cybersecurity market. All findings and data on the global Machine Learning in Healthcare Cybersecurity market provided in the report are calculated, gathered, and verified using advanced and reliable primary and secondary research sources. The regional analysis offered in the report will help you to identify key opportunities of the global Machine Learning in Healthcare Cybersecurity market available in different regions and countries.
The Global Machine Learning in Healthcare Cybersecurity 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Machine Learning in Healthcare Cybersecurity analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.
Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins.
In addition to this, regional analysis is conducted to identify the leading region and calculate its share in the global Machine Learning in Healthcare Cybersecurity. Various factors positively impacting the growth of the Machine Learning in Healthcare Cybersecurity in the leading region are also discussed in the report. The global Machine Learning in Healthcare Cybersecurity is also segmented on the basis of types, end users, geography and other segments.
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Table of Content
1 Industry Overview of Machine Learning in Healthcare Cybersecurity
2 Manufacturing Cost Structure Analysis
3 Development and Manufacturing Plants Analysis of Machine Learning in Healthcare Cybersecurity
4 Key Figures of Major Manufacturers
5 Machine Learning in Healthcare Cybersecurity Regional Market Analysis
6 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Type)
7 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Application)
8 Machine Learning in Healthcare Cybersecurity Major Manufacturers Analysis
9 Development Trend of Analysis of Machine Learning in Healthcare Cybersecurity Market
10 Marketing Channel
11 Market Dynamics
12 Conclusion
13 Appendix
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The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 - News Distinct
Associations with No Place to Meet Are Turning to JUNO, A Live and On-Demand Digital Platform – AiThority
JUNO is pleased to announce the only all-in-one, live, andon-demand learning platformutilizing four human motivators to engage users and maximize the value of their experience. Gone are the days of multiple platforms, contracts, and vendors to secure ongoing engagement and learning with members. JUNO was built for a post-COVID-19reality. Greater strain and tighter budgets require a flexible solution to handle the New Normal and beyond.
Recommended AI News: Hawaii Signs Participating Addendum with DroneUp Providing Public Sector Agencies Access to Drone Services
JUNO facilitates full user engagement by offering these tools and features that meet the emerging user in their most desired expectations.
Connection: From hybrid to completely digital events, virtual meetings are the wave of the future. In fact, Microsoft teams alone have seen 2.7 billion meeting minutes in one day, a 200 percent increase. JUNO onboards users around interests, strengths, and desired improvement areas and allows machine learning triggers to recommend peer connections, mainstage, and breakout learning opportunities.
Gamification: 60% of all start-ups gamify their user experience because gamificationworks! By triggering real and powerful human emotions, users generate higher levels ofhappiness, intrigue, and excitement resulting in desires to engage further and stay involved longer.From profile building to polls, quizzes, and continued learning, JUNO ensures that every user action has value.
Recommended AI News: 3 Steps To Channel Customer Feedback Into Product Innovation
Business growth: So how will JUNO help your business grow? JUNO supports users and partners by facilitating business connections through live exhibit experiences, digital think-tank sessions, suggested collaboration partnerships, and skills-based visibility tools.
Ongoing learning: Live events must move past the transactional into the transformational. JUNO Creates EQ and IQ learning pathways to engage users on all levels. From certification and badging to goal setting and performance commitments, JUNO offers a diverse set of actions for users to personally develop.
In a time in which what got you here wont get you there, JUNO delivers the get you there solution, Former PCMA CEO, Deborah Sexton.
Recommended AI News: NVIDIA Accelerates Apache Spark, Worlds Leading Data Analytics Platform
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Associations with No Place to Meet Are Turning to JUNO, A Live and On-Demand Digital Platform - AiThority
Reality Of Metrics: Is Machine Learning Success Overhyped? – Analytics India Magazine
In one of the most revealing research papers written recent times, the researchers from Cornell Tech and Facebook AI quash the hype around the success of machine learning. They opine and even demonstrate that the trend appears to be overstated. In other words, the so-called cutting edge research or benchmark work perform similarly to one another even if they are a decade apart. In other words, the authors believe that metric learning algorithms have not made spectacular progress.
In this work, the authors try to demonstrate the significance of assessing algorithms more diligently and how few practices can help reflect ML success in reality.
Over the past decade, deep convolutional networks have made tremendous progress. Their application in computer vision is almost everywhere; from classification to segmentation to object detection and even generative models. But is the metric evaluation carried out to track this progress has been leakproof? Are the techniques employed werent affected by the improvement in deep learning methods?
The goal of metric learning is to map data to an embedding space, where similar data are close together, and the rest are far apart. So, the authors begin with the notion that the deep networks have had a similar effect on metric learning. And, the combination of the two is known as deep metric learning.
The authors then examined flaws in the current research papers, including the problem of unfair comparisons and the weaknesses of commonly used accuracy metrics. They then propose a training and evaluation protocol that addresses these flaws and then run experiments on a variety of loss functions.
For instance, one benchmark paper in 2017, wrote the authors, used ResNet50, and then claimed huge performance gains. But the competing methods used GoogleNet, which has significantly lower initial accuracies. Therefore, the authors conclude that much of the performance gain likely came from the choice of network architecture, and not their proposed method. Practices such as these can put ML on headlines, but when we look at how much of these state-of-the-art models are really deployed, the reality is not that impressive.
The authors underline the importance of keeping the parameters constant if one has to prove that a certain new algorithm outperforms its contemporaries.
To carry out the evaluations, the authors introduce settings that cover the following:
As shown in the above plot, the trends, in reality, arent that far from the previous related works and this indicates that those who claim a dramatic improvement might not have been fair in their evaluation.
If a paper attempts to explain the performance gains of its proposed method, and it turns out that those performance gains are non-existent, then their explanation must be invalid as well.
The results show that when hyperparameters are properly tuned via cross-validation, most methods perform similarly to one another. This work, believe the authors, will lead to more investigation into the relationship between hyperparameters and datasets, and the factors related to particular dataset/architecture combinations.
According to the authors, this work exposes the following:
The authors conclude that if proper machine learning practices are followed, then the results of metric learning papers will better reflect reality, and can lead to better works in most impactful domains like self-supervised learning.
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Reality Of Metrics: Is Machine Learning Success Overhyped? - Analytics India Magazine