Category Archives: Machine Learning

Most of us have probably been including HML estimations in some designs without recognizing it. We might have used methodologies that are a blend of existing ones or got together with strategies that are imported from various fields. We try to a great extent to apply data change methods like principles component analysis (PCA) or simple linear correlation analysis to our data preceding passing them to a ML methodology. A couple of experts use extraordinary estimations to mechanize the headway of the limits of existing ML methodologies. HML estimations rely upon an ML plan that is hard and not exactly equivalent to the standard work process. We seem to have misjudged the ML estimations as we fundamentally use them ready to move, for the most part dismissing the nuances of how things fit together.

HML is a progress of the ML work process that perfectly unites different computations, processes, or procedures from equivalent or different spaces of data or areas of usage fully intended to enhance each other. As no single cap fits all heads, no single ML procedure is appropriate for all issues. A couple of strategies that are extraordinary in managing boisterous data anyway may not be prepared for dealing with high-layered input space. Some others could scale pretty well on high-layered input space anyway may not be good for managing sparse data. These conditions are a fair motivation to apply HML to enhance the contender procedures and use one to overcome the deficiency of the others.

The open doors for the hybridization of standard ML methodologies are ceaseless, and this ought to be workable for every single one to collect new combination models in different ways.

This kind of HML consistently consolidates the architecture of at least two customary algorithms, entirely or mostly, in an integral way to develop a more-hearty independent algorithm. The most ordinarily utilized model is Adaptive Neuro-Fluffy Interference System (ANFIS). ANFIS has been utilized for some time and is generally considered an independent customary ML strategy. It really is a blend of the standards of fluffy rationale and ANN. The engineering of ANFIS is made out of five layers. The initial three are taken from fuzzy logic, while the other two are from ANN.

This kind of cross hybrid advancement consistently joins information control cycles or systems with customary ML techniques with the goal of supplementing the last option with the result of the previous. The accompanying models are legitimate opportunities for this kind of crossover learning technique:

If an (FR) calculation is utilized to rank and preselect ideal highlights prior to applying the (SVM) calculation to the information, this can be called an FR-SVM hybrid model.

Assuming a PCA module is utilized to separate a submatrix of information that is adequate to make sense of the first information prior to applying a brain network to the information, we can call it a PCA-ANN hybrid model.

If an SVD calculation is utilized to lessen the dimensionality of an informational collection prior to applying an ELM model, then, at that point, we can call it an SVD-ELM hybrid model.

Hybrid techniques that we depend on include determination, a sort of information control process that looks to supplement the implicit model choice course of customary ML strategies, which have become normal. It is realized that every ML algorithm has an approach to choosing the best model in light of an ideal arrangement of info highlights.

It is realized that each conventional ML technique utilizes a specific improvement or search algorithm, for example, gradient descent or grid search to decide its ideal tuning boundaries. This sort of crossover learning tries to supplement or supplant the underlying boundary improvement strategy by utilizing specific progressed techniques that depend on developmental calculations. The potential outcomes are additionally huge here. Instances of such conceivable outcomes are:

1. Assuming the particular swam advancement (PSO) algorithm is utilized to upgrade the preparation boundaries of an ANN model, the last option turns into a PSO-ANN hybrid model.

2. At the point when generic calculation (GA) is utilized to streamline the preparation boundaries of the ANFIS technique, the last option turns into a GANFIS hybrid model.

3. The equivalent goes with other developmental streamlining calculations like Honey bee, Subterranean insect, Bat, and Fish State that are joined with customary ML techniques to shape their relating half, breed models.

An ordinary illustration of the component determination-based HML is the assessment of a specific supply property, for example, porosity utilizing coordinated rock physical science, geographical, drilling, and petrophysical informational collections. There could be in excess of 30 info highlights from the consolidated informational indexes. It will be a decent learning exercise and a commitment to the assortment of information to deliver a positioning and decide the general significance of the elements. Utilizing the main 5 or 10, for instance, may deliver comparative outcomes and subsequently decrease the computational intricacy of the proposed model. It might likewise help space specialists to fewer features in on the fewer highlights rather than the full arrangement of logs, most of which might be excess.

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What is Hybrid Machine Learning and How to Use it? - Analytics Insight

This Market place offers explanatory expertise available on the market parts like dominating players, manufacturing, sales, intake, import and export, and the simplest improvement in the corporation size, deployment kind, inside, segmentation comprised at some point of this analysis, additionally foremost the players have used various techniques such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions and others, to boom their footprints on this marketplace in order to sustain in long term, moreover to the existing the clean perspective of Global This Market.

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Machine Learning Chip Market is expected to reach USD 72.45 billion by 2027 witnessing market growth with the rate of 40.60% in the forecast period of 2020 to 2027.

Introduction of quantum computing, rising applications of machine learning in various industries, adoption of artificial intelligence across the globe, are some of the factors that will likely to enhance the growth of the machine learning chip market in the forecast period of 2020-2027. On the other hand, growing smart cities and smart homes, adoption of internet of things worldwide, technological advancement which will further boost various opportunities that will lead to the growth of the machine learning chip market in the above mentioned forecast period.

Lack of skilled workforce along with phobia related to artificial intelligence are acting as market restraints for machine learning chip in the above mentioned forecaster period.

We provide a detailed analysis of key players operating in the Machine Learning Chip Market:

North America will dominate the machine learning chip market due to the prevalence of majority of manufacturers while Europe will expect to grow in the forecast period of 2020-2027 due to the adoption of advanced technology.

Market Segments Covered:

By Chip Type

Technology

Industry Vertical

Machine Learning Chip Market Country Level Analysis

Machine learning chip market is analysed and market size, volume information is provided by country, chip type, technology and industry vertical as referenced above.

The countries covered in the machine learning chip market report are U.S., Canada and Mexico in North America, Brazil, Argentina and Rest of South America as part of South America, Germany, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe in Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA).

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Rapid Business Growth Factors

In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor thats helping the market grow faster than usual is the tough competition.

Competitive Landscape and Machine Learning Chip Market Share Analysis

Machine learning chip market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, regional presence, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies focus related to machine learning chip market.

Table of Content:

Part 01: Executive Summary

Part 02: Scope of the Report

Part 03: Research Methodology

Part 04: Machine Learning Chip Market Landscape

Part 05: Market Sizing

More.TOC.. ..Continue

Based on geography, the global Machine Learning Chip market report covers data points for 28 countries across multiple geographies namely

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Key questions answered in this report

Get in-depth details about factors influencing the market shares of the Americas, APAC, and EMEA?

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Machine Learning Chip Market Size by Product Type, By Application, By Competitive Landscape, Trends and Forecast by 2029 themobility.club -...

In recent years, we have seen accelerated OLED materials development, aided by software tools based on machine learning and Artificial Intelligence. This is an excellent development which contributes to the continued improvement in OLED efficiency, brightness and lifetime.

Kyulux's Kyumatic AI material discover system

The promise of these new technologies is the ability to screen millions of possible molecules and systems quickly and efficiently. Materials scientists can then take the most promising candidates and perform real synthesis and experiments to confirm the operation in actual OLED devices.

The main drive behind the use of AI systems and mass simulations is to save the time that actual synthesis and testing of a single material can take - sometimes even months to complete the whole cycle. It is simply not viable to perform these experiments on a mass scale, even for large materials developers, let alone early stage startups.

In recent years we have seen several companies announcing that they have adopted such materials screening approaches. Cynora, for example, has an AI platform it calls GEM (Generative Exploration Model) which its materials experts use to develop new materials. Another company is US-based Kebotix, which has developed an AI-based molecular screening technology to identify novel blue OLED emitters, and it is now starting to test new emitters.

The first company to apply such an AI platform successfully was, to our knowledge, Japan-based Kyulux. Shortly after its establishment in 2015, the company licensed Harvard University's machine learning "Molecular Space Shuttle" system. The system has been assisting Kyulux's researchers to dramatically speed up their materials discovery process. The company reports that its development cycle has been reduced from many months to only 2 months, with higher process efficiencies as well.

Since 2016, Kyulux has been improving its AI platform, which is now called Kyumatic. Today, Kyumatic is a fully integrated materials informatics system that consists of a cloud-based quantum chemical calculation system, an AI-based prediction system, a device simulation system, and a data management system which includes experimental measurements and intellectual properties.

Kyulux is advancing fast with its TADF/HF material systems, and in October 2021 it announced that its green emitter system is getting close to commercialization and the company is now working closely with OLED makers, preparing for early adoption.

Read the original here:
How machine learning and AI help find next-generation OLED materials - OLED-Info

AI, Artificial Intelligence concept,3d rendering,conceptual image.

This week IBM announced its latest Z-series mainframe and MLCommons released its latest benchmark series. The two announcements had something in common Machine Learning (ML) acceleration which is becoming pervasive everywhere from financial fraud detection in mainframes to detecting wake words in home appliances.

While these two announcements were not directly related, but they are part of a trend, showing how pervasive ML has become.

MLCommons Brings Standards to ML Benchmarking

ML benchmarking is important because we often hear about ML performance in terms of TOPS trillions of operations per second. Like MIPS (Millions of Instructions per Second or Meaningless Indication of Processor Speed depending on your perspective), TOPS is a theoretical number calculated from the architecture, not a measured rating based on running workloads. As such, TOPS can be a deceiving number because it does not include the impact of the software stack., Software is the most critical aspect of implementing ML and the efficiency varies widely, which Nvidia clearly demonstrated by improving the performance of its A100 platform by 50% in MLCommons benchmarks over the years.

The industry organization MLCommons was created by a consortium of companies to build a standardized set of benchmarks along with a standardized test methodology that allows different machine learning systems to be compared. The MLPerf benchmark suites from MLCommons include different benchmarks that cover many popular ML workloads and scenarios. The MLPerf benchmarks addresses everything from the tiny microcontrollers used in consumer and IoT devices, to mobile devices like smartphones and PCs, to edge servers, to data center-class server configuration. Supporters of MLCommons include Amazon, Arm, Baidu, Dell Technologies, Facebook, Google, Harvard, Intel, Lenovo, Microsoft, Nvidia, Stanford and the University of Toronto.

MLCommons releases benchmark results in batches and has different publishing schedules for inference and for training. The latest announcement was for version 2.0 of the MLPerf Inference suite for data center and edge servers, version 2.0 for MLPerf Mobile, and version 0.7 for MLPerf Tiny for IoT devices.

To date, the company that has had the most consistent set of submissions, producing results every iteration, in every benchmark test, and by multiple partners, has been Nvidia. Nvidia and its partners appear to have invested enormous resources in running and publishing every relevant MLCommons benchmark. No other vendor can match that claim. The recent batch of inference benchmark submissions include Nvidia Jetson Orin SoCs for edge servers and the Ampere-based A100 GPUs for data centers. Nvidias Hopper H100 data center GPU, which was announced at Spring 2022 GTC, arrived too late to be included in the latest MLCommons announcement, but we fully expect to see Nvidia H100 results in the next round.

Recently, Qualcomm and its partners have been posting more data center MLPerf benchmarks for the companys Cloud AI 100 platform and more mobile MLPerf benchmarks for Snapdragon processors. Qualcomms latest silicon has proved to be very power efficient in data center ML tests, which may give it an edge on power-constrained edge server applications.

Many of the submitters are system vendors using processors and accelerators from silicon vendors like AMD, Andes, Ampere, Intel, Nvidia, Qualcomm, and Samsung. But many of the AI startups have been absent. As one consulting company, Krai, put it: Potential submitters, especially ML hardware startups, are understandably wary of committing precious engineering resources to optimizing industry benchmarks instead of actual customer workloads. But then Krai countered their own objection with MLPerf is the Olympics of ML optimization and benchmarking. Still, many startups have not invested in producing MLCommons results for various reasons and that is disappointing. Theres also not enough FPGA vendors participating in this round.

The MLPerf Tiny benchmark is designed for very low power applications such as keyword spotting, visual wake words, image classification, and anomaly detection. In this case we see results from a mix of small companies like Andes, Plumeria, and Syntiant, as well as established companies like Alibaba, Renesas, Silicon Labs, and STMicroeletronics.

IBM z16 Mainframe

IBM Adds AI Acceleration Into Every Transaction

While IBM didnt participate in MLCommons benchmarks, the company takes ML seriously. With its latest Z-Series mainframe computer, the z16, IBM has added accelerators for ML inference and quantum-safe secure boot and cryptography. But mainframe systems have different customer requirements. With roughly 70% of banking transactions (on a value basis) running on IBM mainframes, the company is anticipating the needs of financial institutes for extreme reliable and transaction processing protection. In addition, by adding ML acceleration into its CPU, IBM can offer per-transaction ML intelligence to help detect fraudulent transactions.

In an article I wrote in 2018, I said: In fact, the future hybrid cloud compute model will likely include classic computing, AI processing, and quantum computing. When it comes to understanding all three of those technologies, few companies can match IBMs level of commitment and expertise. And the latest developments in IBMs quantum computing roadmap and the ML acceleration in the z16, show IBM is a leader in both.

Summary

Machine Learning is important from tiny devices up to mainframe computers. Accelerating this workload can be done on CPUs, GPUs, FPGAs, ASICs, and even MCUs and is now a part of all computing going forward. These are two examples of how ML is changing and improving over time.

Tirias Research tracks and consults for companies throughout the electronics ecosystem from semiconductors to systems and sensors to the cloud. Members of the Tirias Research team have consulted for IBM, Nvidia, Qualcomm, and other companies throughout the AI ecosystems.

Read the rest here:
IBM And MLCommons Show How Pervasive Machine Learning Has Become - Forbes

A team of University of Iowa researchers received $800,000 from Amazon and the National Science Foundation to limit the discriminatory effects of machine learning algorithms.

Larry Phan

University of Iowa researcher Tianbao Yang seats at his desk where he works on AI research on Friday, Aril 8, 2022.

University of Iowa researchers are examining discriminative qualities of artificial intelligence and machine learning models, which are likely to be unfair against ones race, gender, or other characteristics based on patterns of data.

A University of Iowa research team received an $800,000 grant funded jointly by the National Science Foundation and Amazon to decrease the possibility of discrimination through machine learning algorithms.

The three-year grant is split between the UI and Louisiana State University.

According to Microsoft, machine learning models are files trained to recognize specific types of patterns.

Qihang Lin, a UI associate professor in the department of business analytics and grant co-investigator, said his team wants to make machine learning models fairer without sacrificing an algorithms accuracy.

RELATED: UI professor uses machine learning to indicate a body shape-income relationship

People nowadays in [the] academic field ladder, if you want to enforce fairness in your machine learning outcome, you have to sacrifice the accuracy, Lin said. We somehow agree with that, but we want to come up with an approach that [does] trade-off more efficiently.

Lin said discrimination created by machine learning algorithms is seen disproportionately predicting rates of recidivism a convicted criminals tendency to re-offend for different social groups.

For instance, lets say we look at in U.S. courts, they use a software to predict what is the chance of recidivism of a convicted criminal and they realize that that software, that tool they use, is biased because they predicted a higher risk of recidivism of African Americans compared to their actual risk of recidivism, Lin said.

Tianbao Yang, a UI associate professor of computer science and grant principal investigator, said the team proposed a collaboration with Netflix to encourage fairness in the process of recommending shows or films to users.

Here we also want to be fair in terms of, for example, users gender, users race, we want to be fair, Yang said. Were also collaborating with them to use our developed solutions.

Another instance of machine learning algorithm unfairness comes in determining what neighborhoods to allocate medical resources, Lin said.

RELATED: UI College of Engineering uses artificial-intelligence to solve problems across campus

In this process, Lin said the health of a neighborhood is determined by examining household spending on medical expenses. Healthy neighborhoods are allocated more resources, creating a bias against lower income neighborhoods that may spend less on medical resources, Lin said.

Theres a bad cycle that kind of reinforces the knowledge the machines mistakenly have about the relationship between the income, medical expense in the house, and the health, Lin said.

Yao Yao, UI third-year doctoral candidate in the department of mathematics, is conducting various experiments for the research team.

She said the importance of the groups focus is that they are researching more than simply reducing errors in machine learning algorithm predictions.

Previously, people only focus on how to minimize the error but most time we know that the machine learning, the AI will cause some discrimination, Yao said. So, its very important because we focus on fairness.

Read the rest here:
Amazon awards grant to UI researchers to decrease discrimination in AI algorithms - UI The Daily Iowan

Swiss Re, in collaboration with MachineHack, successfully completed the Machine Learning Hackathon held from March 11th to 28th for data scientists and ML professionals to predict accident risk scores for unique postcodes. The end goal? To build a machine learning model to improve auto insurance pricing.

The hackathon saw over 1100+ registrations and 300+ participants from interested candidates. Out of those, the top five were asked to participate in a solution showcase held on the 6th of April. The top five entries were judged by Amit Kalra, Managing Director, Swiss Re and Jerry Gupta, Senior Vice President, Swiss Re who engaged with the top participants, understood their solutions and presentations and provided their comments and scores. From that emerged the top three winners!

Lets take a look at the winners who impressed the judges with their analytics skills and took home highly coveted cash prizes and goodies.

Pednekar comes with over 19 years of work experience in IT, project management, software development, application support, software system design, and requirement study. He is passionate about new technologies, especially data science, AI and machine learning.

My expertise lies in creating data visualisations to tell my datas story & using feature engineering to add new features to give a human touch in the world of machine learning algorithms, said Pednekar.

Pednekars approach consisted of seven steps:

For EDA, Pednekar has analysed the dataset to find out the relationship between:

Image: Rahul Pednekar

Image: Rahul Pednekar

Here, Pednekar merged Population & Road Network datasets with train using left join. He created Latitude and Longitude columns by extracting data from the WKT columns in Roads_network.

He proceeded to

And added new features:

Pednekar completed the following steps:

Image: Rahul Pednekar

Image: Rahul Pednekar

Pednekar has thoroughly enjoyed participating in this hackathon. He said, MachineHack team and the platform is amazing, and I would like to highly recommend the same to all data science practitioners. I would like to thank Machinehack for providing me with the opportunity to participate in various data science problem-solving challenges.

Check the code here.

Yadavs data science journey started a couple of years back, and since then, he has been an active participant in hackathons conducted on different platforms. Learning from fellow competitors and absorbing their ideas is the best part of any data science competition as it just widens the thinking scope for yourself and makes you better after each and every competition, says Yadav.

MachineHack competitions are unique and have a different business case in each of their hackathons. It gives a field wherein we can practice and learn new skills by applying them to a particular domain case. It builds confidence as to what would work and what would not in certain cases. I appreciate the hard work the team is putting in to host such competitions, adds Yadav.

Check the code here.

Rank 03: Prudhvi Badri

Badri entered the data science field while pursuing a masters in computer science at Utah State University in 2014 and had taken classes related to statistics, Python programming and AI, and wrote a research paper to predict malicious users in online social networks.

After my education, I started to work as a data scientist for a fintech startup company and built models to predict loan default risk for customers. I am currently working as a senior data scientist for a website security company. In my role, I focus on building ML models to predict malicious internet traffic and block attacks on websites. I also mentor data scientists and help them build cool projects in this field, said Badri.

Badri mainly focused on feature engineering to solve this problem. He created aggregated features such as min, max, median, sum, etc., by grouping a few categorical columns such as Day_of_Week, Road_Type, etc. He built features from population data such as sex_ratio, male_ratio, female_ratio, etc.

He adds, I have not used the roads dataset that has been provided as supplemental data. I created a total of 241 features and used ten-fold cross-validation to validate the model. Finally, for modelling, I used a weighted ensemble model of LightGBM and XGBoost.

Badri has been a member of MachineHack since 2020. I am excited to participate in the competitions as they are unique and always help me learn about a new domain and let me try new approaches. I appreciate the transparency of the platform sharing the approaches of the top participants once the hackathon is finished. I learned a lot of new techniques and approaches from other members. I look forward to participating in more hackathons in the future on the MachineHack platform and encourage my friends and colleagues to participate too, concluded Badri.

Check the code here.

The Swiss Re Machine Learning Hackathon, in collaboration with MachineHack, ended with a bang, with participants presenting out-of-the-box solutions to solve the problem in front of them. Such a high display of skills made the hackathon intensely competitive and fun and surely made the challenge a huge success!

Originally posted here:
Meet the winners of the Machine Learning Hackathon by Swiss Re & MachineHack - Analytics India Magazine