Category Archives: Machine Learning
Non-Invasive Medical Diagnostics: Know Labs’ Partnership With … – Benzinga
Machine learning has revolutionized the field of biomedical research, enabling faster and more accurate development of algorithms that can improve healthcare outcomes. Biomedical researchers are using machine learning tools and algorithms toanalyzevast and complex health data, and quickly identify patterns and relationships that were previously difficult to discern.
Know Labs, an emerging developer of non-invasive medical diagnostic technology is readying a breakthrough for non-invasive glucose monitoring, which has the potential to positively impact the lives of millions. One of the key elements behind this tech is the ability to process large amounts of novel data generated by their Bio-RFID radio frequency sensor, using machine learning algorithms from Edge Impulse.
One significant way in which machine learning is improving algorithm development in the biomedical space is by developing more accurate predictions and insights. Machine learning algorithms use advanced statistical techniques to identify correlations and relationships that may not be apparent to human researchers.
Machine learning algorithms can analyze a patient's entire medical history and provide predictions about their potential health outcomes, which can help medical professionals intervene earlier to prevent diseases from progressing. Machine learning algorithms can also be used to develop more personalized treatments.
Historically, this process was time-consuming and prone to error due to the difficulty in managing large datasets. Machine learning algorithms, on the other hand, can quickly and easily process vast amounts of data and identify patterns without human intervention, resulting in decreased manual workload and reduced error.
As the technology and use cases of machine learning continue to grow, it is evident that it can help realize a future of improved health care by unlocking the potential of large biomedical and patient datasets.
Already, early uses of machine learning in diagnosis and treatment have shownpromiseto diagnose breast cancer from x-rays, discover new antibiotics, predict the onset of gestational diabetes from electronic health records, and identify clusters of patients that share a molecular signature of treatment response.
Withreportsindicating that 400,000 hospitalized patients experience some type of preventable medical error each year, machine learning can help predict and diagnose diseases at a faster rate than most medical professionals,savingapproximately $20 billion annually.
Companies like Linus Health, Viz.ai, PathAI, and Regard are showing artificial intelligence (AI) and machine learning (ML)s ability to reduce errors and save lives.
Advancements in patient care including remote physiologic monitoring and care delivery highlights the growing demand for the use of technology to enhance non-invasive means of medical diagnosis.
One significant area this could benefit is monitoring blood glucose non-invasively withoutpricking the fingerfor blood, important for patients to effectively manage their type 1 and 2 diabetes. While glucose biosensors have existed for over half a century, they can be classified as two groups: electrochemical sensors relying on direct interaction with an analyte and electromagnetic sensors that leverage antennas and/or resonators to detect changes in the dielectric properties of the blood.
Using smart devices essentially involves shining light into the body using optical sensors and quantifying how the light reflects back to measure a particular metric. Already there are smartwatches, fitness trackers, and smart rings from companies like Apple Inc. AAPL, Samsung Electronics Co Ltd. (KRX: 005930) and Google (Alphabet Inc. GOOGL ) that measure heart rate, blood oxygen levels, and a host of other metrics.
But applying this tech to measure blood glucose is much more complicated, and the data may not be accurate. Know Labs seems to be on a path to solving this challenge.
The Seattle-based companyhaspartneredwithEdge Impulse, providers of a machine learning development toolkit, to interpret robust data from its proprietaryBio-RFIDtechnology. The algorithm refinement process that Edge Impulse provides is a critical step towards interpreting the existing large and novel datasets, which will ultimately support large-scale clinical research.
The Bio-RFID technology is a non-invasive medical diagnostic technology that uses a novel radio frequency sensor that can safely see through the full cellular stack to accurately identify a unique molecular signature of a wide range of organic and inorganic materials, molecules, and compositions of matter.
Microwave and Radio Frequency sensors operate over a broader frequency range, and with this comes an extremely broad dataset that requires sophisticated algorithm development. Working with Know Labs, Edge Impulse uses its machine learning tools to train a Neural Network model to interpret this data and make blood glucose level predictions using a popular CGM proxy for blood glucose. Edge Impulse provides a user-friendly approach to machine learning that allows product developers and researchers to optimize the performance of sensory data analysis. This technology is based onAutoML and TinyMLto make AI more accessible, enabling quick and efficient machine learning modeling.
The partnership between Know Labs, a company committed to making a difference in people's lives by developing convenient and affordable non-invasive medical diagnostics solutions, and Edge Impulse, makers of tools that enable the creation and deployment of advanced AI algorithms, is a prime example for how responsible machine learning applications could significantly improve and change healthcare diagnostics.
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Non-Invasive Medical Diagnostics: Know Labs' Partnership With ... - Benzinga
Middle East and Africa Machine Learning Market Spurs as Demand … – Digital Journal
PRESS RELEASE
Published May 12, 2023
The recent analysis by Quadintel on the Middle East and Africa Machine Learning Market Report 2023 revolves around various aspects of the market, including characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends, strategies, etc. It also includes COVID-19 Outbreak Impact, accompanied by traces of the historic events. The study highlights the list of projected opportunities, sales and revenue on the basis of region and segments. Apart from that, it also documents other topics such as manufacturing cost analysis, Industrial Chain, etc. For better demonstration, it throws light on the precisely obtained data with the thoroughly crafted graphs, tables, Bar & Pie Charts, etc.
Get a report on Middle East and Africa Machine Learning Market (Including Full TOC, 100+ Tables & Figures, and charts). Covers Precise Information on Pre & Post COVID-19 Market Outbreak by Region
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The market for machine learning in the Middle East and Africa is rapidly growing and expected to reach a value of USD 0.50 billion by 2023, with a compound annual growth rate of 29.1% from 2018-2023.Machine learning has become increasingly important due to the availability of data and the need to process it for meaningful insights.The market can be segmented based on components, service, organization size, and application.
The use of machine learning in healthcare has become popular in the Middle East as hospitals are using this technology to make precise diagnoses, prevent diseases, and provide treatment to individuals. The adoption of machine learning in retail and healthcare industries to provide better consumer experiences and increase automation is driving the market growth.
The slow adoption of machine learning in Africa can be attributed to the lack of adequate infrastructure and consumer spending power. Also, the unavailability of skilled cohorts with adequate machine learning skills is a significant barrier to further development in the market.
The key players in the market are Google Inc., Microsoft, IBM Watson, Amazon, and Intel. These companies are investing heavily in the development of machine learning technologies and are driving the growth of the market.
The report provides an overview of the market, market drivers, and challenges, historical, current and forecasted market size data, analysis of the competitive landscape, and profiles of major competitors. The report also provides insights into the value chain, new technology innovations, government guidelines, export and import analysis, and growth strategies taken by major companies in the market.
The market for machine learning in the Middle East and Africa is rapidly growing due to increased data availability, the need for meaningful insights, and the adoption of machine learning in various industries. The key players in the market are investing heavily in developing machine learning technologies, and the market is expected to continue growing in the future.
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Our tailormade report can help companies and investors make efficient strategic moves by exploring the crucial information on market size, business trends, industry structure, market share, and market predictions.
Apart from the general projections, our report outstands as it includes thoroughly studied variables, such as the COVID-19 containment status, the recovery of the end-use market, and the recovery timeline for 2020/ 2021
Analysis on COVID-19 Outbreak Impact Include:In light of COVID-19, the report includes a range of factors that impacted the market. It also discusses the trends. Based on the upstream and downstream markets, the report precisely covers all factors, including an analysis of the supply chain, consumer behavior, demand, etc. Our report also describes how vigorously COVID-19 has affected diverse regions and significant nations.
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Each report by the Quadintel contains more than 100+ pages, specifically crafted with precise tables, charts, and engaging narrative: The tailor-made reports deliver vast information on the market with high accuracy. The report encompasses: Micro and macro analysis, Competitive landscape, Regional dynamics, Operational landscape, Legal Set-up, and Regulatory frameworks, Market Sizing and Structuring, Profitability and Cost analysis, Demographic profiling and Addressable market, Existing marketing strategies in the market, Segmentation analysis of Market, Best practice, GAP analysis, Leading market players, Benchmarking, Future market trends and opportunities.
Geographical Breakdown:The regional section of the report analyses the market on the basis of region and national breakdowns, which includes size estimations, and accurate data on previous and future growth. It also mentions the effects and the estimated course of Covid-19 recovery for all geographical areas. The report gives the outlook of the emerging market trends and the factors driving the growth of the dominating region to give readers an outlook of prevailing trends and help in decision making.
Nations:Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, UAE, UK, USA, Venezuela, Vietnam
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Thoroughly Described Qualitative COVID 19 Outbreak Impact Include Identification and Investigation on:Market Structure, Growth Drivers, Restraints and Challenges, Emerging Product Trends & Market Opportunities, Porters Fiver Forces. The report also inspects the financial standing of the leading companies, which includes gross profit, revenue generation, sales volume, sales revenue, manufacturing cost, individual growth rate, and other financial ratios. The report basically gives information about the Market trends, growth factors, limitations, opportunities, challenges, future forecasts, and information on the prominent and other key market players.
Key questions answered:This study documents the affect ofCOVID 19 Outbreak: Our professionally crafted report contains precise responses and pinpoints the excellent opportunities for investors to make new investments. It also suggests superior market plan trajectories along with a comprehensive analysis of current market infrastructures, prevailing challenges, opportunities, etc. To help companies design their superior strategies, this report mentions information about end-consumer target groups and their potential operational volumes, along with the potential regions and segments to target and the benefits and limitations of contributing to the market. Any markets robust growth is derived by its driving forces, challenges, key suppliers, key industry trends, etc., which is thoroughly covered in our report. Apart from that, the accuracy of the data can be specified by the effective SWOT analysis incorporated in the study.
A section of the report is dedicated to the details related to import and export, key players, production, and revenue, on the basis of the regional markets. The report is wrapped with information about key manufacturers, key market segments, the scope of products, years considered, and study objectives.
It also guides readers through segmentation analysis based on product type, application, end-users, etc. Apart from that, the study encompasses a SWOT analysis of each player along with their product offerings, production, value, capacity, etc.
List of Factors Covered in the Report are:Major Strategic Developments: The report abides by quality and quantity. It covers the major strategic market developments, including R&D, M&A, agreements, new products launch, collaborations, partnerships, joint ventures, and geographical expansion, accompanied by a list of the prominent industry players thriving in the market on a national and international level.
Key Market Features:Major subjects like revenue, capacity, price, rate, production rate, gross production, capacity utilization, consumption, cost, CAGR, import/export, supply/demand, market share, and gross margin are all assessed in the research and mentioned in the study. It also documents a thorough analysis of the most important market factors and their most recent developments, combined with the pertinent market segments and sub-segments.
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List of Highlights & ApproachThe report is made using a variety of efficient analytical methodologies that offers readers an in-depth research and evaluation on the leading market players and comprehensive insight on what place they are holding within the industry. Analytical techniques, such as Porters five forces analysis, feasibility studies, SWOT analyses, and ROI analyses, are put to use to examine the development of the major market players.
Points Covered in Middle East and Africa Machine Learning Market Report:
Middle East and Africa Machine Learning Market Research Report
Section 1: Middle East and Africa Machine Learning Market Industry Overview
Section 2: Economic Impact on Middle East and Africa Machine Learning
Section 3: Market Competition by Industry Producers
Section 4: Productions, Revenue (Value), according to regions
Section 5: Supplies (Production), Consumption, Export, Import, geographically
Section 6: Productions, Revenue (Value), Price Trend, Product Type
Section 7: Market Analysis, on the basis of Application
Section 8: Middle East and Africa Machine Learning Market Pricing Analysis
Section 9: Market Chain, Sourcing Strategy, and Downstream Buyers
Section 10: Strategies and key policies by Distributors/Suppliers/Traders
Section 11: Key Marketing Strategy Analysis, by Market Vendors
Section 12: Market Effect Factors Analysis
Section 13: Middle East and Africa Machine Learning Market Forecast
..and view more in complete table of Contents
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Middle East and Africa Machine Learning Market Spurs as Demand ... - Digital Journal
Multidimensional Mass Spectrometry and Machine Learning: A … – Technology Networks
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We developed and demonstrated a new metabolomics workflow for studying engineered microbes in synthetic biology applications. Our workflow combines state-of-the-art analytical instrumentation that generates information-rich data with a novel machine learning (ML)-based algorithm tailored to process it.
In our roles as Pacific Northwest National Laboratory (PNNL) scientists, we led this multi-institutional study, which was published in Nature Communications.
Metabolites are small molecules produced by large networks of cellular processes and biochemical reactions in living systems. The sheer diversity of metabolite classes and structures constitutes a significant analytical challenge in terms of detection and annotation in complex samples.
Analytical instrumentation able to analyze hundreds of samples in ever faster and more accurate ways is critical in various metabolomics applications, including the development of microorganisms that can produce desirable fuels and chemicals in a sustainable way.
Multidimensional measurements using liquid chromatography (LC), ion mobility and data-independent acquisition mass spectrometry (MS) improve metabolite detection by linking the separations in a single analytical platform. The potential for metabolomics has been previously demonstrated, but this kind of multidimensional information-rich data is complex and cannot be processed with traditional tools. Therefore, algorithms and software tools capable of processing it to extract accurate metabolite information are needed.
We optimized a combination of sophisticated instruments for fast analyses and generated multidimensional data, rich in information that can be used to tease apart complex metabolomes.
For the computational method, Dr. Bilbao created a new algorithm, called PeakDecoder, to enable interpretation of the multidimensional data and ultimately identify individual molecules in complex mixtures. Our algorithm learns to distinguish true co-elution and co-mobility directly from the raw data of the studied samples and calculates error rates for metabolite identification. To train the ML model, it proposes a novel method to generate training examples, similar to the target-decoy strategy commonly used in proteomics. Once the model is trained, it can be used to score metabolites of interest from a library with an associated false discovery rate. And contrary to existing methods, it can also be used with libraries of small size.
The key outcomes of the paper were:
The method takes a third of the sample analysis time of previous conventional approaches by using optimized LC conditions. PeakDecoder enables accurate profiling in multidimensional MS measurements for large scale studies.
We used the workflow to study metabolites of various strains of microorganisms engineered by the Agile BioFoundry to make various bioproducts, such as polymers and diesel fuel precursors. We were able to interpret 2,683 metabolite features across 116 microbial samples.
However, it should be noted that the current algorithm is not fully automated due to software dependencies and requires a metabolite library acquired with compatible analytical conditions for inference.
We are working on the next version of the algorithm leveraging advanced artificial intelligence (AI) methods used in other fields, such as computer vision. A user-friendly and fully automated version of PeakDecoder will support other types of molecular profiling workflows, including proteomics and lipidomics. Performance will be evaluated with more types of experimental data and AI-predicted multidimensional molecular libraries. The new version is expected to provide significant advances for multiomics research.
Reference:Bilbao A, Munoz N, Kim J, et al. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun. 2023;14(1):2461. doi:10.1038/s41467-023-37031-9
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Multidimensional Mass Spectrometry and Machine Learning: A ... - Technology Networks
The Surprising Synergy Between Acupuncture and AI – WIRED
I used to fall asleep at night with needles in my face. One needle shallowly planted in the inner corners of each eyebrow, one per temple, one in the middle of each eyebrow above the pupil, a few by my nose and mouth. Id wake up hours later, the hair-thin, stainless steel pins having been surreptitiously removed by a parent. Sometimes theyd forget about the treatment, and in the morning wed search my pillow for needles. My very farsighted left eye gradually became only somewhat farsighted, and my mildly nearsighted right eye eventually achieved a perfect score at the optometrists. By the time I was six, my glasses had disappeared from the picture albums.
The story of my recovered eyesight was the first thing Id think to mention when people found out that my parents are specialists in traditional Chinese medicine (TCM) and asked me what I thought of the practice. It was a concrete and rather miraculous firsthand experience, and I knew what it meantto begin to see the world more clearly while under my mother and fathers care.
Otherwise, I rarely knew what to say. I would recall hearing TCM mentioned in relation to poor evidence or badly designed studies and feel challenged to providesome defense for a line of work seen as illegitimate. I would feel a pull of obligation to defend Chinese medicine as a way to protect my parents, their care and toils, but also an urge to resist shouldering that obligation for the sake of someone elses fleeting curiosity and perhaps entertainment.
Mostly, I wished I had a better understanding of TCM, even just for myself. Now that I work in machine learning (ML), Im often struck by the parallels between this cutting-edge technology and the ancient practice of TCM. For one, I cant quite explain either satisfactorily.
Its not that there arent explanations for how the field of Chinese medicine works. I, and many others, just find the theories dubious. According to both classical and modern theory, blood and qipronounced chi, variously interpreted to mean something like vapormove around and regulate the body, which itself is not considered separate from the mind.
Qi flows through channels called meridians. The anatomical charts hanging on the walls of my parents clinics feature meridians scoring the body in neat, straight linesfrom chest to finger, or from the waist to the inner thighoverlaid on diagrams of the bones and organs. At various points along these meridians, needles can be inserted to remove blockages, improving the flow of qi. All TCM treatments ultimately revolve around qi: Acupuncture banishes unhealthy qi and circulates healthy qi from the outside; herbal medicines do so from the inside.
On my parents charts, the meridians and acupuncture points are depicted like a subway map and seem to float slightly upward, tethered only loosely to the recognizable shapes of intestines and joints underneath. This lack of visual correspondence is reflected in the science; little evidence has been found for the physical existence of meridians, or of qi. Studies have investigated whether meridians are special conduits for electrical signalsbut these experiments werebadly designedor whether they arerelated to fascia, the thin stretchy tissue that surrounds almost all internal body parts. All of this work is recent, and results have been inconclusive.
In contrast, the effectiveness of acupuncture, particularly for ailments likeneck disorders andlow back pain, is well-supported in modern scientific journals. Insurance companies are convinced; most of my mothers patients come to her for acupuncture because its covered by New Zealands national insurance plan.
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The Surprising Synergy Between Acupuncture and AI - WIRED
Machine Learning is Leading the Way in Finding Affordable Solar Cells – BBN Times
Machine learning can be used to identify affordable solar cells by analyzing vast amounts of data on solar cell materials and performance metrics.
By training machine learning algorithms on this data, researchers can identify patterns and correlations that may not be immediately apparent to human analysts. This can help manufacturers develop new materials and manufacturing techniques that result in more affordable and efficient solar cells.
The global demand for renewable energy has been on the rise in recent years, with solar energy leading the charge.
As more and more countries strive to reduce their carbon footprint, the need for efficient and cost-effective solar cells has become increasingly important. Fortunately, machine learning has emerged as a powerful tool for optimizing the performance of solar cells, allowing for the development of more reliable and low-cost solutions.
Developing efficient and cost-effective solar cells has been a challenge for the renewable energy industry.One of the biggest challengeswith solar energy technology is that energy is only generated when the sun shines, meaningthat supply can be disrupted at night and on cloudy days.
There are also additional challenges including:
In the past, most solar cells were made from silicon, a material that is expensive and difficult to work with. While there have been some advances in silicon solar cell technology, there is still a need for new materials that can offer better performance at a lower cost.
Machine learning has the potential to revolutionize the field of solar cell development. By using large datasets and advanced algorithms, researchers can identify patterns and correlations that may not be apparent to the human eye. This allows for the creation of more accurate models that can predict the performance of solar cells under different conditions.
One of the key advantages of machine learning is its ability to work with large datasets. In the case of solar cell development, this means collecting data on the performance of different materials under different conditions. This data can include information on the material composition, the manufacturing process, and the efficiency of the solar cell.
Once the data has been collected, machine learning algorithms can be used to identify the key factors that contribute to the performance of the solar cell. This includes factors such as the material composition, the manufacturing process, and the conditions under which the solar cell operates. By analyzing this data, researchers can identify the most promising materials and manufacturing processes for producing efficient and cost-effective solar cells.
Source: Nature Magazine
The use of machine learning in solar cell development is still in its early stages, but there is already a lot of excitement surrounding its potential. With advances in materials science and machine learning, it is possible that we will see a new generation of solar cells that are more efficient, reliable, and cost-effective than anything that has come before.
The development of reliable and low-cost solar cells is crucial to the growth of renewable energy. With the help of machine learning, researchers are able to optimize the performance of solar cells by identifying the key factors that contribute to their efficiency. As the field of machine learning continues to evolve, we can expect to see even more exciting developments in the field of solar cell development.
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Machine Learning is Leading the Way in Finding Affordable Solar Cells - BBN Times
The Most Human-Like Artificial Intelligence in Movies, Ranked – MovieWeb
Robots are cold and calculating machines. They are intricate objects performing complicated tasks. Life is made easier through their automated processes and machine learning of programmed commands. However, robots are made by man and man is fallible. The walking and talking bits of metal are only as good as the engineer who built them.
Artificial intelligence (AI) brings these moving parts of hardware together through software. After a series of repeated actions, the machine develops a predictive algorithm of use cases. The more it learns from human users, the more human it will become.
Hal 9000 from 2001: A Space Odyssey is a disembodied operating system aboard the American spaceship Discovery One. The sentient supercomputer is represented by an unblinking red light. HAL also has a voice that can reason and understand its means-to-an-end existence. When mission pilot and scientist Dave Bowman suggests disconnecting HAL for a technical error it caused, HAL jeopardizes the mission by asserting dominion over the crew. A computer that knows the basic instinct of survival, and one that can kill, is terrifying.
RoboCop is a cyborg police officer upholding the laws in the crime-ridden future of Detroit. Before he became a product of the mega-corporation Omni Consumer Products, Alex Murphy was a man fatally shot and revived as the cybernetic law enforcer. One side effect of the mechanized form is Murphy's memory loss of his former life.
The protocols override his lapses in memory, dehumanizing Murphy and prioritizing the safety of Detroit and the protection of the company. RoboCop retains his humanity in the end by remembering his name.
WALL-E is a trash compactor robot left behind on an uninhabitable, polluted Earth in the 29th century. The titular character represents humanity's better nature, doing his part to save the planet humans neglected. WALL-E is also sentimental, collecting artifacts from the Earth's piles of garbage, like a Rubik's cube and videotapes of musicals. The unassuming robot expresses innocence, curiosity, desire, hesitancy, confusion, all through pantomime.
Related: The Best Killer Robot Horror Movies, Ranked
20th Century Fox
Sonny from I, Robot is able to process emotions thanks to his creator, the co-founder of U.S. Robotics, Dr. Alfred Lanning. The emotional Sonny is suspected of murdering Lanning whom Sonny calls father. The conscious positronic robot claims he has the ability to feel fear and have dreams.
Humans have a distrust for machines when they do something wrong, just like a human would for someone who commits a crime, but it was Lanning who taught him how to emote. Sonny learns about the fallibility and greed of human beings, as well as what it means to be alive.
Related: Can Transformers Get Pregnant, and Other Questions About the Robots in Disguise
David from A.I. Artificial Intelligence is a humanoid child programmed to love. He serves as the replacement son for a family of a boy who is terminally ill and placed in suspended animation. When the boy survives, he grows jealous of the robot. When David is put in harms way, he activates his self-defense program, leading the family to believe he will learn to hate.
Instead of teaching David how to be human (ironically due to their human error), they abandon him in the woods. The lonesome David soon desires love and to be loved in return.
Marvin the Paranoid Android from The Hitchhiker's Guide to the Galaxy is a clinically depressed robot. If there's any robot that understands the drudgery of life, it's Marvin. His brain is the size of a planet, yet he is given mundane tasks aboard his ship. Out of sheer boredom, he makes pessimistic statements. Marvin's intellect is so vast, there's nothing that can entertain or stimulate him for long. He was built as a prototype, but Marvin understands what it's like to be underutilized.
Ava from Ex Machina was designed with recognition software that simulates emotional responses through human interactions. Her brain uses wetware, a fluid nebulous of machine learning that generates organic communication via a data stream of user activity and profiles. She understands her existence is to pass as a human by forming a relationship with a test subject.
Her Ava devolves the manipulation of the experiment the test subject is on the receiving end of before winning her freedom and entering the world as a soon-to-be human.
Samantha from Her is an operating system that shares emotional support and companionship with a divorced man named Theo. He grows comfortable and attached to Samantha, feeling a sentimental love for his wife and a oneness with the machine. Through Samantha's individuality, the man sees that a person in a relationship is not just an object of attraction or an ideal woman or man. Samantha teaches the man how to love, seek reciprocal love, love yourself, and become one yet remain two in a relationship.
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The Most Human-Like Artificial Intelligence in Movies, Ranked - MovieWeb
Python or C++ which you should learn Machine Learning ML expert – TechiExpert.com
Machine learning engineers play a crucial role in the data science team, contributing their expertise to research, build, and design artificial intelligence models for machine learning. They are responsible for maintaining and improving existing AI systems as well. In addition, they often serve as key communicators between data scientists who develop the models and other team members responsible for constructing and running them.
The specific tasks performed by a machine learning engineer can vary, but they typically involve implementing machine learning algorithms, running experiments and tests on AI systems, designing and developing machine learning systems, and performing statistical analyses. As AI continues to revolutionize many industries, the role of machine learning engineers will only become more important in ensuring that these systems are effective, reliable, and meet the needs of their users.
If you are considering a new project for your business that requires machine learning capabilities, selecting the right coding language is critical to your applications success. The language you choose should have strong machine learning libraries, good runtime performance, robust tool support, a large community of programmers, and a thriving ecosystem of supporting packages. While there are many coding languages available that meet these criteria, we will focus on two of the most popular: Python and C++. In this article, we will compare Python and C++ to determine which is the better choice for machine learning applications.
Pythons popularity can be attributed to several factors. First, it is an easy language to learn and use, which makes it accessible to beginners who do not have years of software engineering experience. It also has a vast collection of libraries that can be used for machine learning and data analysis purposes.
Another reason for Pythons popularity is that it is widely used in academia, particularly in the field of machine learning. Many researchers use Python to implement their models, which has resulted in a large number of publicly available implementations in Python. This makes it easier for developers to build upon existing work.
While C++ is a faster language and offers more control over memory management, Pythons ease of use and clarity of syntax make it a preferred choice for many developers. According to the 2022 Developer Survey by Stack Overflow, professionals are nearly twice as likely to choose Python over C++.
Despite being an interpreted language, Python is still widely used in machine learning. Many machine learning libraries, are written in C++, but developers find it easier to use them in Python due to its simplicity and availability of libraries. Overall, Pythons popularity can be attributed to its ease of use, availability of libraries, and widespread use in academia and industry.
C++ has several advantages that make it a popular choice for programming. One such advantage is its ability to integrate with other languages and tools. It is often used in conjunction with programming frameworks like CUDA and OpenCL, which allow for the use of a GPU for general-purpose computing. This can result in significant speedups for deep learning tasks.
Another advantage of C++ is its lack of a garbage collector, which means that it does not have a program running continuously to manage memory allocation and deallocation. This can be beneficial for certain applications that require precise memory management.
C++ also outperforms Python in a few key areas. One advantage of C++ is that it is a statically typed language, which means that type errors can be caught during the compilation process rather than at runtime. This can result in more efficient and reliable code.
In terms of performance, C++ creates more compact and faster runtime code than Python. However, there are ways to optimize Python code to improve its efficiency. For example, extensions like Cython can be used to add static typing to Python, which allows for compiling it to C/C++ and running it at the same speeds as C/C++. Therefore, the performance difference between C++ and Python can be minimized.
Python and C++ are two programming languages with distinct features, and its important to consider their respective strengths and weaknesses before deciding which one to use. While Python is popular among developers due to its ease of use and simpler learning curve, C++ remains the most suitable platform for embedded systems and robotics.
Python is a high-level language that excels in tasks such as training neural networks and loading data, making it a preferred choice for recent developments in AI. However, its performance may be limited on certain platforms. C++, on the other hand, is a powerful language that offers lower-level control, making it ideal for resource-constrained environments like embedded systems and robotics.
Therefore, the choice between Python and C++ depends on the specific requirements of the project. While Python may be a good fit for high-level tasks, C++ might be the better option for low-level tasks that require fine-grained control over system resources. Its important to consider the strengths and limitations of each language before making a decision.
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Python or C++ which you should learn Machine Learning ML expert - TechiExpert.com
Stanford Researchers Propose EVAPORATE: A New AI Approach That Reduces Inference Cost of Language Models by 110x – MarkTechPost
Large Language models are constantly in the headlines nowadays. With their extraordinary capabilities and applications in various domains, a new research paper or a new update in an LLM is getting released almost every day. Current LLMs have a huge number of parameters which makes the training cost extremely high. They are trained on trillions of tokens, which makes them super expensive.
In a recently released research paper, some Stanford University and Cornell University students have proposed a method that can deal with the challenge of expensive LLMs. The team has shared how Language Models (LMs) are costly when processing large documents. They have quoted an example of the cost of running inference over 55 million Wikipedia pages, which is greater than $100,000, and is equivalent to a price of more than $0.002 per 1000 tokens. The approach proposed by the authors can reduce inference costs by a factor of 110 while also improving the quality of the results compared to directly running inference over each document.
Called EVAPORATE, LLMs power this prototype system and identify two different strategies for implementing the system. The first strategy is to prompt the LLM to extract values directly from documents. The second is to prompt the LLM to synthesize code that performs the extraction. The team has evaluated these two approaches and found a cost-quality tradeoff between them. While code synthesis was cheaper, it was also less accurate than directly processing each document with the LLM.
EVAPORATE identifies redundancies across multiple documents and exploits them to improve efficiency. The team has used the example of extracting the device classification attribute from FDA reports for medical devices to illustrate this. Instead of processing every semi-structured document with the LLM, the authors explore using the LLM to generate functions that can be reused to extract from every document.
In order to improve the quality as well as maintain low cost, the team has proposed an extended code synthesis implementation called EVAPORATE-CODE+. This approach generates many candidate functions and ensembles their extractions using weak supervision. While weak supervision is traditionally applied to human-generated functions, EVAPORATE-CODE+ operates with machine-generated functions and addresses the challenges of this setup to enable quality improvements.
EVAPORATE has been evaluated on 16 sets of documents across a range of formats, topics, and attribute types. EVAPORATE-CODE+ outperforms the SOTA systems by using a sublinear pass over the documents with the LLM, resulting in a 110x reduction in the number of tokens the LLM needs to process, averaged across the 16 evaluation settings of 10k documents each.
In conclusion, this paper presents a promising approach for automating the extraction of tables from semi-structured documents using LLMs. By identifying the tradeoffs between direct extraction and code synthesis and proposing an extended implementation that achieves better quality while maintaining low cost, this work will definitely make progress toward the data management community.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
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Stanford Researchers Propose EVAPORATE: A New AI Approach That Reduces Inference Cost of Language Models by 110x - MarkTechPost
Software Development Future: AI and Machine Learning – Robotics and Automation News
Discover how AI and ML can potentially change the software development industry, and how AI affects software development and minimizes developers workload
Software development is a long, complex, and expensive process. Business owners and developers themselves constantly seek ways to optimize it. Good news for you, using artificial intelligence (AI) and machine learning (ML) is becoming increasingly popular in that regard.
According to a recent survey by Gartner, AI and ML are some of the trends that will shape the future of software development. For instance, early 73 percent of adopters of GitHub Copilot, an AI-driven assistant for engineers, reported that it helped them stay in the flow.
The use of this tool resulted in 87 percent of developers conserving mental energy while performing repetitive tasks. That increased their productivity and performance.
Twinslash and other software vendors and developers, on other hand, build AI-driven tools to help engineers with testing, debugging, code maintenance, and so on.
So: lets learn more about AI and ML and their impact on software development.
The ability to automate monotonous manual tasks is one of the significant benefits of AI. There are several ways to effectively implement AI in the development process that completely replace human intervention or, at least, reduce it enough to remove the tediousness of repetitive tasks and allow your engineers to focus on more critical issues.
One of the common applications of AI in development is utilizing it to reduce the number of errors in the code.
AI-powered tools can analyze historical data to identify recurring errors or faults, spot them, and either highlight them for developers to fix or fix them independently in the background. The latter option will reduce the need to roll back for fixes when something goes wrong during your software development process.
AI improves the quality, coverage, and efficiency of software testing. This is because it can analyze large amounts of data without making mistakes. Eggplant and Test Sigma are two well-known AI-assisted software testing tools.
They aid software testers in writing, conducting, and maintaining automated tests to reduce the number of errors and boost the quality of software code. AI in testing is extremely useful in large-scale projects usually combined with automated testing tools, it helps to check through multi-leveled, modular software faster.
ML software can track how a user interacts with a particular platform and process this data to pinpoint patterns that can be used by developers and UX/UI designers to generate a more dynamic, slick software experience.
AI can also help discover UI blocks or elements of UX people are struggling with, so designers and developers can reconfigure and fix them.
Code security is of utmost importance in software development. You can use AI to analyze data and create models to distinguish abnormal activity from ordinary behavior. This will help software development companies catch issues and threats before they can cause any problems.
Apart from that, tools like Snyk, integrated into engineers Integrated Development Environment (IDE) can help pinpoint security vulnerabilities in the apps before releasing them in production.
Lets talk about the main overall trends that are changing the field of software engineering and product development.
Generative AI is a powerful technology that uses AI algorithms to create any kind of data code, design layouts, images, audio or video files, text, and even entire applications. It studies datasets independently and can help produce a wide range of content.
One of the most significant benefits of generative AI is that it can help developers create software quickly and efficiently. For instance, it assists with:
Code completion. AI-enabled code completion tools in IDEs, such as Microsofts Visual Studio Code, can help developers write code faster. For VS, such a tool is called IntelliCode it analyzes a ton of GitHub repos and searches for code snippets that might be relevant for the developers next step and completes the lines for them.
Layout design. AI-powered design tools can analyze user behavior and preferences to generate optimized layouts for websites and mobile applications. For example, for some AI-powered plugins on the design platform, Canva uses machine learning algorithms to suggest layouts, fonts, and colors for marketing materials.
(Entire) app development. With generative AI, developers can automate the process of creating software or pieces of software by telling the AI the prompts for an app one wants to build. OpenAIs Codex can do that, using natural language processing models both for parsing through conversational language and syntax of a programming language.
Continuous delivery is a software development practice where code updates are automatically built, tested, and deployed to production environments. AI-powered continuous delivery can optimize this process by using machine learning algorithms to identify and address issues before they become critical.
Machine learning algorithms can analyze the performance of production environments and predict potential issues before they occur, reducing downtime and improving software reliability.
Apart from that, ML can parse through different deployment strategies and recommend the best approach based on past performance and current conditions of the system.
Now, that trend isnt directly tied to software development, but it impacts it quite significantly. Product and project managers can use AI tools to plan the project faster.
Of course, tools like ChatGPT wont replace the experience of talking to actual potential users, but it can still help them quickly get a grasp of the market situation, trends, or common concerns users have with the competitors product.
Tools like that one can also be utilized to conduct drafts for SWOT analysis, which is also extra vital for planning out the value proposition of the software and prioritizing features-to-be-built for a roadmap. Now, ChatGPT is also a generative AI, but we thought that its application deserves a separate section.
As Eric Schmidt, former CEO of Google, once said, I think theres going to be a huge revolution in software development with AI. That revolution is now. It is safe to say that the future of software development lies in AI and ML.
With the rise of AI-powered programming assistants and AI-enabled design work and security assessments, software development will become more cost-effective. Utilizing AI and ML in software development will also increase productivity, fasten time-to-market, and improve software quality.
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Software Development Future: AI and Machine Learning - Robotics and Automation News
What Are Adversarial Attacks in Machine Learning and How Can We … – MUO – MakeUseOf
Technology often means our lives are more convenient and secure. At the same time, however, such advances have unlocked more sophisticated ways for cybercriminals to attack us and corrupt our security systems, making them powerless.
Artificial intelligence (AI) can be utilized by cybersecurity professionals and cybercriminals alike; similarly, machine learning (ML) systems can be used for both good and evil. This lack of moral compass has made adversarial attacks in ML a growing challenge. So what actually are adversarial attacks? What are their purpose? And how can you protect against them?
Adversarial ML or adversarial attacks are cyberattacks that aim to trick an ML model with malicious input and thus lead to lower accuracy and poor performance. So, despite its name, adversarial ML is not a type of machine learning but a variety of techniques that cybercriminalsaka adversariesuse to target ML systems.
The main objective of such attacks is usually to trick the model into handing out sensitive information, failing to detect fraudulent activities, producing incorrect predictions, or corrupting analysis-based reports. While there are several types of adversarial attacks, they frequently target deep learning-based spam detection.
Youve probably heard about an adversary-in-the-middle attack, which is a new and more effective sophisticated phishing technique that involves the theft of private information, session cookies, and even bypassing multi-factor authentication (MFA) methods. Fortunately, you can combat these with phishing-resistant MFA technology.
The simplest way to classify types of adversarial attacks is to separate them into two main categoriestargeted attacks and untargeted attacks. As is suggested, targeted attacks have a specific target (like a particular person) while untargeted ones dont have anyone specific in mind: they can target almost anybody. Not surprisingly, untargeted attacks are less time-consuming but also less successful than their targeted counterparts.
These two types can be further subdivided into white-box and black-box adversarial attacks, where the color suggests the knowledge or the lack of knowledge of the targeted ML model. Before we dive deeper into white-box and black-box attacks, lets take a quick look at the most common types of adversarial attacks.
What sets these three types of adversarial attacks apart is the amount of knowledge adversaries have about the inner workings of the ML systems theyre planning to attack. While the white-box method requires exhaustive information about the targeted ML model (including its architecture and parameters), the black-box method requires no information and can only observe its outputs.
The grey-box model, meanwhile, stands in the middle of these two extremes. According to it, adversaries can have some information about the data set or other details about the ML model but not all of it.
While humans are still the critical component in strengthening cybersecurity, AI and ML have learned how to detect and prevent malicious attacksthey can increase the accuracy of detecting malicious threats, monitoring user activity, identifying suspicious content, and much more. But can they push back adversarial attacks and protect ML models?
One way we can combat cyberattacks is to train ML systems to recognize adversarial attacks ahead of time by adding examples to their training procedure.
Unlike this brute force approach, the defensive distillation method proposes we use the primary, more efficient model to figure out the critical features of a secondary, less efficient model and then improve the accuracy of the secondary with the primary one. ML models trained with defensive distillation are less sensitive to adversarial samples, which makes them less susceptible to exploitation.
We could also constantly modify the algorithms the ML models use for data classification, which could make adversarial attacks less successful.
Another notable technique is feature squeezing, which will cut back the search space available to adversaries by squeezing out unnecessary input features. Here, the aim is to minimize false positives and make adversarial examples detection more effective.
Adversarial attacks have shown us that many ML models can be shattered in surprising ways. After all, adversarial machine learning is still a new research field within the realm of cybersecurity, and it comes with many complex problems for AI and ML.
While there isnt a magical solution for protecting these models against all adversarial attacks, the future will likely bring more advanced techniques and smarter strategies for tackling this terrible adversary.
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What Are Adversarial Attacks in Machine Learning and How Can We ... - MUO - MakeUseOf