Category Archives: Machine Learning
OpenAI Says GPT-4 Is Better in Nearly Every Way. What Matters More Is Millions Will Use It – Singularity Hub
In 2020, artificial intelligence company OpenAI stunned the tech world with its GPT-3 machine learning algorithm. After ingesting a broad slice of the internet, GPT-3 could generate writing that was hard to distinguish from text authored by a person, do basic math, write code, and even whip up simple web pages.
OpenAI followed up GPT-3 with more specialized algorithms that could seed new products, like an AI called Codex to help developers write code and the wildly popular (and controversial) image-generator DALL-E 2. Then late last year, the company upgraded GPT-3 and dropped a viral chatbot called ChatGPTby far, its biggest hit yet.
Now, a rush of competitors is battling it out in the nascent generative AI space, from new startups flush with cash to venerable tech giants like Google. Billions of dollars are flowing into the industry, including a $10-billion follow-up investment by Microsoft into OpenAI.
This week, after months of rather over-the-top speculation, OpenAIs GPT-3 sequel, GPT-4, officially launched. In a blog post, interviews, and two reports (here and here), OpenAI said GPT-4 is better than GPT-3 in nearly every way.
GPT-4 is multimodal, which is a fancy way of saying it was trained on both images and text and can identify, describe, and riff on whats in an image using natural language. OpenAI said the algorithms output is higher quality, more accurate, and less prone to bizarre or toxic outbursts than prior versions. It also outperformed the upgraded GPT-3 (called GPT 3.5) on a slew of standardized tests, placing among the top 10 percent of human test-takers on the bar licensing exam for lawyers and scoring either a 4 or a 5 on 13 out of 15 college-level advanced placement (AP) exams for high school students.
To show off its multimodal abilitieswhich have yet to be offered more widely as the company evaluates them for misuseOpenAI president Greg Brockman sketched a schematic of a website on a pad of paper during a developer demo. He took a photo and asked GPT-4 to create a webpage from the image. In seconds, the algorithm generated and implemented code for a working website. In another example, described by The New York Times, the algorithm suggested meals based on an image of food in a refrigerator.
The company also outlined its work to reduce risk inherent in models like GPT-4. Notably, the raw algorithm was complete last August. OpenAI spent eight months working to improve the model and rein in its excesses.
Much of this work was accomplished by teams of experts poking and prodding the algorithm and giving feedback, which was then used to refine the model with reinforcement learning. The version launched this week is an improvement on the raw version from last August, but OpenAI admits it still exhibits known weaknesses of large language models, including algorithmic bias and an unreliable grasp of the facts.
By this account, GPT-4 is a big improvement technically and makes progress mitigating, but not solving, familiar risks. In contrast to prior releases, however, well largely have to take OpenAIs word for it. Citing an increasingly competitive landscape and the safety implications of large-scale models like GPT-4, the company opted to withhold specifics about how GPT-4 was made, including model size and architecture, computing resources used in training, what was included in its training dataset, and how it was trained.
Ilya Sutskever, chief technology officer and cofounder at OpenAI, told The Verge it took pretty much all of OpenAI working together for a very long time to produce this thing and lots of other companies would like to do the same thing. He went on to suggest that as the models grow more powerful, the potential for abuse and harm makes open-sourcing them a dangerous proposition. But this is hotly debated among experts in the field, and some pointed out the decision to withhold so much runs counter to OpenAIs stated values when it was founded as a nonprofit. (OpenAI reorganized as a capped-profit company in 2019.)
The algorithms full capabilities and drawbacks may not become apparent until access widens further and more people test (and stress) it out. Before reining it in, Microsofts Bing chatbot caused an uproar as users pushed it into bizarre, unsettling exchanges.
Overall, the technology is quite impressivelike its predecessorsbut also, despite the hype, more iterative than GPT-3. With the exception of its new image-analyzing skills, most abilities highlighted by OpenAI are improvements and refinements of older algorithms. Not even access to GPT-4 is novel. Microsoft revealed this week that it secretly used GPT-4 to power its Bing chatbot, which had recorded some 45 million chats as of March 8.
While GPT-4 may not to be the step change some predicted, the scale of its deployment almost certainly will be.
GPT-3 was a stunning research algorithm that wowed tech geeks and made headlines; GPT-4 is a far more polished algorithm thats about to be rolled out to millions of people in familiar settings like search bars, Word docs, and LinkedIn profiles.
In addition to its Bing chatbot, Microsoft announced plans to offer services powered by GPT-4 in LinkedIn Premium and Office 365. These will be limited rollouts at first, but as each iteration is refined in response to feedback, Microsoft could offer them to the hundreds of millions of people using their products. (Earlier this year, the free version of ChatGPT hit 100 million users faster than any app in history.)
Its not only Microsoft layering generative AI into widely used software.
Google said this week it plans to weave generative algorithms into its own productivity softwarelike Gmail and Google Docs, Slides, and Sheetsand will offer developers API access to PaLM, a GPT-4 competitor, so they can build their own apps on top of it. Other models are coming too. Facebook recently gave researchers access to its open-source LLaMa modelit was later leaked onlinewhile a Google-backed startup, Anthropic, and Chinas tech giant Baidu rolled out their own chatbots, Claude and Ernie, this week.
As models like GPT-4 make their way into products, they can be updated behind the scenes at will. OpenAI and Microsoft continually tweaked ChatGPT and Bing as feedback rolled in. ChatGPT Plus users (a $20/month subscription) were granted access to GPT-4 at launch.
Its easy to imagine GPT-5 and other future models slotting into the ecosystem being built now as simply, and invisibly, as a smartphone operating system that upgrades overnight.
If theres anything weve learned in recent years, its that scale reveals all.
Its hard to predict how new tech will succeed or fail until it makes contact with a broad slice of society. The next months may bring more examples of algorithms revealing new abilities and breaking or being broken, as their makers scramble to keep pace.
Safety is not a binary thing; it is a process, Sutskever told MIT Technology Review. Things get complicated any time you reach a level of new capabilities. A lot of these capabilities are now quite well understood, but Im sure that some will still be surprising.
Longer term, when the novelty wears off, bigger questions may loom.
The industry is throwing spaghetti at the wall to see what sticks. But its not clear generative AI is usefulor appropriatein every instance. Chatbots in search, for example, may not outperform older approaches until theyve proven to be far more reliable than they are today. And the cost of running generative AI, particularly at scale, is daunting. Can companies keep expenses under control, and will users find products compelling enough to vindicate the cost?
Also, the fact that GPT-4 makes progress on but hasnt solved the best-known weaknesses of these models should give us pause. Some prominent AI experts believe these shortcomings are inherent to the current deep learning approach and wont be solved without fundamental breakthroughs.
Factual missteps and biased or toxic responses in a fraction of interactions are less impactful when numbers are small. But on a scale of hundreds of millions or more, even less than a percent equates to a big number.
LLMs are best used when the errors and hallucinations are not high impact, Matthew Lodge, the CEO of Diffblue, recently told IEEE Spectrum. Indeed, companies are appending disclaimers warning users not to rely on them too muchlike keeping your hands on the steering wheel of that Tesla.
Its clear the industry is eager to keep the experiment going though. And so, hands on the wheel (one hopes), millions of people may soon begin churning out presentation slides, emails, and websites in a jiffy, as the new crop of AI sidekicks arrives in force.
Image Credit:Luke Jones /Unsplash
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OpenAI Says GPT-4 Is Better in Nearly Every Way. What Matters More Is Millions Will Use It - Singularity Hub
How Machine Learning Helps Improve Fleet Safety – Robotics and Automation News
Artificial intelligence is one of those technical buzzwords that has captivated the press and is being discussed in practically every facet of life.
A subset of artificial intelligence known as machine learning, in particular, may assist building and construction fleet operators in optimizing fleet performance while preserving safety as a primary concern.
As roads become increasingly dangerous, safety directors face a challenging task: sifting through terabytes of information to uncover weaknesses in fleet safety.
As data analytics technologies develop, safety managers must learn to use them to surf the data tsunami, stay afloat, and protect the safety of their drivers.
Artificial intelligence (AI) is sometimes mistaken for machine learning (ML). People frequently confuse or use the phrases interchangeably, although they are not the same thing.
AI entails having a machine perform something that a person would ordinarily do. Machine learning, on the other hand, refers to technology that learns or works out how to accomplish something on its own.
Read on to learn more about machine learning and how it improves fleet safety.
Giving computers data and letting them learn for themselves is exactly what machine learning entails. This system can take a massive quantity of information and develop models based on behavioral patterns. They can learn and improve themselves autonomously by studying known occurrences without being trained. As the system develops, it may be fed fresh data and used to forecast the outcomes of future occurrences.
Machine learning in fleet management enhances how data analytics systems handle large amounts of data. The system begins to learn which data is most frequently reviewed throughout daily use and adapts itself in real time based on the owners behavior.
Machine learning algorithms enable the building of dashboards with features that make it simple to examine different data points such as vehicle downtime and specific driver behavior that should be rectified. These advanced neural networks can warn drivers when their vehicles need repair or are going to experience mechanical troubles.
Smart fleet management systems are better capable of diagnosing problems than traditional ones. A company can make better overall judgments when it can view the large picture of its fleet in one location. ML and AI technologies can drastically reduce total expenses.
Machine learning is beneficial in all aspects of fleet vehicle management, including efficiency and safety. Manual methods made fleet management laborious and difficult, but machine learning helps simplify operational operations, making them easy and simple.
Moreover, when integrated with machine vision, ML may improve fleet management even further.
Listed below are a few advantages of ML-driven fleet management:
In fleet management, machine learning employs predictive analysis to avoid probable accidents and notify at-risk drivers. A large and full set of historical data may be used to develop a prediction model. It entails examining the actions that led to the accidents.
Using the correct machine learning technology enables risk minimization, accident avoidance, and insurance claim reduction. Understanding, selecting, and then executing the best solution is important to make an accurate and effective forecast.
Integrating centralized data management software with specialized tools allows for rapid information collecting, prediction, and exception handling.
Listed below are a few ways how ML improves fleet safety.
Machine learning has enabled fleet managers to become more proactive in risk management. Early safety measures were quite reactive. They may have detected things like harsh braking circumstances, but they lacked information about what caused them.
Machine learning goes beyond merely logging such occurrences. It may, for example, include a video to demonstrate what caused the brakes. This reduces false alarms and enables fleets to allocate precious resources where they are most needed.
From time to time, police reports are not always reliable after an accident. When cameras were first employed, it became clear that most of the information in police reports was incorrect. When cameras were put at various transportation businesses, it wasnt long before the technology vindicated a driver who had been wrongfully accused of causing a crash by law police.
Fleets that use the most up-to-date safety ML technologies can be more focused in their training initiatives. This has aided driver turnover. Rather than retraining all drivers in the fleet on backing owing to a rise in backing events, the fleet may concentrate on only those drivers whose conduct puts them at a higher risk of backing issues.
Fleet companies that use ML safety solutions must also keep the human factor in mind. This should be considered as another tool in the toolkit for assisting drivers in sharing the road safely.
People do not trust a corporation that goes too far down the ML route and just tells them to absolutely believe this machine and do exactly what it says. It must be included in a program. ML exists to help humans make the best decisions possible.
Data obtained by safety technology also provides a safety manager with confidence when dealing with driving behaviors. They are not making decisions based on assumptions or insufficient information. Driver scorecards gamified performance, creating a competitive environment that drove drivers to perform at their best.
Fleet management is a critical component of running a profitable business. Since crashes are statistically uncommon, and carriers cant look at that fine level of data, one cant make inferences based on what happens in an hour, a day, or even a week. The safety of a fleet may be dramatically enhanced with excellent machine learning in place.
No, machine learning is not the answer to all the problems in fleet management. It can provide various information to the company with which they can solve problems using a variety of methods.
Listed below are some of the factors that can affect machine learning;
Machine learning training necessitates the storage system reading and rereading whole data sets, generally at random. This implies that archive systems that only provide sequential access techniques, such as tape, cannot be used.
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How Machine Learning Helps Improve Fleet Safety - Robotics and Automation News
We Need To Make Machine Learning Sustainable. Here’s How – Forbes
As machine learning progresses at breakneck speed, its intersection with sustainability is ... [+] increasingly crucial.
Irene Unceta is a professor and director of the Esade Double Degree in Business Administration & AI For Business
As machine learning progresses at breakneck speed, its intersection with sustainability is increasingly crucial. While it is clear that machine learning models will alter our lifestyles, work environments, and interactions with the world, the question of how they will impact sustainability cannot be ignored.
To understand how machine learning can contribute to creating a better, greener, more equitable world, it is crucial to assess its impact on the three pillars of sustainability: the social, the economic, and the environmental.
The social dimension
From a social standpoint, the sustainability of machine learning depends on its potential to have a positive impact on society.
Machine learning models have shown promise in this regard, for example, by helping healthcare organizations provide more accurate medical diagnoses, conduct high-precision surgeries, or design personalized treatment plans. Similarly, systems dedicated to analyzing and predicting patterns in data can potentially transform public policy, so long as they contribute to a fairer redistribution of wealth and increased social cohesion.
However, ensuring a sustainable deployment of this technology in the social dimension requires addressing challenges related to the emergence of bias and discrimination, as well as the effects of opacity.
Machine learning models trained on biased data can perpetuate and even amplify existing inequalities, leading to unfair and discriminatory outcomes. A controversial study conducted by researchers at MIT showed, for example, that commercial facial recognition software is less accurate for people with darker skin tones, especially darker women, reinforcing historical racial and gender biases.
Moreover, large, intricate models based on complex architectures, such as those of deep learning, can be opaque and difficult to understand. This lack of transparency can have a two-fold effect. On the one hand, it can lead to mistrust and lack of adoption. On the other, it conflicts with the principle of autonomy, which refers to the basic human right to be well-informed in order to make free decisions.
To promote machine learning sustainability in the social dimension, it is essential to prioritize the development of models that can be understood and that provide insights into their decision-making process. Knowing what these systems learn, however, is only the first step. To ensure fair outcomes for all members of society, regardless of background or socioeconomic status, diverse groups must be involved in these systems design and development and their ethical principles must be made explicit. Machine learning models today might not be capable of moral thinking, as Noam Chomsky recently highlighted, but their programmers should not be exempt from this obligation.
The economic dimension
Nor should the focus be solely on the social dimension. Machine learning will only be sustainable for as long as its benefits outweigh its costs from an economic perspective, too.
Machine learning models can help reduce costs, improve efficiency, and create new business opportunities. Among other things, they can be used to optimize supply chains, automate repetitive tasks in manufacturing, and provide insights into customer behavior and market trends.
Even so, the design and deployment of machine learning can be very expensive, requiring significant investments in data, hardware, and personnel. Models require extensive resources, in terms of both hardware and manpower, to develop and maintain. This makes them less accessible to small businesses and developing economies, limiting their potential impact and perpetuating economic inequality.
Addressing these issues will require evaluating the costs and benefits carefully, considering both short- and long-term costs, and balancing the trade-offs between accuracy, scalability, and cost.
But not only that. The proliferation of this technology will also have a substantial impact on the workforce. Increasing reliance on machine learning will lead to job loss in many sectors in the coming years. Efforts must be made to create new job opportunities and to ensure that workers have the necessary skills and training to transition to these new roles.
To achieve economic sustainability in machine learning, systems should be designed to augment, rather than replace, human capabilities.
The environmental dimension
Finally, machine learning has the potential to play a significant role in mitigating the impact of human activities on the environment. Unless properly designed, however, it may turn out to be a double-edged sword.
Training and running industrial machine learning models requires significant computing resources. These include large data centers and powerful GPUs, which consume a great deal of energy, as well as the production and disposal of hardware and electronic components that contribute to greenhouse gas emissions.
In 2018, DeepMind released AlphaStar, a multi-agent reinforcement-learning-based system that produced unprecedented results playing StarCraft II. While the model itself can be run on an average desktop PC, its training required the use of 16 TPUs for each of its 600 agents, running in parallel for more than 2 weeks. This raises the question of whether and to what extent these costs are justified.
To ensure environmental sustainability we should question the pertinence of training and deploying industrial machine learning applications. Decreasing their carbon footprint will require promoting more energy-efficient hardware, such as specialized chips and low-power processors, as well as dedicating efforts to developing greener algorithms that optimize energy consumption by using less data, fewer parameters, and more efficient training methods.
Machine learning may yet contribute to building a more sustainable world, but this will require a comprehensive approach that considers the complex trade-offs of developing inclusive, equitable, cost-effective, trustworthy models that have a low technical debt and do minimal environmental harm. Promoting social, economic, and environmental sustainability in machine learning models is essential to ensure that these systems support the needs of society, while minimizing any negative consequences in the long term.
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We Need To Make Machine Learning Sustainable. Here's How - Forbes
How data analytics and machine learning can transform your … – Supply Management
Data analytics is a powerful tool for procurement professionals to unlock value in their data but it's far from a one size fits all.
By understanding the different types, and their relevance to procurement, leaders and professionals can make informed decisions that lead to more optimised processes and better outcomes.
Data analytics can be categorised into four groups: descriptive, diagnostic, predictive and prescriptive. Descriptive and diagnostic analytics are typically more basic, while predictive and prescriptive categories are referred to as advanced because they use more sophisticated methods and uncover deeper insights.
The four categories of data analytics explained
Where does machine-learning fit in?
While there can be an overlap between advanced data analytics (ADA) and machine-learning (ML), the distinction lies in their specific use cases, the amount and complexity of the data utilised, the sophistication required and the level of human involvement versus automation involved.
Both ADA and ML can uncover insights and help make informed decisions around procurement strategy and operations by targeting processes such as demand forecasting, inventory management, and spend analysis. Some cases, involving less structured and more complex data, require cutting edge ML. For example, if a procurement team wants to analyse large volumes of supplier feedback, customer reviews, or legal contracts to identify patterns, sentiment, or risky clauses, this would require state of the art natural language processing algorithms.
ADA and ML models can overlap, but ML algorithms typically require a higher level of mathematical and statistical knowledge compared to advanced data analytics. ML can range from simple linear and logistic regression models to more complex models like decision trees, random forests and neural networks.
ADA can involve a human carefully creating a model, which is then tested for validity. In ML, a human helps train a model to understand how well it can adapt and predict new data, given business constraints. But after that, the model can theoretically re-train and re-learn from new datasets on its own, making it more autonomous and dynamic.
Its also important to stress part of the confusion between ADA and ML is related to not distinguishing between models and processes when referring to these terms. An ADA process might be obtaining insights, for instance understanding the characteristics of suspicious financial transactions based on historical data, whereas an ML process would be continuous monitoring, eg. real-time prediction of suspicious financial transactions based on historical data.
In other words, even if ADA and ML might be using the exact same mathematical model, the ML process can include the ADA process in a way that automates and optimises the tasks the ADA performs.
So where do you start when implementing procurement analytics?
Identifying the low-hanging fruit is essential, and businesses should focus on projects that provide a direct connection to value, impact multiple areas of the business, and make it easy to envision the potential of ADA and ML.
Such swift, high-ROI, holistic procurement analytics projects are feasible when expertise in data science, research, and forensic accounting are combined.
Dr Kyriakos Christodoulides is the director of Novel Intelligence.
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How data analytics and machine learning can transform your ... - Supply Management
Top Machine Learning Papers to Read in 2023 – KDnuggets
Machine Learning is a big field with new research coming out frequently. It is a hot field where academia and industry keep experimenting with new things to improve our daily lives.
In recent years, generative AI has been changing the world due to the application of machine learning. For example, ChatGPT and Stable Diffusion. Even with 2023 dominated by generative AI, we should be aware of many more machine learning breakthroughs.
Here are the top machine learning papers to read in 2023 so you will not miss the upcoming trends.
Singing Voice Beautifying (SVB) is a novel task in generative AI that aims to improve the amateur singing voice into a beautiful one. Its exactly the research aim of Liu et al. (2022) when they proposed a new generative model called Neural Singing Voice Beautifier (NSVB).
The NSVB is a semi-supervised learning model using a latent-mapping algorithm that acts as a pitch corrector and improves vocal tone. The work promises to improve the musical industry and is worth checking out.
Deep neural network models have become bigger than ever, and much research has been conducted to simplify the training process. Recent research by the Google team (Chen et al. (2023)) has proposed a new optimization for the Neural Network called Lion (EvoLved Sign Momentum). The method shows that the algorithm is more memory-efficient and requires a smaller learning rate than Adam. Its great research that shows many promises you should not miss.
Time series analysis is a common use case in many businesses; For example, price forecasting, anomaly detection, etc. However, there are many challenges to analyzing temporal data only based on the current data (1D data). That is why Wu et al. (2023) propose a new method called TimesNet to transform the 1D data into 2D data, which achieves great performance in the experiment. You should read the paper to understand better this new method as it would help much future time series analysis.
Currently, we are in a generative AI era where many large language models were intensively developed by companies. Mostly this kind of research would not release their model or only be commercially available. However, the Meta AI research group (Zhang et al. (2022)) tries to do the opposite by publicly releasing the Open Pre-trained Transformers (OPT) model that could be comparable with the GPT-3. The paper is a great start to understanding the OPT model and the research detail, as the group logs all the detail in the paper.
The generative model is not limited to only generating text or pictures but also tabular data. This generated data is often called synthetic data. Many models were developed to generate synthetic tabular data, but almost no model to generate relational tabular synthetic data. This is exactly the aim of Solatorio and Dupriez (2023) research; creating a model called REaLTabFormer for synthetic relational data. The experiment has shown that the result is accurately close to the existing synthetic model, which could be extended to many applications.
Reinforcement Learning conceptually is an excellent choice for the Natural Language Processing task, but is it true? This is a question that Ramamurthy et al. (2022) try to answer. The researcher introduces various library and algorithm that shows where Reinforcement Learning techniques have an edge compared to the supervised method in the NLP tasks. Its a recommended paper to read if you want an alternative for your skillset.
Text-to-image generation was big in 2022, and 2023 would be projected on text-to-video (T2V) capability. Research by Wu et al. (2022) shows how T2V can be extended on many approaches. The research proposes a new Tune-a-Video method that supports T2V tasks such as subject and object change, style transfer, attribute editing, etc. Its a great paper to read if you are interested in text-to-video research.
Efficient collaboration is the key to success on any team, especially with the increasing complexity within machine learning fields. To nurture efficiency, Peng et al. (2023) present a PyGlove library to share ML ideas easily. The PyGlove concept is to capture the process of ML research through a list of patching rules. The list can then be reused in any experiments scene, which improves the team's efficiency. Its research that tries to solve a machine learning problem that many have not done yet, so its worth reading.
ChatGPT has changed the world so much. Its safe to say that the trend would go upward from here as the public is already in favor of using ChatGPT. However, how is the ChatGPT current result compared to the Human Experts? Its exactly a question that Guo et al. (2023) try to answer. The team tried to collect data from experts and ChatGPT prompt results, which they compared. The result shows that implicit differences between ChatGPT and experts were there. The research is something that I feel would be kept asked in the future as the generative AI model would keep growing over time, so its worth reading.
2023 is a great year for machine learning research shown by the current trend, especially generative AI such as ChatGPT and Stable Diffusion. There is much promising research that I feel we should not miss because its shown promising results that might change the current standard. In this article, I have shown you 9 top ML papers to read, ranging from the generative model, time series model to workflow efficiency. I hope it helps.Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and Data tips via social media and writing media.
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Top Machine Learning Papers to Read in 2023 - KDnuggets
Google introduces new machine learning add on for Google Sheets – TechiExpert.com
Spreadsheets are often used by businesses of all sizes to complete both simple and complex tasks. Machine learning technology advancements have the potential to revolutionise different industries. Spreadsheet usage is meant to be accessible to all types of users, whereas machine learning is usually perceived as being too complex to use. Google is currently attempting to shift that paradigm for its online spreadsheet application Google Sheets. Explore more about the new machine learning add on for Google Sheets right below.
The operation of Google Sheets works in these three steps given below.
Check out the benefits of simple ML or new machine learning addon in Google sheets right below.
The beta version of Simple ML for Sheets is now accessible. A team of TensorFlow developers developed the Google Sheets add-on to make machine learning available to Sheets users with no prior experience with machine learning. Pretrained machine learning models and other no-code features are primarily used to achieve this.
Predicting missing values and identifying abnormal values are the two main ML tasks that this machine learning add-on is intended to support. Nevertheless, Simple ML for Sheets can also be used for more complex use cases like developing, testing, and analyzing machine learning models. It is likely that Simple MLs Advanced Tasks will need to be used, especially for data scientists and more experienced users who want to use Simple ML to make predictions.
For installing Simple ML for Sheets, users should go to the Extensions tab, get over the Add-ons options, and get add-ons. From there, finding and installing Simple ML is a fairly simple process.
Bottom Lines
Even though Simple ML is quick and reasonably accurate, users still need to know how to set up their data and read the newly created model to be successful. This new machine learning addon is very beneficial for the users of Google sheets. Hence, explore this wonderful addon of Google sheets and enjoy the best features to grab success in your business. You can find your business operating smoothly with simple ML.
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Google introduces new machine learning add on for Google Sheets - TechiExpert.com
Introduction to machine learning with python – EurekAlert
Machine Learning is one of the approach of Artificial Intelligence in which Machines become capable of drawing intelligent decisions like humans by learning from its past experiences. In classical methods of Artificial Intelligence, step by step instructions are provided to the machines to solve a problem. Machine Learning combines classical methods of Artificial Intelligence with the knowledge of past to gain human like intelligence.
The book Introduction to Machine Learning with Python has made explanation on Machine Learning with Python from basics to the advanced level so as to assist beginners in building strong foundation and develop practical understanding.
The beginners with zero or little knowledge about Machine Learning can gain insight into this subject from this book. This book explains Machine Learning concepts using real life examples implemented in Python.
The book Introduction to Machine Learning with Python present detailed practice exercises for offering a comprehensive introduction to machine learning techniques along with basics of Python. The book leverages algorithms of machine learning in a unique way of describing real life applications. Though not mandatory, some experience with subject knowledge will fasten the learning process.
About the authors:
Dr. Deepti Chopra has done PhD in the area of Natural Language Processing from Banasthali Vidyapith. Currently, she is working as Associate Professor at JIMS Rohini, Sector 5. Dr. Chopra is an author of five books and two MOOCs. Two of her books have been translated into Chinese and one has been translated into Korean. She has 2 Australian Patents and 1 Indian Patent to her credit. Dr. Chopra has several publications in various International Conferences and journals of repute. Her areas of interest include Artificial Intelligence, Natural Language Processing and Computational Linguistics. Her primary research works involve machine translation, information retrieval, and cognitive computing.
Mr. Roopal Khurana is working as Assistant General Manager at Railtel Corporation of India Ltd., IT Park, Shastri Park, Delhi. Currently, he is working in the field of Data Networking, MPLS Technology. He has done BTech in Computer Science and Engineering from GLA University, Mathura, India. He is a technology enthusiast. Previously, he has worked with companies, such as Orange and Bharti Airtel.
Keywords:
Artificial Intelligence, Computer Science and IT, Machine Learning, Deep Learning, Python Programming, Back propagation, Supervised Learning, Scikit Learn, Unsupervised Learning, Numpy, Decision Trees, Matplotlib, Support Vector Machine, pandas, Neural Network, Logistic Regression, Linear regression, Clustering, Jupyter notebook, Classification.
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Anodot Uses Intel Hardware, Software to Improve Performance of … – I-Connect007
Using Intel hardware, Intel Integrated Performance Primitives (Intel IPP) and Intel oneAPI Data Analytics Library (oneDAL), Anodot improved the performance of its autocorrelation function (ACF) and XGBoost algorithms, significantly reducing machine learning (ML) compute time and costs associated with autonomous business monitoring and anomaly detection.
The data analytics company created a solution for its customers that identifies revenue-critical business incidents in real time through models that analyze hundreds of millions of time series metrics every minute. For the anomaly-detection platform, unlimited scalability and effective management of compute costs are needed as it grows in addition to improving upon the speed, efficiency and accuracy of model training and inferencing.
While Anodot already runs its AI platform on Intel CPUs, the team ran performance tests on the Intel Xeon Scalable processor platform in an extended collaboration. Through optimizations to ACF using Intel IPP for anomaly detection, the team recorded up to 127 times faster training performance and a 66% reduction in the overall cost of running the training algorithm in a cloud environment achieved by cutting the ACF runtime by almost 99%. Optimizations to XGBoost algorithms using oneDAL and the baseline XGBoost model for forecasting resulted in 4 times faster inferencing time, as well as enabling the service to analyze 4 times the amount of data at no additional cost for inference.1
When choosing a machine learning platform, you need to think about scale as your business grows, said Ira Cohen, chief data scientist at Anodot. So, model efficiencies and compute cost effectiveness become increasingly important. Our performance tests show the Intel software and Xeon platform provide us efficiency gains that will allow us to deliver an even higher quality of service at lower cost.
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Anodot Uses Intel Hardware, Software to Improve Performance of ... - I-Connect007
Gigster Launches Artificial Intelligence Services Suite to Support Companies at Every Level of AI Readiness – Yahoo Finance
>>Gigster, the leading AI software development platform, announces three new services to help companies see rapid benefits from AI and machine learning no matter what their current level of AI maturity
AUSTIN, Texas, March 20, 2023 /PRNewswire/ -- Innovation and digital transformation firm, Gigster, today announced three new service offerings to help companies speed up their workflows and better leverage data through artificial intelligence. The new services offer specialized teams and proven development processes tailored to the clients' current business needs and level of AI maturity.
Gigster's new offerings include AI Aspire, designed for companies taking their first step into artificial intelligence. AI Infuse provides companies with off the shelf tooling and quick value realization by infusing machine learning into existing workflows. AI Evolve, for organizations already experienced with AI, helps transform businesses through customized, strategic initiatives, augmentation of existing AI teams, and fully-managed AI solutions.
"After years of gradual adoption, we're seeing an unprecedented number of companies ready to adopt AI at scale," said Gigster's VP of Product, Cory Hymel. "We want to ensure that every company can innovate through AI no matter what their current experience level."
Gigster has spent the past decade delivering artificial intelligence and machine learning solutions through its network of over 900 engineers, managers, designers and its own AI-powered development platform. Gigster's data-driven platform analyzes millions of data points to predict delays and bugs before they can affect project timelines, automatically deliver project resources to speed development time, and more quickly assemble teams perfectly matched for specific projects. Their proven processes can assemble AI development teams in less than a week and complete 94% of projects on time and within budget.
The demand for artificial intelligence services has skyrocketed in the past few months due to new solutions and pressure on organizations to improve overall productivity. AI can be used for predictive maintenance, automation of manual processes, automated data generation, pattern recognition and prediction, and more.
"Our fluid, global workforce democratizes access to technology like AI so companies can innovate at scale without needing to support large, in-house data science teams," said Hymel. "As the tech space changes rapidly and every organization works to keep up, having access to a team that is already set up and ready to innovate can be a huge differentiator."
In addition to AI Aspire, AI Infuse, and AI Evolve, Gigster recently launched their fully-managed solution for enterprise ChatGPT integrations. For more information on their new AI service offerings and to get updates on how Gigster is transforming companies through AI initiatives, visit Gigster.
For more information, please visit http://www.gigster.com or follow @trygigster on Twitter.
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