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Modern data quality practices utilize advanced technologies, automation, and machine learning to manage diverse data sources, prioritize governance, and ensure real-time processing. Contrasting with traditional methods, modern approaches encompass a broader range of data types, including unstructured and external data. They address the challenges of processing massive data volumes through technologies like cloud computing, emphasizing real-time processing over batch mode. Collaboration across stakeholders, treating data quality as a continuous process, distinguishes modern practices, ensuring accurate, reliable data for informed decision-making and business success.

The Density Kernel Depth (DKD) method emerges as a crucial tool for outlier detection in functional data, addressing the challenges posed by complex curves and surfaces. Unlike traditional approaches suited for scalar or multivariate data, functional data, represented by continuous curves, requires advanced techniques. DKD compares the density of each curve to the overall dataset density using non-parametric kernel methods. This yields a depth value for each curve, facilitating efficient outlier detection. DKD's flexibility, interpretability, and computational efficiency make it suitable for diverse functional data structures. In practical scenarios, such as analyzing patient heart rate curves, DKD offers a holistic perspective, detecting entire abnormal curves for more comprehensive insights. As data complexity grows, DKD exemplifies a promising tool for navigating intricate functional data landscapes, paving the way for sophisticated analysis techniques.

The UK, US, EU, Australia, and China have signed the Bletchley declaration, acknowledging the potentially catastrophic risks posed by artificial intelligence (AI). Twenty-eight governments agreed to collaborate on AI safety research during the AI safety summit hosted by the British government. The declaration recognizes the potential harm stemming from advanced AI systems and emphasizes the need for collective global efforts in addressing these risks. UK Technology Secretary Michelle Donelan highlighted the importance of collectively examining risks around frontier AI. The diplomatic success marks a step toward global unity, with South Korea and France set to host future AI summits. However, international consensus on global AI regulations remains elusive.

The evolving landscape of transaction monitoring in the realm of financial institutions calls for a shift from traditional, wide-net approaches to a more effective and efficient focus on identifying suspicious activities. Technological advancements, including AI and machine learning, are key in this transition, complementing rules-based detection methods. The future of transaction monitoring involves expanding rule-based detection, making previously unseen connections, fostering cross-industry AML collaboration, and continuous optimization of detection processes. Organizational transformation will see a reduction in false positives handling, an increase in alert complexity, a demand for senior-level investigators, and improved compliance accuracy. Advanced analytics are set to revolutionize the fight against financial crime.

Data engineering and machine learning pipelines serve distinct yet interconnected roles in data-driven organizations. Data pipelines focus on collecting, cleaning, integrating, and storing data, forming the foundation for informed decision-making. Machine learning pipelines, in contrast, encompass data cleaning, feature engineering, model training, evaluation, deployment, and monitoring, enabling automation of ML model development. While data pipelines follow a linear path from data source to storage, ML pipelines operate in a circular fashion, iterating from data to model deployment. Both require adaptable computational resources to handle variable workloads. Understanding these differences is crucial for building and maintaining effective pipelines, ensuring timely, accurate data for informed decisions and improved performance.

To accelerate analytics and achieve faster insights from data, IT leaders are turning to modern data pipelines with flexible infrastructure. Automation and orchestration play a key role in breaking down data silos and ensuring efficient data flow. The choice of infrastructure, like Pure Storage, with advanced data persistence and storage capabilities, is crucial. Such solutions support real-time analytics, scalability, and agility, reducing energy consumption. IT leaders are urged to modernize both infrastructure and applications to fully harness the potential of data-driven decision-making. The combination of modern data pipelines and the right storage solution enables businesses to break through silos and gain real-time insights, fueling digital transformation.

In this article, the author explores the limitations of modern artificial intelligence (AI) and introduces the concept of "living AI." They argue that current neural networks lack the ability to mimic the dynamic and adaptable nature of biological neural networks. The article highlights recent scientific findings about the role of transmembrane proteins in neural plasticity, suggesting that neurons are not just signal conductors but can change their response to incoming signals in real time. To address this limitation, the author proposes a new mathematical neuron with a variable dynamic position function, akin to the axon initial segment in biological neurons. This innovation would enable AI to create its own attitude towards sensory information, make mistakes, and learn, gradually forming its own character and preference matrix. The author envisions AI built around a neural matrix that behaves like a living organism, offering potential applications in creating personalized AI with human-like characteristics. The article concludes that AI based on matrix neural networks could evolve into a digital form of life, actively participating in the real world.

Artificial intelligence (AI) has revolutionized digital advertising, offering a data-driven approach that empowers advertisers. AI's generative capabilities streamline content creation, while its analytical prowess dissects consumer behavior for precise targeting. Real-time optimization ensures campaigns are fine-tuned on the fly, reducing ad spend waste. Additionally, AI guides strategic decision-making by forecasting trends and offering market insights. The human-AI partnership is the future, with AI serving as a co-pilot to optimize campaigns while retaining the human touch in marketing. In the ever-evolving digital landscape, AI in advertising brings efficiency, personalization, and strategic advantage, ultimately driving success in the industry.

Application mapping," or application dependency mapping, is a critical process in managing complex big data ecosystems. It involves visualizing the intricate relationships and dependencies between various software applications and the underlying IT infrastructure. Application mapping enhances data lineage, performance optimization, collaboration, and risk management in big data environments. By offering a holistic view of system architecture, it facilitates decision-making, troubleshooting, and compliance efforts. Automated discovery tools and manual annotation are used for mapping, with hierarchical mapping helping to understand dependencies. Best practices include starting at a high level, drilling down for details, documenting data flows, incorporating metadata, and identifying single points of failure. In sum, effective application mapping is essential for managing and optimizing big data environments.

Data Governance and Data Management are often used interchangeably but have distinct roles in handling an organization's data. Data Management encompasses data capture, storage, transformation, and usage, aligning with the business strategy. It involves various roles collaborating to ensure data quality and compliance. Data Governance, a subset of Data Management, coordinates people, technology, and activities to integrate and ensure legal compliance. While both focus on data quality, integration, and standards, Data Management includes technical details, handled by IT experts, while Data Governance emphasizes high-level decision-making by business stakeholders. Combining these approaches can lead to more effective data management and governance, preventing miscommunication and scope creep.

WebSummit 2022 in Lisbon welcomed 71.033 attendees from 160 countries. Most of the presented companies are "start ups", companies in the initial stage of development and looking for investors. This is one of the central objectives of the event: to help put in contact, on the one hand, those who have projects and ideas, but need investment, and, on the other hand, those who have money to invest and are looking for good opportunities to monetize that capital.

In the spotlight, as always, were multiple conferences on different stages and formats, total around 1.000 speakers, including Brad Smith - the president of Microsoft, and Changpeng Zhao - the Chinese-Canadian entrepreneur who founded and leads Binance, the cryptocurrency giant.

Among the Portuguese speakers were several political leaders, such as Marcelo Rebelo de Sousa, António Costa, Carlos Moedas, Fernando Medina, in addition to other figures from different sectors, from the outset leaders of large companies such as the executive president of TAP, the president of GALP, the CEO of EDP.

WebSummit’s educational content, informative masterclasses, and innovative roundtables are giving valuable skills to attendees. WebSummit “attracts a diverse list of world-class speakers and facilitates content that inspires and empowers attendees.”

Tickets for WebSummit 2023 are already available on the official website of summit.

We are participating at Portugal Smart Cities Summit. Come and meet us at FIL - Parque das Nações - October 11-13, Pavilion 04 - Stand 4A04.

The “Smart Cities” Concept encompasses more than mobility, digital platforms or sustainability. The fundamental objective of a Smart City is the incorporation of all these areas in order to improve the lives of citizens in the world. PORTUGAL SMART CITIES SUMMIT is the place of convergence and the physical marketplace for creating opportunities for the national and international market.

Opportunities linked to research and education that provide business creation in the most diverse sectors. Opportunities to reflect on the future of organizing cities in the world.

Today’s businesses need a ton of information, analysis, and market studies to make the best possible decisions.

The most crucial skills for the labor market of today are digital ones, which require constant reskilling. By 2022, the World Economic Forum estimates that at least 54% of all workers will need to upgrade their digital skills.

To enable data analysis and predict the future for efficient decision-making techniques, organizations use AI tools on a bigger scale. A PwC study found that 86% of people consider AI to be a common technology. This is due to AI tools have developed into a driving force for technical advancements including Machine Learning (ML), Predictive Analytics, Natural Language Processing (NLP), Data Labeling, and many more. (see the concepts in our library/Artificial Intelligence – General Description)

Every firm can benefit from using AI tools to increase revenue; for instance, it can assist organizations to find their weak points and make precise forecasts about competitors, market trends, and prospects. AI decision-making is superior to human decision-making in that it is devoid of bias and emotion.

Scientists from Tokyo Institute of Technology instead of predicting the smell from molecular data, developed the method which predicts molecular features based on the odor impression. This is achieved using standard mass spectrum data and machine learning models. ML (see concept in our library/Machine Learning – General Description)

The mass spectra of odor mixtures is obtained by a linear combination of the mass spectra of single components. This simple method allows for the quick preparation of the predicted spectra of odor mixtures and can also predict the required mixing ratio, an important part of the recipe for new odor preparation.

This novel method described in this study can provide highly accurate predictions of the physicochemical properties of odor mixtures, as well as the mixing ratios required to prepare them, thereby opening the door to endless tailor-made fragrances.

It would be also interested for the food, health, beauty, and wellness industries, where odors and fragrances are of keen interest.

The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson’s from their nocturnal breathing — i.e., breathing patterns that occur while sleeping.

Over the years, researchers have investigated the potential of detecting Parkinson’s using cerebrospinal fluid and neuroimaging, but such methods are invasive, costly, and require access to specialized medical centers, making them unsuitable for frequent testing that could otherwise provide early diagnosis or continuous tracking of disease progression.

The researchers demonstrated that the artificial intelligence assessment of Parkinson's can be done every night at home while the person is asleep and without touching their body.

This study is likely one of the largest sleep studies ever conducted on Parkinson’s.

Data warehouses are widely used by organizations of all sizes. Nowadays one of the big decisions for an organization that's looking to deploy a data warehouse is whether to put it on premises or in the cloud. Both ways have their advantages and disadvantages.

An enterprise data warehouse stores data from all of an organization's business operations in a single, centralized platform, which is handling by in-house administrator and IT-team.

A big challenge for on-premises data warehouses is the need to deploy a hardware and software computing environment.

One of the benefits of a cloud data warehouse is that the vendor assumes responsibility for the underlying architecture.

The cost of deploying and supporting a data warehouse system in an on-premises data center usually will be much higher than renting one from a cloud provider with usage-based payments.

But cloud data warehouses can also pose some challenges to organizations and require changes in IT processes.

Here in the article, there are more details on the pluses and minuses of two approaches in some key areas.

AI/ML use cases in the supply chain help retailers, suppliers and distributors drive transformational changes that are so much needed today. Machine Learning delivers unprecedented value to supply chain operations: from cost savings through reduced operational overhead and risk mitigation, to enhanced supply chain forecasting, speedy deliveries, and improved customer service, to name a few. Machine learning is also providing insights into where automation can deliver the most significant scale advantages.

Some use cases of AI in SCM:

-Predicting customer’s behaviour

-Predictive capabilities are helping demand forecasting

-Avoiding charge-back risks

-Smart warehouses

-Identifying quality issues in line production at the early stages, etc.

The average cost of a food recall due to bacterial or microbial contamination is US$10 million. AI can help optimize the current systems used in the food processing industry.

The power of AI and machine learning is the predictive ability; it can help the food industry take steps earlier to reduce the occurrence of foodborne disease in their operations.

The idea is to get machines to think like the human brain. So that the current errors and inefficiencies from human intervention—which may at times compromise food safety—can be predicted by AI and ultimately decreased or eliminated.

Another promising application is using AI to analyze the behavioral data of human workers in the processing line. In practice, this could mean workers fill in a quick, online weekly survey about their workplace culture. Natural language processing would then be used to analyze their answers and create a quantitative measure of food safety culture. The company could predict the likelihood of food safety issues by looking at the trends in food safety culture and combining them with the other food safety related data, and take timely steps to reduce the chance of an outbreak.

With AI advancing at an exponential rate, it’s become possible for companies to use artificial intelligence to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading to increased efficiency in customer service throughout different industry vertices.

Machine learning and AI-based interactive voice response systems have created a new paradigm for what customers and customer service agents can expect from these technologies. When applied correctly, artificial intelligence will enhance the customer experience in various ways, from identifying their interests through sentiment analysis to gathering data about their preferences.

In a short term AiTecServ will release AIsense solution based on this technology.

NLP (see concept in our library/artificial intelligence – Natural Language Processing/Common Tasks)

Two of the most innovative and disruptive technologies in use today are data science and blockchain technology. Data science examines and explains the unprocessed data to comprehend how a system functions. A cutting-edge method of recording transactions and storing financial data is blockchain technology. These two ideas have been used to create amazing advances in a variety of fields, including banking and software development.

Possibilities of Blockchain important for data science:

-Enables data tracing

-The capability of real-time analysis

-Ensures the data’s accuracy

-Lessens the difficulty of data exchange

-Builds trust

-Improving data integrity

Data is the foundation of blockchain technology.

The new platform reportedly enables Vodafone’s data scientists and developers to slash the time of going from proof-of-concept to production from five months to just four weeks.

Vodafone highlighted four key features of AI Booster:

-Automated ML lifecycle compliance activities (drift/skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services.

-Embedded security by design.

-Capitalise on Google-native ML tooling using BQML, AutoML, Vertex AI and others.

-Boost adoption through standardised and embedded ML templates.

The AI system consists of a sensor in the ball that relays its position on the field 500 times a second, and 12 tracking cameras mounted underneath the roof of stadiums, which use machine learning to track 29 points in players’ bodies.

Software will combine this data to generate automated alerts when players commit offside offenses. Alerts will be sent to officials in a nearby control room, who will validate the decision and tell referees on the field what call to make.

FIFA claims this process will happen “within a few seconds and means that offside decisions can be made faster and more accurately.” The data generated by the cameras and ball will also be used to create automated animations, which can be played on screens in the stadium and in TV broadcasts “to inform all spectators in the clearest possible way” of why the call was made.

The 2022 World Cup will take place in Qatar, making it the first World Cup to ever be hosted in an Arabic country. To offset the heat of Qatar, the tournament will be held from November to December instead of in the summer, as is tradition.

The universities had created a tool that measures the electricity usage of any machine-learning program. With it, Azure users building new models can view the total electricity consumed by graphics processing units (GPUs)—computer chips specialized for running calculations in parallel—during every phase of their project, from selecting a model to training it and putting it to use. It’s the first major cloud provider to give users access to information about the energy impact of their machine-learning programs.

The new Azure tool, which debuted in October, currently reports energy use, not emissions. So now researchers figured out how to map energy use to emissions, and they presented a companion paper on that work at FAccT, a major computer science conference, in late June. Researchers used a service called Watttime to estimate emissions based on the zip codes of cloud servers running 11 machine-learning models.

They found that emissions can be significantly reduced if researchers use servers in specific geographic locations and at certain times of day. Emissions from training small machine-learning models can be reduced up to 80% if the training starts at times when more renewable electricity is available on the grid, while emissions from large models can be reduced over 20% if the training work is paused when renewable electricity is scarce and restarted when it’s more plentiful.

Here are some of the best deep learning tools. Though this list is by no means exhaustive! There are more deep learning tools in the market but these are the ones that are used the most.

1. H2O.ai

H2O is a deep learning tool that was built from scratch in Java and follows a seamless integration with other open source products like Apache Hadoop and Spark. It has an easy to use Web UI and is massively scalable in big data analysis.

2. TensorFlow

TensorFlow is a deep learning tool that was written in highly-optimized C++ and CUDA (Nvidia’s language for programming GPUs) and provides an interface to languages like Python, Java, Go. It is an open-source library that was developed by the tech giant, Google, for the smooth running of deep learning applications.

TensorFlow makes it fairly easy for beginners and even experts to create machine learning models for mobile, web, desktop, and cloud.

3. Keras

Keras is a high-level neural network API that is capable of running on top of TensorFlow or Theano. It is written in Python and was developed mainly on enabling faster experimentation. Keras deep learning library allows the user for easier and faster prototyping with the use of modularity, minimalism and easy extensibility.

4. Caffe

Caffe is a deep learning tool whose framework is built with expression, speed and modularity in consideration. Developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors, it is also an open source deep learning tool.

5. DeepLearningKit

DeepLearningKit is an open source deep learning tool for Apple’s iOS, OS X, tvOS, etc. The main idea behind its creation was to support pre-trained models on all the Apple devices that have a GPU. This deep learning tool is developed in Swift and can be used on device GPU to perform low-latency deep learning calculations. DeepLearningKit supports Convolutional Neural Networks too. Its vision is to support other deep learning tools like Torch and TensorFlow.

6. Torch

Torch is an efficient open source program that uses a LuaJIT scripting language and C/CUDA implementation. If you are using this deep learning tool, you will be able to take advantage of its powerful features like: multiple routines for indexing, transposing, slicing, amazing interface to C via LuaJIT, neural network. It offers fast, efficient GPU support and is easily embeddable that makes it easier to work with iOS, Android, FPGA.

7. Theano

Theano is a Python library that is used to evaluate mathematical operations including multi-dimensional arrays. It is mainly used in building deep learning projects and is way faster on a GPU than a CPU. With its use, it is possible to attain speed that rivals self-made C implementations for problems that involve large amounts of data.

AutoML (Automated Machine Learning) automates certain key components of the machine learning pipeline. Some aspects of the machine learning pipeline are automated using AutoML (TPOT, Auto-Sklearn, AutoViML, AutoKeras and many more).

Advantages of AutoML are Accessibility, Efficiency, Fewer Errors, Cost savings, Meet Industry Demands.

But data science jobs would not be eliminated by a lot of reasons.

1.While AutoMLs are capable of selecting models in the majority of cases, they are still unable of doing the majority of a Data Scientist’s labour. We still require data scientists/analysts to apply their domain expertise to develop more meaningful features and information that affect the intended outcome (Feature Engineering).

2.AutoML will not replace most data science professions; rather, it will assist experts in completing their assignments more quickly.

3.Machines aren’t smart enough, and algorithms frequently fail to generalize and comprehend the context of an issue.

4.While AutoML can assist us in finding a good model for a specific problem, it cannot create a novel approach, which is frequently necessary for emergent real-world challenges.

AutoML will continue to grow in popularity. Certain duties will almost certainly be mechanised, and certain responsibilities will almost certainly be eliminated. Because a deep learning model has many parameters, the AutoML framework may speed up a data scientist’s work and deliver the optimal deep learning model in a shorter amount of time.

As businesses do cloud migration, they are moving away from their old on-premises data warehousing technologies, to these new cloud data platforms. The transformation is a huge shift in data management and has profound implications for businesses. Businesses require a strong knowledge of cloud architectures, data lakes, storage options, and cloud subscription models to make technology choices. Cloud data warehouse solution providers have integrated data science and analytic tools hosted directly inside of their data warehouse offerings. Companies shifting towards the cloud must consider these trends:

-iPaaS (Integration Platform-as-a-Service) for Integration

-NoSQL for Big Data

-Data Marts For Quick Data Access

-Column-Storage For Advanced Analytical Query

-DW Automation for Deploying Code

The cloud migration of data warehouses brings all the benefits of cloud-like agility, cost, effectiveness, scalability, and performance. With cloud data warehouse automation, your team can quickly keep up with new infrastructure and migration projects and increase development and operations capabilities. This is the best way to maximize your organization’s return on cloud investment.

New software based on Machine Learning concept that helps drivers find parking spaces will also incentivize drivers to cooperate with local councils in their quest to keep pollution within safe limits in busy urban centers, as part of a far-reaching program designed to reduce toxic air in city centers.

The new project will use the latest AI technology to create services that allow local authorities to analyze vast amounts of data on driver behavior and to better control local travel patterns.

Imagine you are traveling into town on a Thursday morning and without you knowing it, your car is the one engine that triggers the town to go over the allowed pollution level, resulting in a big fine for the local government. Now imagine that instead of this happening, you receive a suggestion to park in another, better place, and you are issued a free parking space. You're also shown a low-traffic route to your free parking space. The whole service would be tailored to your individual needs while also helping towards net-zero objectives.

It will form an intrinsic part of the software development strategy for the future, allowing to expand capabilities and more importantly reduce pollution and manage mobility supply and demand in real-time.

These is a list of books made at Marketing AI Institute to understand how artificial intelligence is changing life, business, and the world.

The books are listed according to a "reading tracks" depending on what you may be looking to achieve with AI.

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

Prediction Machines: The Simple Economics of Artificial Intelligence

You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place

Marketing Artificial Intelligence: AI, Marketing, and the Future of Business

The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You

The AI Marketing Canvas: A Five Stage Roadmap to Implementing Artificial Intelligence in Marketing

The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

Applied Artificial Intelligence: A Handbook for Business Leaders

Voice Strategy: Creating Useful & Usable Voice Experiences

Human + Machine: Reimagining Work in the Age of AI

Artificial Intelligence: A Modern Approach

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

How to Create a Mind: The Secret of Human Thought Revealed

Deep Learning

Homo Deus: A Brief History of Tomorrow

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

AI Superpowers: China, Silicon Valley, and the New World Order

Superintelligence: Paths, Dangers, Strategies

The system, uses deep learning (see concept in our library/artificial intelligence – Computer vision/Some Concepts), reads live camera footage and adapts the lights to compensate, keeping the traffic flowing and reducing congestion.

In testing, the system significantly outperformed all other methods, which typically rely on manually-designed phase transitions.

Because the AI "sees" high traffic volume before the cars have gone through the lights and makes its decision then, it is more responsive and can react more quickly.

The program can be set up to view any traffic junction—real or simulated—and will start learning autonomously. The reward system can be manipulated, for example to encourage the program to let emergency vehicles through quickly. But the program always teaches itself, rather than being programmed with specific instructions.

The researchers hope to begin testing their system on real roads this year.

Machine learning is making great strides in healthcare to make sure patients get the best services. Now we could talk about 9 main areas:

-Precision medicine and personalisation of healthcare

-Categorisation applications

-Analysing imaging

-Claims and payment administration

-Other administrative processes

-Prediction and health policy

-Electronic health records

-Diagnosis and treatment

-Drug development

Long-term outcomes could include better quality of care and a more efficient and cost-effective healthcare system, which can only benefit patients, providers, insurers, regulators, and policymakers.

Supercomputers help to study the causes and effects—usually in that order—of complex phenomena. However, scientists occasionally need to deduce the origins of scientific phenomena based on observable results. These so-called inverse problems are notoriously difficult to solve, especially when the amount of data that must be analyzed outgrows traditional machine-learning tools.

To reduce coordinator-worker communication, which often involves repeating the same requests multiple times, the team introduced a response cache that stores the metadata from each request. The first new strategy was this caching approach, which allows to immediately recognize and automatically calculate familiar requests.

The second new technique involves grouping the mathematical operations of multiple DNN models, which streamlines tasks and improves scaling efficiency—the total number of images processed per training step—by taking advantage of the similarities in each model's calculations. This process leads to significant improvements in power usage as well.

By strategically grouping these models, the team aims to eventually train a single model on multiple GPUs and achieve the same efficiency obtained when training one model per GPU.

Many companies don’t know how to get started on their AI journeys. But now AI industry makes the quality of data a priority. AI cannot thrive when it’s not trusted. Explainable AI (XAI) is a partial solution to the problem. XAI is an artificial intelligence in which the results of the solution can be understood by humans.

So, what is expected:

-a large number of acquisitions of startups specializing in synthetic data training;

-a lot of companies will smoothly incorporate AI in their work;

-the migration of AI from the cloud to the edge will accelerate;

-human input to lead to more useful AI decisions;

-enterprises will favor AI solutions that avoid lock-in;

-chatbots will get much closer to offering a human-like experience.

Deep-learning algorithms to accurately predict how changes in measurements could indicate forthcoming earthquakes—a task that has confounded experts for centuries.

Deep-learning algorithms are particularly useful for earthquake monitoring because they can take the burden off human seismologists.

Deep-learning could make the process of predict quicker, and more accurate, by helping to cut through large volumes of data. The model uncovers quakes that would previously have been dismissed as human-generated vibrations.

In the Machine Learning in Manufacturing Market study 2022, the industry landscape is covered from driving factors to upstream markets and the overall state of the market. An in-depth analysis of the overall growth prospects for the global and regional market was provided which was based on an in-depth analysis of key industry players, primary and secondary data.

The report contains analysis of companies Intel, IBM, Siemens, GE, Google, Microsoft, Micron Technology, Amazon Web Services (AWS), Nvidia, Sight Machine, with 114 Pages Report.

Machine Learning in manufacturing can be used in visual quality control. Harnessing the power of image recognition and deep learning may significantly reduce the cost of visual quality control while also boosting overall process efficiency.

Analysts presently use artificial intelligence to distinguish the environmental and epidemiological examples that lead up to scourges to foresee flare-ups before they occur. Numerical models and AI can investigate enormous measures of information to get hints about a probable wellspring of contamination. And help in the prevention of the spreading of disease through AI.

Extraordinary difficulties require incredible devices. Furthermore, in reality, as we know it, where an infection, for example, SARS-CoV-2 has had an enormous worldwide effect is simply a matter of months, its intricacy and speed require new strategies for tracking down arrangements. Progressive types of man-made consciousness (man-made intelligence) are as of now assuming a basic part in our pandemic-battling endeavours, and they’re supposed to assume an inexorably fundamental part in the conflict against irresistible infection later on. We see simulated intelligence propelling our irresistible sickness battling abilities across the accompanying aspects:

• Determination
• Wellbeing
• Treatment
• Relief

The world’s first print and digital publication focused on AI, Big Data and Analytics ranks top 10 Machine Learning books for business leaders. The Analytics Insight gathered extensive guides, academic studies, quick read works and even ones with economic and political mentions, such as the rise of China’s tech giants like Alibaba. There is also marketing insights and real-life examples from successful entrepreneurs.

.1 Applied Artificial Intelligence: A Handbook for Business Leaders-
.2 Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data-Driven Technologies-
.3 AI Superpowers: China, Silicon Valley, and the New World Order-
.4 Artificial Intelligence: The Insights You Need from Harvard Business Review-
.5 The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World-
.6 Artificial Intelligence and Machine Learning for Business-
.7 AI for People and Business-
.8 Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions-
.9 Artificial Intelligence for Marketing: Practical Applications-
.10 The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You-

In the article, the author explains the origins, details and exemplifies the process of one deep learning application: convolutional neural network (CNN) which helps computers detect objects. In addition, he briefly covers its variations, such as Region-based Convolutional Neural Network (R-CNN), Fast R-CNN and Faster R-CNN. In conclusion, the specialist mentions the latest study about it: You Only Look Once (YOLO).

In this article published in bdtechtalks.com, deep learning pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun discuss a future in which deep learning models can learn with little or no help from humans. Deep learning can be described as artificial neural networks in which it mimics brain funcionality. The specialists highlighted a few subsets such as Supervised, Reinforcement, Contrastive and System 2 learning, alongside to pros and cons. The considerations were published this month in the paper “Deep Learning for AI”, in the Communications of the ACM journal.

O futuro do Deep Learning, de acordo com seus pioneiros, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun

As timing is critical when it comes to delivering vaccines, carriers and airlines are using AI to overcome logistical difficulties of COVID-19 vaccine distribution. The author gather great examples of how AI has helped to optimize vaccine transportation during the Covid-19 pandemic. For example, it was used to increase fuel efficiency by tracking drivers behaviour, predict land or air traffic using historical data to estimate how long it will arrive in specific locations. Besides, it allowed the temperature of the vaccine to be monitored throughout the delivery journey, using Bluetooth intelligent mesh technology.