Category Archives: Machine Learning

Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality of life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation doses to the bladder and rectum. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and to develop dosage thresholds for each organ region. Our findings show no statistically significant association between the bladder and quality-of-life scores. However, we found a statistically significant association between the radiation applied to posterior and anterior rectal regions and changes in quality of life. Finally, we estimated radiation therapy dose thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.

Computers in biology and medicine. 2020 Nov 28 [Epub ahead of print]

Zhijian Yang, Daniel Olszewski, Chujun He, Giulia Pintea, Jun Lian, Tom Chou, Ronald C Chen, Blerta Shtylla

New York University, New York, NY, 10012, USA; Applied Mathematics and Computational Science Program, University of Pennsylvania, Philadelphia, PA, 19104, USA., Carroll College, Helena, MT, 59625, USA; Computer, Information Science and Engineering Department, University of Florida, Gainesville, FL, 32611, USA., Smith College, Northampton, MA, 01063, USA., Simmons University, Boston, MA, USA; Department of Psychology, Tufts University, Boston, MA, 02111, USA., Department of Radiation Oncology, The University of North Carolina, Chapel Hill, NC, 27599, USA., Depts. of Computational Medicine and Mathematics, UCLA, Los Angeles, CA, 90095-1766, USA., Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS, 66160, USA., Department of Mathematics, Pomona College, Claremont, CA, 91711, USA; Early Clinical Development, Pfizer Worldwide Research, Development, and Medical, Pfizer Inc, San Diego, CA, 92121, USA. Electronic address: .

PubMed http://www.ncbi.nlm.nih.gov/pubmed/33333364

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Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. - UroToday

Machine-learning processes are invaluable at mining data for patterns in oil and gas production, but are generally limited in interpreting the information for decision-making needs.

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Both a machine-learning algorithm and an engineer can predict if a bridge is going to collapse when they are given data that shows a failure might happen. Engineers can interpret the data based on their knowledge of physics, stresses and other factors, and state why they think the bridge is going to collapse. Machine-learning algorithms generally cant give an explanation of why a system would fail because they are limited in terms of interpretability based on scientific knowledge.

Since machine-learning algorithms are tremendously useful in many engineering areas, such as complex oil and gas processes, Petroleum Engineering Professor Akhil Datta-Gupta is leading Texas A&M Universitys participation in a multi-university and national laboratory project to reduce this limitation. The project began Sept. 2 and was initially funded by the U.S. Department of Energy (DOE). He and the other participants will inject science-informed decision-making into machine-learning systems, creating an advanced evaluation system that can assist with the interpretation of reservoir production processes and conditions while they happen.

Hydraulic fracturing operations are complex. Data is continually recorded during production processes so it can be evaluated and modeled to simulate what happens in a reservoir during the injection and recovery processes. However, these simulations are time-consuming to make, meaning they are not available during production and are more of a reference or learning tool for the next operation.

Enhanced by Datta-Guptas fast marching method, machine-learning systems can quickly compress data so they can render how fluid movements change in a reservoir during actual production processes.

Courtesy of Akhil Datta-Gupta

The DOE project will create an advanced system that will quickly sift data produced during hydraulic fracturing operations through physics-enhanced machine-learning algorithms, which will filter the outcomes using past observed experiences, and then render near real-time changes to reservoir conditions during oil recovery operations. These rapid visual evaluations will allow oil and gas operators to see, understand and effectively respond to real-time situations. The time advantage permits maximum production in areas that positively respond to fracturing, and stops unnecessary well drilling in areas that show limited response to fracturing.

It takes considerable effort to determine what changes occur in the reservoir, said Datta-Gupta, a University Distinguished Professor and Texas A&M Engineering Experiment Station researcher. This is why speed becomes critical. We are trying to do a near real-time analysis of the data, so engineering operations can make decisions almost on the fly.

The Texas A&M teams first step will focus on evaluating shale oil and gas field tests sponsored with DOE funding and identifying the machine-learning systems to use as the platform for the project. Next, they will upgrade these systems to merge multiple types of reservoir data, both actual and synthetic, and evaluate each system on how well it visualizes underground conditions compared to known outcomes.

At this point, Datta-Guptas research related to the fast marching method (FMM) for fluid front tracking will be added to speed up the systems visual calculations. FMM can rapidly sift through, track and compress massive amounts of data in order to transform the 3D aspect of reservoir fluid movements into a one-dimensional form. This reduction in complexity allows for the simpler, and faster, imaging.

Using known results from recovery processes in actual reservoirs, the researchers will train the system to understand changes the data inputs represent. The system will simulate everyday information, like fluid flow direction and fracture growth and interactions, and show how fast reservoir conditions change during actual production processes.

We are not the first to use machine-learning in petroleum engineering, Datta-Gupta said. But we are pioneering this enhancement, which is not like the usual input-output relationship. We want complex answers, ones we can interpret to get insights and predictions without compromising speed or production time. I find this very exciting.

Excerpt from:
Enhancing Machine-Learning Capabilities In Oil And Gas Production - Texas A&M University Today

Swami Sivasubramanian, vice-president of machine learning at AWS, spoke about the five tenets of innovation that AWS strives towards while announcing new machine learning tools, during AWS re:Invent

AWS vice-president of machine learning, Swami Sivasubramanian, announced new machine learning capabilities during re:Invent

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs.

With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools.

Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity. The technology has provided foundations for autonomous vehicles and robotic communication, among other budding spaces. One drawback of machine learning, however, is that a single framework is yet to be established for all practitioners, with Tensorflow, Pytorch and Mxnet being the main three.

AWS SageMaker, the cloud service providers machine learning service, has been able to speed up training processes. During the keynote, availability of faster distribution training on Amazon SageMaker was announced, which is predicted to complete training up to 40% faster than before and can allow for completion in the space of a few hours.

This article explores the ways in which Kubernetes enhances the use of machine learning (ML) within the enterprise. Read here

From preparing and optimising data and algorithms to training and deployment, machine learning training can be time-consuming and costly. AWS released SageMaker in 2017 to break down barriers for budding data engineers.

Following its predecessor, SageMaker, Data Wrangler was launched during re:Invent to accelerate data preparation, which commonly takes up most of the time spent on training machine learning algorithms. This tool allows for the preparation of data from multiple sources without the need to write code. With more than 300 data transformations, Data Wrangler can cut the time taken to aggregate and prepare data from weeks to minutes.

To then make it even easier for builders to reach their project goals in the quickest time possible, the Sagemaker Feature Store was launched, which allows features to stay in sync with each other and aggregate data faster.

Sagemaker Pipelines is another new tool which allows developers to leverage end-to-end continuous integration and delivery.

There is also a need to understand and eradicate biases, and in response to this, AWS announced Sagemaker Clarify. This tool works in four steps; by detecting bias during analyses with algorithms before delivering a report which allows steps to be taken; models are checked for unbalanced data, and once deployed, a report is given for each input for prediction, which helps to provide information to customers. Bias detection can be carried out over time, with notifications being given if any bias is found.

As artificial intelligence becomes more prevalent throughout business and society, companies need to be mindful of human bias creeping into their machine models. Richard Downs, UK director at Applause discusses how businesses can use the wisdom of crowds to source the diverse set of data and inputs needed to train algorithms. Read here

John Loughlin, chief technologist in data and analytics at Cloudreach, said: The Clarify product really caught my eye, because bias is an important problem that we need to address, so that people maintain their trust in these kinds of technology. We dont want adoption to be impeded because models arent doing what theyre supposed to.

Also announced during the keynote was deep profiling for Sagemaker Debugger, which allows builders to monitor performance in order to move the training process along faster.

With the aim of making machine learning accessible to as many builders as possible, SageMaker Autopilot was introduced last year to provide recommendations on the best models for any project. The tool features added visibility, showing users how models are built, and ranking models using a leaderboard, before one is decided on.

Integration of this kind of technology for databases, data warehouses, data lakes and business intelligence (BI) tools were referred to as future frontiers that customers have been demanding, and machine learning tools were announced for Redshift and Neptune during the keynote. While capabilities for Redshift make it possible to get predictions for data warehouses starting from a SQL query, ML for Neptune can make predictions for connected datasets without the need for prior experience in using the technology.

Brad Campbell, chief technologist in platform development at Cloudreach, said: What stands out when I look at ML for Redshift is that what you have in Redshift, which you dont get in other data sources, is the true composite of your businesss end-to-end value chain in one place.

Typically when Ive worked in Redshift, there was a lot of ETL work to be done, but with ML, this can really unlock value for people who have all this end-to-end value chain data coalesced in a data warehouse.

Another recently launched tool, Amazon Quicksight ML, provides stories of data dashboards in natural language, cutting the time spent on gaining business intelligence information from days or weeks to seconds. The tool takes into consideration the different terms that various departments within an organisation may use, meaning that the tool can be used by any member of staff, regardless of the department they work in.

Kevin Davis, cloud strategist at Cloudreach, said: There is another push in this area to lower the bar of entry for ML consumption in the business space. There is a broadening of scope for people who can implement these services, and a lot of horizontal integration for ML capabilities, along with some deep vertical implementation capabilities.

Yair Green, CTO at GlobalDots, explains how artificial intelligence and machine learning changed the Software-as-a-Service industry. Read here

Without considering problems that the business needs to solve, no project can be truly successful. According to Sivasubramanian, any good machine learning problem to focus on is rich in data, impacts the business, but cant be solved using traditional methods.

AI-powered tools such as Code Guru, DevOps Guru, Connect and Kendra from AWS allow staff to quickly solve business problems that arise within DevOps, call centres and intelligent search services, which can range from performance issues to customer complaints.

During the keynote, the launch of Amazon Lookout for Metrics was announced, which will allow developers to find anomalies within their machine learning models, with the tool ranking them according to severity. This ensures that models are working as they should be.

The caveat I have around Lookout for Metrics is that its clearly directed, and intended to look at the most common business insights, said Davis.

In terms of generally lowering the bar of entry, you can potentially put this in the hands of business analysts that are familiar enough with SQL queries, and allow them to directly pull insights or anomalies from business data stores.

For the healthcare sector, AWS also announced the launch of Amazon Healthlake, which provides an analysis of patient data that would otherwise be difficult to make conclusions on due to its usually unstructured nature.

Commenting on the release of Amazon Healthlake, Samir Luheshi, chief technologist in application modernisation at Cloudreach, said: Healthlake stands out as very interesting. There are a lot of challenges around managing HIPAA and EU GDPR, and its not an easy lift, so Id be interested to see how extra layers can be applied to this to make it suitable for consumption in Europe.

Andrew Pellegrino, director of intelligent automation at DataRobot, analyses RPA and the rise of intelligent automation in healthcare. Read here

Just as algorithms need to be learned so that tasks can be automated effectively, the final tenet of ML discussed by Sivasubramanian calls for companies that deploy machine learning to encourage their engineers to continuously learn new skills and technologies, if they arent doing so already.

AWS has been looking to educate the next generation of builders through its own Machine Learning University, which offers solution-based machine learning training and certification, and where budding builders can learn from AWS practitioners. Learners can also develop skills specific to a particular job role, such as a cloud architect or cloud developer.

Furthermore, AWS DeepRacer, the cloud service providers 3D racing simulator, allows developers of any skill level to learn the essentials of reinforcement learning, and submit models in an aim to win races. The decision making of models can be evaluated with the aid of a 1/18th scale car thats driven by machine learning.

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How AWS's five tenets of innovation lend themselves to machine learning - Information Age

Biology 2.0: Combining machine-learning, robotics and biology to deliver drug discovery of tomorrow

Intelligent OMICS, Arctoris and Medicines Discovery Catapult test in silico pipeline for identifying new molecules for cancer treatment.

Medicines discovery innovators, Intelligent OMICS, supported by Arctoris and Medicines Discovery Catapult, are applying artificial intelligence to find new disease drivers and candidate drugs for lung cancer. This collaboration, backed by Innovate UK, will de-risk future R&D projects and also demonstrate new cost and time-saving approaches to drug discovery.

Analysing a broad set of existing biological information, previously hidden components of disease biology can be identified which in turn lead to the identification of new drugs for development. This provides the catalyst for an AI-driven acceleration in drug discovery and the team has just won a significant Innovate UK grant in order to prove that it works.

Intelligent OMICS, the company leading the project, use in silico (computer-based) tools to find alternative druggable targets. They have already completed a successful analysis of cellular signalling pathways elsewhere in lung cancer pathways and are now selectively targeting the KRAS signalling pathway.

As Intelligent OMICS technology identifies novel biological mechanisms, Medicines Discovery Catapult will explore the appropriate chemical tools and leads that can be used against these new targets, and Arctoris will use their automated drug discovery platform in Oxford to conduct the biological assays which will validate them experimentally.

Working together, the group will provide druggable chemistry against the entire in silico pipeline, offering new benchmarks of cost and time effectiveness over conventional methods of discovery.

Much has been written about the wonders of artificial intelligence and its potential in healthcare, says Dr Simon Haworth, CEO of Intelligent OMICS. Our newsflows are full of details of AI applications in process automation, image analysis and computational chemistry. The DeepMind protein folding breakthrough has also hit the headlines recently as a further AI application. But what does Intelligent OMICS do that is different?

By analysing transcriptomic and similar molecular data our neural networks algorithms re-model known pathways and identify new, important targets. This enables us to develop and own a broad stream of new drugs. Lung cancer is just the start we have parallel programs running in many other areas of cancer, in infectious diseases, in auto-immune disease, in Alzheimers and elsewhere.

We have to thank Innovate UK for backing this important work. The independent validation of our methodology by the highly respected cheminformatics team at MDC coupled with the extraordinarily rapid, wet lab validation provided by Arctoris, will finally prove that, in drug discovery, the era of AI has arrived.

Dr Martin-Immanuel Bittner, Chief Executive Officer of Arctoris commented:

We are thrilled to combine our strengths in robotics-powered drug discovery assay development and execution with the expertise in machine learning that Intelligent OMICS and Medicines Discovery Catapult possess. This unique setup demonstrates the next stage in drug discovery evolution, which is based on high quality datasets and machine intelligence. Together, we will be able to rapidly identify and validate novel targets, leading to promising new drug discovery programmes that will ultimately benefit patients worldwide.

Prof. John P. Overington, Chief Informatics Officer at Medicines Discovery Catapult:

Computational based approaches allow us to explore a top-down approach to identifying novel biological mechanisms of disease, which critically can be validated by selecting the most appropriate chemical modulators and assessing their effects in cellular assay technologies.

Working with Intelligent OMICS and with support from Arctoris we are delighted to play our part in laying the groundwork for computer-augmented, automated drug discovery. Should these methods indeed prove fruitful, it will be transformative for both our industry and patients alike.

If this validation is successful, the partners will have established a unique pipeline of promising new targets and compounds for a specific pathway in lung cancer. But more than that they will also have validated an entirely new drug discovery approach which can then be further scaled to other pathways and diseases.

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Machine-learning, robotics and biology to deliver drug discovery of tomorrow - - pharmaphorum

Artificial Intelligence (AI) and Machine Learning (ML) are certainly not new industries. As early as the 1950s, the term machine learning was introduced by IBM AI pioneer Arthur Samuel. It has been in recent years wherein AI and ML have seen significant growth. IDC, for one, estimates the market for AI to be valued at $156.5 billion in 2020 with a 12.3 percent growth over 2019. Even amid global economic uncertainties, this market is set to grow to $300 billion by 2024, a compound annual growth of 17.1 percent.

There are challenges to be overcome, however, as AI becomes increasingly interwoven into real-world applications and industries. While AI has seen meaningful use in behavioral analysis and marketing, for instance, it is also seeing growth in many business processes.

"The role of AI Applications in enterprises is rapidly evolving. It is transforming how your customers buy, your suppliers deliver, and your competitors compete. AI applications continue to be at the forefront of digital transformation (DX) initiatives, driving both innovation and improvement to business operations," said Ritu Jyoti, program vice president, Artificial Intelligence Research at IDC.

Even with the increasing utilization of sensors and internet-of-things, there is only so much that machines can learn from real-world environments. The limitations come in the form of cost and replicable scenarios. Heres where synthetic data will play a big part

Dor Herman

We need to teach algorithms what it is exactly that we want them to look for, and thats where ML comes in. Without getting too technical, algorithms need a training process, where they go through incredible amounts of annotated data, data that has been marked with different identifiers. And this is, finally, where synthetic data comes in, says Dor Herman, Co-Founder and Chief Executive Officer of OneView, a Tel Aviv-based startup that accelerates ML training with the use of synthetic data.

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Herman says that real-world data can oftentimes be either inaccessible or too expensive to use for training AI. Thus, synthetic data can be generated with built-in annotations in order to accelerate the training process and make it more efficient. He cites four distinct advantages of using synthetic data over real-world data in ML: cost, scale, customization, and the ability to train AI to make decisions on scenarios that are not likely to occur in real-world scenarios.

You can create synthetic data for everything, for any use case, which brings us to the most important advantage of synthetic data--its ability to provide training data for even the rarest occurrences that by their nature dont have real coverage.

Herman gives the example of oil spills, weapons launches, infrastructure damage, and other such catastrophic or rare events. Synthetic data can provide the needed data, data that could have not been obtained in the real world, he says.

Herman cites a case study wherein a client needed AI to detect oil spills. Remember, algorithms need a massive amount of data in order to learn what an oil spill looks like and the company didnt have numerous instances of oil spills, nor did it have aerial images of it.

Since the oil company utilized aerial images for ongoing inspection of their pipelines, OneView applied synthetic data instead. we created, from scratch, aerial-like images of oil spills according to their needs, meaning, in various weather conditions, from different angles and heights, different formations of spills--where everything is customized to the type of airplanes and cameras used.

This would have been an otherwise costly endeavor. Without synthetic data, they would never be able to put algorithms on the detection mission and will need to continue using folks to go over hours and hours of detection flights every day.

With synthetic data, users can define the parameters for training AI, in order for better decision-making once real-world scenarios occur. The OneView platform can generate data customized to their needs. An example involves training computer vision to detect certain inputs based on sensor or visual data.

You input your desired sensor, define the environment and conditions like weather, time of day, shooting angles and so on, add any objects-of-interest--and our platform generates your data; fully annotated, ready for machine learning model training datasets, says Herman.

Annotation also has advantages over real-world data, which will often require manual annotation, which takes extensive time and cost to process. The swift and automated process that produces hundreds of thousands of images replaces a manual, prolonged, cumbersome and error-prone process that hinders computer vision ML algorithms from racing forward, he adds.

OneViews synthetic data generation involves a six-layer process wherein 3D models are created using gaming engines and then flattened to create 2D images.

We start with the layout of the scene so to speak, where the basic elements of the environment are laid out The next step is the placement of objects-of-interest that are the goal of detection, the objects that the algorithms will be trained to discover. We also put in distractors, objects that are similar so the algorithms can learn how to differentiate the goal object from similar-looking objects. Then the appearance building stage follows, when colors, textures, random erosions, noises, and other detailed visual elements are added to mimic how real images look like, with all their imperfections, Herman shares.

The fourth step involves the application of conditions such as weather and time of the day. For the fifth step, sensor parameters (the camera lens type) are implemented, meaning, we adapt the entire image to look like it was taken by a specific remote sensing system, resolution-wise, and other unique technical attributes each system has. Lastly, annotations are added.

Annotations are the marks that are used to define to the algorithm what it is looking at. For example, the algorithm can be trained that this is a car, this is a truck, this is an airplane, and so on. The resulting synthetic datasets are ready for machine learning model training.

For Herman, the biggest contribution of synthetic data is actually paradoxical. By using synthetic data, AI and AI users get a better understanding of the real world and how it works--through machine learning. Image analytics comes with bottlenecks in processing, and computer vision algorithms cannot scale unless this bottleneck is overcome.

Remote sensing data (imagery captured by satellites, airplanes and drones) provides a unique channel to uncover valuable insights on a very large scale for a wide spectrum of industries. In order to do that, you need computer vision AI as a way to study these vast amounts of data collected and return intelligence, Herman explains.

Next, this intelligence is transformed to insights that help us better understand this planet we live on, and of course drive decision making, whether by governments or businesses. The massive growth in computing power enabled the flourishing of AI in recent years, but the collection and preparation of data for computer vision machine learning is the fundamental factor that holds back AI.

He circles back to how OneView intends to reshape machine learning: releasing this bottleneck with synthetic data so the full potential of remote sensing imagery analytics can be realized and thus a better understanding of earth emerges.

The main driver behind Artificial Intelligence and Machine Learning is, of course, business and economic value. Countries, enterprises, businesses, and other stakeholders benefit from the advantages that AI offers, in terms of decision-making, process improvement, and innovation.

The Big message OneView brings is that we enable a better understanding of our planet through the empowerment of computer vision, concludes Herman. Synthetic data is not fake data. Rather, it is purpose-built inputs that enable faster, more efficient, more targeted, and cost-effective machine learning that will be responsive to the needs of real-world decision-making processes.

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How This CEO is Using Synthetic Data to Reshape Machine Learning for Real-World Applications - Yahoo Finance

Throughout the most recent years, as sensor and MCU costs dove and shipped volumes have gone through the roof, an ever-increasing number of organizations have attempted to exploit by adding sensor-driven embedded AI to their products.

Automotive is driving the trend the average non-autonomous vehicle presently has 100 sensors, sending information to 30-50 microcontrollers that run about 1m lines of code and create 1TB of data per vehicle every day. Extravagance vehicles may have twice the same number of, and autonomous vehicles increase the sensor check significantly more drastically.

Yet, its not simply an automotive trend. Industrial equipment is turning out to be progressively brilliant as creators of rotating, reciprocating and other types of equipment rush to add usefulness for condition monitoring and predictive support, and a huge number of new consumer products from toothbrushes, to vacuum cleaners, to fitness monitors add instrumentation and smarts.

An ever-increasing number of smart devices are being introduced each month. We are now at a point where artificial intelligence and machine learning in its exceptionally essential structure has discovered its way into the core of embedded devices. For example, smart home lighting systems that automatically turn on and off depend on whether anybody is available in the room. By all accounts, the system doesnt look excessively stylish. Yet, when you consider everything, you understand that the system is really settling on choices all alone. In view of the contribution from the sensor, the microcontroller/SOC concludes if to turn on the light or not.

To do all of this simultaneously, defeating variety to achieve troublesome detections in real-time, at the edge, inside the vital limitations isnt at all simple. In any case, with current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

They can regularly achieve detections that escape traditional engineering models. They do this by making significantly more productive and compelling utilization of data to conquer variation. Where traditional engineering approaches will ordinarily be founded on a physical model, utilizing data to appraise parameters, machine learning approaches can adapt autonomously of those models. They figure out how to recognize signatures straightforwardly from the raw information and utilize the mechanics of machine learning (mathematics) to isolate targets from non-targets without depending on physical science.

There are a lot of different regions where the convergence of machine learning and embedded systems will prompt great opportunities. Healthcare, for example, is now receiving the rewards of putting resources into AI technology. The Internet of Things or IoT will likewise profit enormously from the introduction of artificial intelligence. We will have smart automation solutions that will prompt energy savings, cost proficiency as well as the end of human blunder.

Forecasting is at the center of so many ML/AI conversations as organizations hope to use neural networks and deep learning to conjecture time series data. The worth is the capacity to ingest information and quickly acknowledge insight into how it changes the long-term outlook. Further, a large part of the circumstance relies upon the global supply chain, which makes improvements significantly harder to precisely project.

Probably the most unsafe positions in production lines are as of now being dealt by machines. Because of the advancement in embedded electronics and industrial automation, we have ground-breaking microcontrollers running the whole mechanical production systems in assembling plants. However, the majority of these machines are not exactly completely automatic and still require a type of human intercession. In any case, the time will come when the introduction of machine learning will help engineers concoct truly intelligent machines that can work with zero human mediation.

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Embedded AI and Machine Learning Adding New Advancements In Tech Space - Analytics Insight