Accelerating the discovery of new materials for 3D printing

 

A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses.

To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength.

By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. 

“Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span,” says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.

Additional authors include co-lead author Timothy Erps, a technical associate in CDFG; Mina Konaković Luković, a CSAIL postdoc; Wan Shou, a former MIT postdoc who is now an assistant professor at the University of Arkansas; senior author Wojciech Matusik, professor of electrical engineering and computer science at MIT; and Hanns Hagen Geotzke, Herve Dietsch, and Klaus Stoll of BASF. The research was published today in Science Advances.

Optimizing discovery

In the system the researchers developed, an optimization algorithm performs much of the trial-and-error discovery process.

A material developer selects a few ingredients, inputs details on their chemical compositions into the algorithm, and defines the mechanical properties the new material should have. Then the algorithm increases and decreases the amounts of those components (like turning knobs on an amplifier) and checks how each formula affects the material’s properties, before arriving at the ideal combination.

Then the developer mixes, processes, and tests that sample to find out how the material actually performs. The developer reports the results to the algorithm, which automatically learns from the experiment and uses the new information to decide on another formulation to test.

“We think, for a number of applications, this would outperform the conventional method because you can rely more heavily on the optimization algorithm to find the optimal solution. You wouldn’t need an expert chemist on hand to preselect the material formulations,” Foshey says.

The researchers have created a free, open-source materials optimization platform called AutoOED that incorporates the same optimization algorithm. AutoOED is a full software package that also allows researchers to conduct their own optimization.

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Scaler Academy launches new course in data science and machine learning

 Scaler Academy launches new course in data science and machine learning

Ed-tech startup Scaler Academy today said that it has launched a new program for engineers in data science and machine learning (ML).

The company said that the program had been designed based on a survey conducted by the company with around 100 data scientists working in leading tech and product firms worldwide.

The course will have a foundation of data structures and algorithms, followed by mathematics, data mining, statistical analysis, data science, machine learning, 

deep

 learning and big data.

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Attention-based deep neural network increases detection capability in sonar systems

 

The Deep-learning technique detects multiple ship targets better than conventional networks.

n underwater acoustics, deep learning is gaining traction in improving sonar systems to detect ships and submarines in distress or in restricted waters. However, noise interference from the complex marine environment becomes a challenge when attempting to detect targeted ship-radiated sounds.

In the Journal of the Acoustical Society of America, published by the Acoustical Society of America through AIP Publishing, researchers in China and the United States explore an attention-based deep neural network (ABNN) to tackle this problem.

"We found the ABNN was highly accurate in a target recognition, exceeding a conventional deep neural network, particularly when using limited single-target data to detect multiple targets," co-author Qunyan Ren said.

Deep learning is a machine-learning method that uses artificial neural networks inspired by the human brain to recognize patterns. Each layer of artificial neurons, or nodes, learns a distinct set of features based on the information contained in the previous layer.

ABNN uses an attention module to mimic elements in the cognitive process that enable us to focus on the most important parts of an image, language, or other pattern and tune out the rest. This is accomplished by adding more weight to certain nodes to enhance specific pattern elements in the machine-learning process.

Incorporating an ABNN system in sonar equipment for targeted ship detection, the researchers tested two ships in a shallow, 135-square-mile area of the South China Sea. They compared their results with a typical deep neural network (DNN). Radar and other equipment were used to determine more than 17 interfering vessels in the experimental area.

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Artificial intelligence may be set to reveal climate-change tipping points.

 

Researchers are developing artificial intelligence that could assess climate change tipping points. The deep learning algorithm could act as an early warning system against runaway climate change.

Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said.

"We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can."Some tipping points that are often associated with run-away climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.

The innovative approach with this AI, according to the researchers, is that it was programmed to learn not just about one type of tipping point but the characteristics of tipping points generally.

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TOP 10 MACHINE LEARNING TOOLS 2021

 

Here is the list of the top 10 machine learning tools in 2021.

Machine learning (ML) is one approach for businesses to improve how they use large data to better understand their consumers’ behaviour, happiness and loyalty. ML can look for patterns and abnormalities that users wouldn’t think to look for on their own.

Some machine learning algorithms are pre-programmed to specialise in a certain task, but in this article, we’ll focus on machine learning tools that allow users to create their own machine learning methods for any data they have. Now, let’s get down to the top 10 machine learning tools of 2021.

 

Best Machine Learning Tools

Shogun

Shogun toolbox, often known as Shogun, is a machine learning tool library that is independent and accessible to use. The solution is written entirely in C++, making it highly accessible to enterprises of various sizes and backgrounds. Shogun is also available in a number of other programming languages, like R, Python, Ruby, Scala and others.

Shogun includes a number of methods and data structures that may be used to investigate typical machine learning issues. This programme may be used to add vector machine functionality to an existing tool or to play around with clustering techniques and linear discriminant analysis. Advanced user interfaces make learning and evolution simpler.

 

Scikit-Learn

Scikit-Learn is a machine learning package developed as a single platform, which is an intriguing strategy for machine learning software. This technology may be used for a range of data management and building strategies. Scikit-data Learn’s regression, categorization, clustering, and pre-processing capabilities and also the ability to acquire other Python modules, are popular among its users.

Apart from allowing you to manage and manipulate your data in a variety of ways, this technology may also assist business executives in the development of Python-based machine learning methods. You may also use the same environment to test and train your algorithms.

 

Jupyter Notebook

Jupyter Notebook is one of the most well-known machine learning software programmes available. The solution combines ultra-fast processing rates with an easy-to-use platform for developing and learning. Furthermore, developers have the option of working with one of three languages: Python, R, or Julia.

Jupyter offers a large community of developers from all around the world as an open-source solution for MI and computational applications. The Jupyter notebook allows you to share and save live code while working on your projects, then access technology via a graphical user interface.

 

WEKA

WEKA was created at the University of Waikato in New Zealand and is also known as the Waikato Environment for Knowledge Analysis. This tried-and-true open-source machine learning system may be used via a graphical user interface, a Java API, or conventional terminal programmes. While WEKA has a wide range of uses, it is most often used for research, teaching I models and developing robust applications.

WEKA is ideal for novices since it has a variety of built-in tools for developing machine learning jobs. You may also receive complete access to a number of other well-known toolboxes, such as Sckit-Learn.

 

Azure Machine Learning Studio

Azure Machine Learning Studio has been one of the earliest drag-and-drop technology solutions for machine learning algorithms, created by Microsoft. The Azure portfolio now includes a far larger collection of machine learning tools, such as the Machine Learning Designer, which streamlines and speeds the process of developing, testing, and maintaining machine learning models.

The Azure Machine Learning Studio is a simple-to-use application that assists businesses in connecting modules and datasets into a plan for developing machine learning technologies. GPU and CPU access are also supported by Azure.

 

KNIME

KNIME specialises in everything from statistical analysis and administration to deep learning research, with the goal of making innovation more accessible to the general public. Businesses may use KNIME software to access data from multiple touchpoints around the organisation and combine it into actionable insights. You may utilise data technology to make sense of your statistics, then use that knowledge to inform your machine learning algorithm.

KNIME is a GUI-based machine learning tool that is completely open-source. To get started with this service, you don’t need any programming experience. Anyone can start mining their data and making the most of it.

 

Amazon Machine Learning

The Amazon Machine Learning software intends to provide any developer and data scientist access to machine learning capabilities. The platform, which was recognised a leader in Gartner’s Magic Quadrant for AI developer services, is assisting businesses of all sizes in rediscovering what’s possible in the ML world.

Companies may use this technology to swiftly construct, train and deploy multiple machine learning services, integrate AI into existing systems, or create custom solutions based on their company’s specific needs. There’s a lot of help available to get you started, as well as a variety of deep learning frameworks and machine learning infrastructure alternatives to select from.

 

Google Cloud AutoML

Google’s Cloud AutoML solution, being one of the most affordable machine learning software solutions available, allows even developers with minimal AI experience to develop high-quality machine learning methods. Through pre-trained models built to support diverse services, such as speech and text recognition, this integrated technology gives any organisation the potential to reap the maximum benefit of artificial intelligence.

 

RapidMiner

RapidMiner is a machine learning platform for executives, data scientists and forward-thinking businesses. RapidMiner, which is now used by over 40,000 firms across the world, lets businesses tap into their valuable information and turn it into applying machine learning.

RapidMiner users may use visual workflow designers, automatic modelling tools and extensive data discovery and preparation tools to access a range of machine learning techniques. There are also applications in almost every industry!

 

TensorFlow

TensorFlow is a popular name in the machine learning field, presently owned by Google. TensorFlow, like many other popular software alternatives, offers an open-source framework that is ideal for large-scale machine learning applications. It combines deep neural networks with other machine learning techniques and it’s very useful for Python users.

The fact that TensorFlow can operate on both CPU and GPU technologies is maybe its most essential feature. There are also a plethora of models and datasets to explore, including support for natural language processing, picture classification and more. TensorFlow has also just launched a certification scheme of its own.

 

Conclusion

The demand for intelligent technology is greater than ever as the globe undergoes a tremendous digital change. However, there are a variety of excellent software programmes and tools available for leaders who want to take advantage of this environment.

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TOP 100 ARTIFICIAL INTELLIGENCE STARTUPS TO LOOKOUT FOR IN 2021

 

Sooner or later, the concept of digitization will completely take over all repetitive tasks. Today, with the help of big data, advanced technologies like automation, artificial intelligence, IoT, and machine learning are leveraging unimaginable amounts and types of information to work from. It is streamlining tedious, repetitive, and difficult tasks, which tend to slow down production and also increases the cost of operation. Owing to the evolution of technology, artificial intelligence startups are mushrooming like never before. The companies are driving the world into a new phase of digitization with a mixture of disruptive statistical methods, computational intelligence, soft computing, and traditional symbolic AI.

Artificial intelligence is the combination of two amazing concepts namely science and engineering. With the infusion of disruptive trends and human intelligence, intelligent machines and intelligent computing programs are emerging. Slowly, the flare of innovations moved away from IT and entered into diverse industries including healthcare, education, finance, marketing, business, telecommunication, etc. Organizations realized that by digitizing repetitive tasks, an enterprise can cut the cost of paperwork and labor which further eliminates human error, thus boosting efficiency. Automating processes involve employing artificial intelligence solutions that can support digitization and deliver data-driven insights. Artificial intelligence startups emerge as a ready-made solution provider that supports every company’s individual needs. AI startups in 2021 use big data to sophisticated AI models and leverage new solutions that could better serve customers. Analytics Insight has listed the top 100 artificial intelligence startups that are driving the next-generation development in technology.

Eg :

1. 8topuz

Headquarter(s): Limassol, Cyprus

Founded: 2010

Focus Area: AI Trading Software, Automated AI Software

Sector: Fintech

Website: https://8topuz.com/

8topuz is a disruptive fintech company that offers an easy-to-access AI-based automated investment system to effectively help customers grow their wealth regardless of their knowledge in trading. 8topuz’s application is designed to work both for investors and non-investors who want to increase their wealth with a fully automated AI-based trading system that is easy to set up and requires no management. It democratizes the way investments are done by bringing sophisticated elite trading technology to laymen. 8topuz’s uses the blend of artificial intelligence and machine learning to leverage next-generation trading software that follows risk management principles.

 

2. Accrad

Headquarter(s): 2020

Founded: Cape Town, South Africa

Focus Area: Artificial Intelligence, Deep Learning, Medical AI Software

Sector: Healthcare

Website: http://accrad.com/

Accrad is a health tech company that assists radiologists to reduce their workload with the precision of artificial intelligence. Radiologists work under different circumstances and deadlines and might find diagnosis through x-rays a bit difficult. Therefore, Accrad has come up with a futuristic solution to help with accurate and fast image diagnosis. The company has made x-ray processing more convincing and simpler. Its signature product CheXRad, a deep learning algorithm that identifies locations in the chest radiograph has the capability to predict 15 different diseases including Covid-19.

 

3. Affable.ai 

Headquarter(s): Suntec Tower One, Singapore

Founded: 2017

Focus Area: Influencer Marketing, Data-driven Marketing Solution, Big Data

Sector: Marketing

Website: https://www.affable.ai/

Affable.ai is a data-driven influencer marketing platform where customers can find relevant and authentic influencers and manage marketing operations. By using cutting-edge computer vision algorithms on social media posts, the company delivers actionable insights about micro-influencers and their audience. Similar to how Google has sophisticated its search and promote relative ads to users, Affable.ai has also built one-click marketing at a shorter scale. Recently, the company has raised US$2 million from Prime Venture Partners, Decacorn Capital, and SGInnovate. Affable.ai was planning to use the funding to expand its international presence into markets such as the US.

 

4. Affirm

Headquarter(s): San Francisco, California

Founded: 2012

Focus Area: Artificial Intelligence, Machine Learning, Data Science

Sector: Fintech

Website: https://www.affirm.com/

If you are thinking of buying a new product or shop for a necessary dress, or go on a trip, but has funding issues, then affirm can help you with installment loans. Affirm is a futuristic fintech company that offers loans to customers at the point of sale. The company aims to revolutionize the banking industry to be more accountable and accessible to consumers. Affirm has partnered with over 2,000 merchants including familiar brands across travel, personal fitness, electronics, apparel and beauty, and more to give shoppers a wide range of options. When the customers check out, they can get a load facility from the company. Affirm has closed a US$500 million securitization of its point-of-sale (POS) installment lands, which will help boost its growth in the future.

 

5. AI. Reverie

Headquarter(s): New York

Founded: 2017

Focus Area: Artificial Intelligence, Big Data, Machine Learning

Sector: Diverse Industries

Website: https://aireverie.com/

Reverie is one of the few companies that are working with synthetic data to leverage privacy-preserving data applications. It is an innovative platform that leverages data to train machine learning algorithms, which could eventually enhance machines’ understanding of the world. The company offers a suite of synthetic data and vision APIs to help businesses across different industries improve their AI applications. As an overall move, the solutions help in the creation of smart cities, sustainable farms, safer homes, etc. AI. Reverie has recently appointed Aayush Prakash, a former Nvidia Deep Learning expert as the Head of its Machine Learning team.

 

6. Aidoc

Headquarter(s): Tel Aviv, Israel

Founded: 2016

Focus Area: Image Analysis, Artificial Intelligence

Sector: Healthcare

Website: https://www.aidoc.com/

Aidoc is a health tech company that innovates to serve physicians’ needs and to create a measured impact on the imaging workflow. Founded in 2016, the company supports and enhances the impact of radiologist diagnostic power by helping physicians expedite patient treatment and improve the quality of care. To leverage the perfect solution, Aidoc’s leadership team has worked together on operation AI with the unprecedented healthcare market in mind. A month ago, Aidoc has raised US$65 million in Series C funding, which will help the company enhance its medical imaging platform.

 

7. Aira

Headquarter(s): California, United States

Founded: 2015

Focus Area: Wearable, Software, Assistive Technology

Sector: Computer Software

Website: https://aira.io/

Aira provides tech-enabled services for over 300 million visually impaired people around the globe. The company is aiming to deliver instant access to visual information from anyone, anytime, and anywhere. Aira’s assistive services blend wearable technology, artificial intelligence, and augmented reality to empower a network of trained, professional agents to remotely assist people who are blind or have low vision. Aira has raised US$12 million in a funding round led by private investors including Jawad Ahsan, Lori Greiner, and Robert Herjavec. The company said that it would use the funding to accelerate its capacity for innovation.

 

8. Airobotics

Headquarter(s): Tel Aviv, Israel

Founded: 2014

Focus Area: Automated Drones

Sector: Mining, Seaports, Oil & Gas, Industrial Facilities

Website: https://www.airoboticsdrones.com

Airobotics is an automated drone platform that develops pilotless drone solutions and offers an end-to-end, fully automated platform for collecting aerial data and gaining valuable insights. The industry-grade platform is available on-site and on-demand, enabling industrial facilities to access premium aerial data in a faster, safer, and more efficient way. With a merger of aerospace hardware design, robust electronic systems, and leading software engineers, Airobotics is leveraging sophisticated commercial drone operations.

 

9. Aisera

Headquarter(s): Palo Alto, California, United States

Founded: 2017

Focus Area: AI Service Desk, AI Customer Service, Conversational AI, Conversational RPA, AIOps

Sector: Call Centers, Customer Service, HR, IT, Sales & Marketing

Website: https://aisera.com/

Aisera is an AI services company providing an AI-driven solution to change the daily routines of employees and customers. Aisera aims to help users with greater self-service by automating common to complex actions, tasks, ad workflow. This enables users to focus on high-value work, while those requesting help can resolve their issues with self-service resolutions. The company recently announced that it would be partnering with Microsoft to deliver next-generation AI service desk solutions to automate requests. Besides, Aisera has also secured US$40 million in a recent Series C funding.

 

10. Alation

Headquarter(s): Redwood City, California, United States

Founded: 2012

Focus Area: Data Governance, Analytics, Cloud Transformation, Digital Transformation, DataOps, Business Analytics, Data Science

Sector: Finance, Healthcare, Insurance, Manufacturing, Retail, Technology, Public

Website: https://www.alation.com/

Alation is pioneering in providing a machine learning data catalog that helps people find, understand, and trust data across a certain organization. The company is leading the evolution into a platform for a broad range of data intelligence solutions including data search and discovery, data governance, data stewardship, analytics, and digital transformation. Alation goes a step beyond in delivering self-service analytics that allows everyone to explore and find insights into their data. The company has recently raised US$110 million in a Series D round led by Riverwood Capital with participation from new investors like Sanabil Investments and Snowflake Ventures.

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Top 10 books can provide information to the data storage architects who have an interest in Data Storage.

 

Data storage architects are entrusted with a great deal of responsibility. It is important to be able to manage huge volumes of data without issue. Books are a fantastic source for experts wanting to learn about a certain sector of technology, whether hardback or digital, and data storage architects are no exception. Here is a list of the top ten books for data storage architects. These books are prepared by writers with expertise and renown in data storage and are designed for both beginners and specialists.

1. The Artificial Intelligence Infrastructure Workshop

2. MongoDB: The Definitive Guide

3. Principles of Database Management

4. Software-Defined Data Infrastructure Essentials

5. The Data Warehouse Toolkit

6. Data Center Storage

7. The Enterprise Big Data Lake

8. The History of Data Storage

9. Information Storage and Management

10. Computer Engineering, Data Storage, Networking and Security

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