These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience.

“The DSML market is simultaneously more vibrant and messier than ever,” a gaggle of Gartner analysts led by Peter Krensky wrote in the Magic Quadrant for DSML Platforms, which was published earlier this month. “The definitions and parameters of data science and data scientists continue to evolve, and the market is dramatically different from how it was in 2014, when we published the first Magic Quadrant on it.”

The 2021 Magic Quadrant for DSML is heavily represented by companies to the right of the axis, which anybody who’s familiar with Gartner’s quadrant-based assessment method knows represents the “completeness of vision.” No fewer than 13 of the 20 vendors to make the quadrant’s cut landed on the right side, which indicates active innovation.

Generating new DSML features and exploring new DSML methods is the name of the game in this fast-moving business, Gartner says. “There remains a glut of compelling innovations and visionary roadmaps,” the analysts wrote. “…[V]endors are heavily focused on innovation and differentiation, rather than pure execution. Innovation remains key to survival and relevance.”

The Connecticut-based analyst firm did not sound surprised to conclude that the cloud biggies have moved strongly into the space. “The long-expected gigantic presence in this market of Google and Amazon is now easily felt as they compete with Microsoft for supremacy in terms of DSML capabilities in the cloud,” the analysts write.

However, that does not mean that they are sucking all the air out of the room, as smaller companies have found success in the market, with a few achieving what Gartner termed “hypergrowth.” A few well-established leaders from the previous generation of statistical tools, like SAS, MathWorks, and IBM (SPSS) are also doing well, Gartner notes. In fact, those three vendors are collectively doing better than AWS, Google, and Microsoft when it comes to ability to execute.

The DSML market is young and vibrant, and there is ample revenue and funding opportunities for companies that differentiate themselves on the product side, Gartner says. There is just a “moderate” level of M&A activity at this time, which indicates a growing market. With that said, the vendors who made Gartner’s cut had to prove themselves by meeting certain customer-count and financial performance criteria. And of course, they have to have a product that meets the definition of an DSML platform.

Which begs the question: Just what is an DSML platform? Gartner defines it as a place “to source data, build models and operationalize machine learning,” either by certified, card-carrying data scientists or people who are doing data science work, i.e. citizen data scientists, data engineer, or ML specialists.

Beyond that broad definition, Gartner identified 13 other capabilities that may (or may not) exist in a given DSML platform, including: data ingestion; data preparation; data exploration; feature engineering; model creation and training; model testing; deployment; monitoring; maintenance; data and model governance; explainable artificial intelligence (XAI); business value tracking; and collaboration.

Here’s a brief description of the pros and cons provided for each of the vendors listed in the Magic Quadrant, courtesy of Gartner:

Leaders Quadrant

Databricks Unified Data Platform

Pros: Scalable multi-cloud support; empowerment of data scientists; execution and expansion.

Cons: Lack of support for citizen data scientists; need for governance and responsible AI; growing cloud competition.

Dataiku Data Science Studio

Pros: Support for citizen data scientists; focus on business value; market traction.

Cons: Heavy use of extensions and plugins; emerging story around “XOps” (i.e. unified management of data, ML, models, and platforms); pricing for smaller teams.

IBM Watson Studio on IBM Cloud Pak for Data

Pros: support for multiple personas; composite AI vision; responsible AI and governance.

Cons: scope of auto AI features; doubts about Watson brand; lack of clarity in product-bundling.

MathWorks MATLAB

Pros: Robust composite AI capabilities; integrated domain knowledge; verifiable and reliable ML.

Cons: Interface lacks usability among non-engineers and non-scientists; interpretability of ML models; lack of augmented DSML capabilities.

SAS Viya

Pros: Market understanding and presence; cloud-native architecture and open source integration; automated feature engineering and modeling.

Cons: Perceived high cost; product bundling; marketing strategy.

TIBCO Software (various products)

Pros: Leading edge DSML capabilities; integration of DS and BI/analytics; support for collaboration and applied analytics.

Cons: Limited ModelOps capabilities; lack of support for citizen data science capabilities; financial growth in 2020.

Visionaries Quadrant

AWS (various products)

Pros: Breadth and depth of cloud platform; performance and scalability; data labeling and human-in-the-loop capabilities

Cons: Lack of attention on citizen data scientist; rapid rollout of products and maturity; maturity of on-prem, hybrid, and multi-cloud support

DataRobot Enterprise AI Platform

Pros: Sales strategy and execution; high-touch customer service; successful acquisitions.

Cons: Complexity of product portfolio; resource-heavy onboarding; capability gaps.

Google Cloud AI Platform

Pros: Responsible AI vision and capabilities; research contributions; cohesion and simplification of consolidated products.

Cons: Rapid pace of change; steep learning curve; lack of capabilities for on-prem, hybrid, and multi-cloud deployments.

KNIME Analytics Platform

Pros: Breadth and depth of DSML capabilities; commitment to open source; visual workflow coherence.

Cons: Limitations in enterprise deployments; responsible AI vision; low market traction.

Microsoft Azure Machine Learning

Pros: Strong support for enterprise DS; support for multiple personas; openness and partnerships.

Cons: Requirement of use of other Azure services; immaturity of on-prem, hybrid, and multi-cloud capabilities; lack of support for augmented DSML capabilities.

RapidMiner (various products)

Pros: Support for multiple personas; “clear vision and delivery of aligned features”; expandability and governance.

Cons: Growth rate; average advanced analytics capabilities; academic perception of product.

H2O.ai (various products)

Pros: Vision for value creation; extensive automation; rich AI explainability features (XAI).

Cons: Lack of some data access and data prep features; OEM partner strategy; collaboration and cohesion.

Challengers Quadrant

Alteryx Analytics Process Automation

Pros: Support for multiple personas; product packaging and go-to-market strategy; customer support.

Cons: Changing product portfolio; high cost; lack of innovation.

Niche Players Quadrant

Alibaba Cloud’s Platform for AI (PAI) Studio and Data Science Workshop

Pros: Strong community in China; advanced use-case modeling; and seamless integration.

Cons: Focus on Asia; lack of product vision; narrow usage and focus on professional data scientists.

Altair Knowledge Studio and Knowledge Works

Pros: Ease of use; support for data pipelines; customer satisfaction

Cons: Functional gaps in lineup; limited rollouts in some industries; relatively slow growth.

Anaconda Enterprise

Pros: Trusted and flexible platform; based on open source; culture of collaboration.

Cons: Focus on technical audience; lack of model operationalization functions; runtime stability.

Cloudera Data Platform

Pros: Native Spark on Kubernetes; support for complex data workloads; metadata support for DataOps and MLOps.

Cons: No GUI for development; lack of coherence of products; domain-specific solutions.

Domino Data Lab Data Science Platform

Pros: Support for large, expert teams; mature MLOps capabilities; support for on-prem, hybrid, and multi-cloud.

Cons: Support for small, immature DS teams; low market visibility; open source vision;

Samsung SDS Brightics AI

Pros: Comprehensive ecosystem vision; data access, prep, and visualization; ease of use and collaboration.

Cons: Limited adoption outside of Asia; gaps in product vision; limited capabilities in ModelOps and explainability.

This is indeed a great time to be in the data science and machine learning business. Whether you’re a user of these tools or helping to develop them, the rapid pace of innovation is not only exciting but good for business as a whole.

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Learn how AI innovation is leading to broader market adoption, and how security integrators can take advantage of the trends

Back in 2017, when use and hype of the Artificial Intelligence buzzword really hit the security industry, we wrote: “The uphill climb for today’s video surveillance and other manufacturers, who are tasked with integrating and then marketing AI to security integrators and end-users, is how to dispel the pre-conceived perceptions of what Artificial Intelligence really is.”

Nearly four years later, AI has gained a more stable foothold in the security industry; however, the struggle to advance from the early adopter phase to the broad adoption phase is still taking shape.

This exclusive trends roundtable sponsored by Tyco American Dynamics takes a closer look at this struggle, as well as other aspects of AI technology adoption, with Johnson Controls/Tyco Security Products Manager Jammy DeSousa, as well as veteran integrator Eric Yunag, VP of Technology & Innovation for Convergint Technologies.

What indications have you seen that we are now past the early adopter phase when it comes to AI technology, and if so, when will we see markedly broader adoption?

Yunag: I think one of the clearest signals that we have reached a tipping point is the interest from major cloud and technology companies investing in “platform” strategies in the computer vision space – such as Amazon AWS with Panorama, Microsoft Azure with Custom Vision, and NVIDIA with Metropolis.

These companies clearly recognize the enormous impact these technologies will have on the consumption of their services and are evolving their product strategy and investments accordingly. The speed of development, diversity of hardware deployment options, and scalability of the solutions enabled by those platforms will pave the way for sustained market adoption.

Last year also offered another signal in the form of accelerated digital transformation initiatives across a number of vertical market segments that included legacy security, fire, and life safety systems. In many mission-critical, enterprise environments, these technologies had simply not been proven enough to be relied on, but that has begun to change.

DeSousa: More and more leading companies are investing in AI, such as IBM, Amazon, JCI, etc., because they understand the potential. AI is such a broad “tool” that can be used in almost every business case to make it more efficient, making more accurate decisions faster. The industry will only see broader adoption when more companies use AI and learn to communicate its benefits. The companies who can best communicate AI’s value in their products and solutions will gain quicker adoption.

How does AI differ from traditional intelligent video analytics?

Yunag: Primarily this comes down to the virtually limitless flexibility of AI and what it can be trained to recognize vs. the essentially static nature of traditional analytics. AI – or better stated, computer vision – can leverage ground truth and sample data to train machine learning models to recognize very specific scene elements or, more importantly, behavioral elements. Ultimately, the value of many machine learning models becomes less about the video itself and more about data and trends identified by data, which businesses can then leverage to more effectively optimize outcomes, manage behaviors, or reduce risks.

What criteria should integrators and end-users apply when evaluating AI software and technology?

Yunag: First, it is important to consider a software company’s philosophical approach to the ethical use of AI as it relates to computer vision. There is a continually evolving landscape of laws and regulations that need to be considered before scaling any significant deployment. It is also critical to understand very specifically what the application and use-case is for these technologies. Broader is not better. The more specific the use case, the higher the likelihood of success will be and more impactful the results.

There are several specific computer vision solutions that are effective because they were developed and trained to serve a particular industry or a specific environment. The results could be different if used in another environment where conditions are significantly different.

Lastly, I believe that the best solutions allow for some element of “human in the loop” – even the best AI models can be improved with periodic human validation and correction to allow for continuous improvement and optimization.

DeSousa: Accuracy, performance and user experience. Companies need to trust AI in their systems, so having a high accuracy and”Facial recognition, tracking persons of interest, and automatically redacting faces for privacy is the tip of the iceberg of what AI could bring to a VMS. As technology advances, a VMS without AI will become obsolete.”
– Jammy DeSousa, Johnson Controls Security Technology performance rate is key. 80%

accuracy is good, but 90-plus is better and more trustworthy. If an application is centered around real-time video monitoring (airport, bank, healthcare facility) has a BOLO (be on the lookout), knowing about that intruder, and knowing where he or she is or was last as quickly as possible could be a matter of life and death.

Explain the role that a VMS plays in AI deployments.

Yunag: It is likely that Video Management Systems will remain a core component of enterprise surveillance environments into the foreseeable future. Beyond the historic forensic requirements, there is a significant amount of data (information) stored in traditional VMS, which has the potential to provide additional value as a source of data for model training and enhanced, intelligent search.

A number of innovative computer vision companies are coming to market that take advantage of the in-place VMS infrastructure to connect to a single enterprise system, and aggregate historical data to provide real-time analytics and intelligence for investigative purposes and better situational intelligence.

DeSousa: Video management systems use a multitude of analytic features within the software on either the cameras or recorders. AI can take a camera that simply provides a viewpoint on the entrance of a building and turn it into a highly intelligent behavioral sensor. Facial recognition, tracking persons of interest, and automatically redacting faces for privacy is the tip of the iceberg of what AI could bring to a VMS. As technology advances, a VMS without AI will not only become obsolete, but ineffective compared to a VMS that deploys AI features.

What are the ideal target markets for this technology?

Yunag: These technologies are evolving at an incredible pace, and new use-cases are possible seemingly every day. As a result, the ideal market and customer applications remain very broad. Any environment where digital transformation is a priority, and real-time data and behavioral analytics are valuable, are prime areas for pilots and proof of concepts.

Digital transformation is a power driver in nearly all segments, and connecting the physical and digital world via computer vision is one of the most powerful ways this is accomplished. I believe security integrators have a unique role to play in that transformation, given our experience deploying and maintaining cameras as mission-critical sensors.

Another area of current interest is the broad desire for computer vision applications that enable or accelerate ‘return to work.’ Use-cases like mask detection, social distancing, and crowd detection are all areas of high interest. While these use-cases can be applied in many markets, care must be taken to effectively implement these solutions given the potentially significant differences in environments – manufacturing floor vs. retail checkout, as an example.

DeSousa: Integrators should target any customer who would find value in adding analytic technology to their system. This would also have to be a customer who has the necessary funds to be able to invest in AI. For example, a bank that wants to market that they are the most secure bank in the Midwest would find it extremely marketable to say they use the best in analytic and AI technology to keep”As integrators, it is our responsibility to manage customer expectations by clearly defining the business objectives first. This can be further supported by developing a proof of concept that demonstrates the capabilities in a real environment.”
– Eric Yunag, Convergint TechnologiesPhoto: Convergint Technologies their investments secure.

What is the best way for integrator salespeople to make a case for AI for customers?

Yunag: I would encourage security integration salespeople to think bigger. Integrators need to start thinking and educating their clients about computer vision and AI platforms as force multipliers for security, but also for other business outcomes. As use-cases grow, the total addressable market grows, and educating the market on how to leverage visual intelligence for broader organizational use will be well worth the additional effort and engagement.

DeSousa: With recent events, having an analytic technology that can track persons of interest, or redact faces automatically, or ensure mask compliance could be of value. The starting point is to understand the customer’s problem, and the cost of these problems or loss of productivity. This will allow the best pairing of technology with return on investment or increase in productivity. Ensure that the cost of the solution does not outweigh the value you are trying to offer.

Are there any misconceptions about the technology, and is it meeting end-user expectations? Also, explain the importance of managing expectations and not overpromising from an integrator’s perspective.

Yunag: There is a common misconception that AI can solve anything – “just use AI.” Because of this, end-user expectations when sourcing a solution labeled as “AI” are very high. Every use-case is different, and therefore, each recommendation should be carefully evaluated to ensure objectives are met.

When choosing which technology may be the right deployment for a customer, it is essential to understand how it will be used (very specifically), what data it needs to provide, and how it will help the company make better or faster decisions moving forward.

As integrators, it is our responsibility to manage customer expectations by clearly defining the business objectives first. This can be further supported by developing a proof of concept that demonstrates the capabilities in a real environment. Subsequent pilots can then be launched to validate the solution’s effectiveness before investments in a broad deployment.

In a time of breakneck innovation, “AI” companies are coming to market seemingly every day, and it is unlikely that all will be successful. The integrator’s job is to vet the right partners, understand the market fit, and ultimately how they can uniquely deliver value to customers.

DeSousa: The biggest misconception is that AI makes decisions by itself and learns by itself. AI needs to be taught in order to function. The benefit of AI is not that it can learn on its own – the benefit is that once fully trained, it can be leveraged for automation of forensic and real-time tasks. In many cases, it can perform tasks more efficiently and accurately than a human in unstructured environments. How well the AI is trained determines its effectiveness over that of the human eye and instinct.

The importance of managing expectations and not over promising comes from how well integrators educate customers. With AI being relatively fresh to the market, being able to effectively educate customers will reduce the risk of them interpreting AI for more than what it really is. Set expectations and show how AI will work for them in use cases as accurately as possible.

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CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale.

2020 was a year most of us want to forget. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything from health to cybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments. 

Businesses are now focused on what’s next and preparing for an economic surge in the latter half of 2021 once more people globally are vaccinated and the world returns to a more “normal” way of life. Here are four ways AI and ML will continue to shape multiple industries and integrate with other technologies to drive further innovation and change in the year ahead:

1. Increased Commercial Applications For “Federated ML”

“Federated ML” or a “cloud-in-a-pocket” approach will play a more prominent role. The applied principles and techniques employed with federated ML means data doesn’t need to be in the cloud anymore. Today’s devices can store more data than ever before and likely more than a user could ever produce. As a result, AI models that help to improve personalized services no longer need to be centralized on company servers, but can instead exist on the device itself.

Equally, the techniques used by federated ML ensure user data is kept on the device and not on a server, while still providing access to predictive AI modeling. Data isn’t shared in the same way either. The advanced ML models used in federated ML keep data in data owners’ hands, leading to greater privacy. This approach is a new take on data privacy and a growing megatrend. A good example of this is Siri running on your iPhone, but not sending all of your data to Apple’s servers.

Is Your Company Ready To Make Artificial Intelligence And Augmented Analytics Mainstream?Data-Centric, AI-First Market Trends And Predictions For 2021The Next Generation Of Artificial Intelligence (Part 2)

Federated ML and its principles are currently in use, but greater commercial applications in this area are on the horizon. The introduction of Apple’s M1 chip and the industry-leading neural engine, for example, was specifically designed for advanced ML processes. Federated ML will also provide increased use cases within the financial services sector in areas like loan risk prediction, while AI and ML applications will also advance many other industries.

2. Promising AI Applications Within The Health Sector 

The onset of Covid-19 was a catalyst for advancing technologies in pharma, medicine and the health sector, including a newly updated focus on nursing, patient care, remote patient monitoring and telehealth. 

The integration of AI technology promises use cases in terms of data aggregation, updating patients’ charts and analyzing tests and images to suggest possible diagnoses and more. The application of AI in health, in a supporting role, also frees up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting their doctors with AI to combat their doctor shortage. 

AI technology is rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research. The technology will only evolve in the year ahead and play a more prominent role, especially as the world continues to weather the effects of Covid-19.

3. Hyper-Personalization Within E-Commerce

It’s true many industries suffered under the weight of the pandemic. E-commerce, however, ballooned. Amazon saw over 5.2 billion visitors worldwide in June 2020 and even temporarily limited delivery to only essential items given an unprecedented flood of orders. Consumers will demand even more customized experiences in 2021, giving rise to hyper-personalization and greater customer experience within the e-commerce sector. 

“Algorithmic e-commerce” — or the smart, systemic digitization of business functions often handled manually — will usher in widespread adoption and utilization of AI and ML by enterprises in the e-commerce sector. For example, AI-powered natural language generation (NLG) content will produce an algorithmic e-commerce experience, where customers receive bespoke online shopping experiences through customized product and category descriptions that turn a product page into a personalized sales pitch. Ultimately, this burgeoning trend will lead to a market shift that delivers more value to consumers — where vendors take a more product-type approach to personalization and customer experience, versus a consulting-product approach, as they’ve done previously.

4. New AI And ML Innovations With NLG

Natural language from phonetics, understanding, processing and generation has seen significant advancements in the last few years. As a result, the combination of AI and ML technology with NLG is rapidly pushing the boundaries of what is possible. 

Large and small companies already utilize the technology across multiple sectors and industries. We’ve already seen consumer applications like Google phone calls and enterprise applications like business process automation based on unstructured data (i.e., text to voice). Facebook has also achieved impressive results in semi-supervised and self-supervised learning techniques utilizing AI and NLP.  

GPT-3, the brainchild of OpenAI, a San Francisco-based AI lab is the third in a series of autocomplete tools designed by the company that provides a “text-in-text-out” interface that provides automatic text completion. This area has gained commercial traction and we’ll see real-life innovations and advancing use cases in this area rapidly accelerate over the course of this year. Areas like CSS generation utilizing GPT-3 and applications similar to Google Smart Compose will gain ground in 2021.

IDC expects worldwide AI spending to reach $110 billion by 2024. Companies should review products and projects closely to capitalize on what AI and ML have to offer — and then laser focus on those that use existing first-party data instead of those that first require the setup and creation of large, complex big data sets and data crunching. 

AI and ML technologies are more than buzzwords or simple predictions: They offer businesses limitless possibilities to evolve use cases to improve productivity, expand their customer base, boost ROI and grow their bottom line.

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Thammasat School of Engineering (TSE), has joined forces with the Department of Industry Promotion (DIP), for promoting low-cost Internet of Things technology (IoT).

This is the technology under the SMEs performance improvement project, which will transform manufacturing sectors in SME businesses allow them to “Pay less and Gain more” by using IoT technologies like labor and machine smart monitoring system, stressing that engineers can do so much more for the society.

Asst.Prof.Dr.Supakit Prueksaaroon, a teacher at the Department of Electrical and Computer Engineering, TSE, reveals that Thammasat School of Engineering (TSE), and the Department of Industry Promotion (DIP) through the Division of Digital Industry Development (DDI), are launching the “SMEs performance improvement” project, a counseling activity that helps the entrepreneurs choosing the suitable engineering technology for their businesses.

The technology will be used as a tool to create a better manufacturing system creating stable businesses that will be the driving forces for the country’s economics. This application of knowledge proved that TSE is not only training future engineers, it also focuses on presenting and adapting engineering knowledge and skills to every aspect in the country.

TSE has been working on sharing the IoT technology application in the industrial sectors by conducting field research in factories and providing counseling about how to improve the manufacturing system in the factories with technologies like labor and machine smart monitoring system. For example, in egg farms, the entrepreneurs can now use the technology to count produces and track the hens’ health, allow 6-7 workers to work on the other tasks. Another example is the melamine goods factory, where the technology can help bringing solving the skills and time management problem.

IoT is a very low-cost technology. Entrepreneurs can easily cooperate it by installing IoT board and tracking sensors that can track something like egg and concrete sheet. The system can analyze the data and use the insights to improve the system. There is also a smartphone notification that sends push notifications for the equipment’s status, beginning time, number of products produced per shift. Besides, there is a Line Notify that will send the results directly to a smartphone making it easy to control the setting in the factory as well as solving any problems.

“Even though the industry sector is one of the main driving forces of the country performances and economics, the manufacturing sector of the SMEs still faces many restrictions like the loss of income opportunity due to the lack of skills of some labors and inaccurate statistics from human errors in tracking the produce. Therefore, IoT, the technology that connecting every system to the internet, monitoring everything, will help increase the quality of the manufacturing process and the product as well as lower the cost and increase income. For the workers, the system also acts as a training program for them, creating a more efficient workflow in the long run.” Asst.Prof.Dr.Supakit Prueksaaroon commented.

Assoc. Prof. Dr. Thira Jearsiripongkul, Dean of Thammasat School of Engineering (TSE) added that TSE has implemented the Problem-based Learning (PBL) and always support the researchers, expert specializing in different areas of engineering, and the students to be a part in developing the country’s industrial sector, including SMEs business. PBL prompt the student to create a positive process, and first-hand experience on and opportunities in the industrial field. Also, TSE has been sharing the knowledge and supporting the nation’s industry by working with the public organization like the Department of Industry Promotion (DIP) to ensure that the Thai industry sector will sustainably transfer to the era of “Industry of the Future.”

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The UAE is constantly pushing the envelope as far as innovation and creativity are concerned, specially in the realm of Artificial Intelligence and automation.

Robots in fact are increasingly becoming handy tools and great assistants for human beings in key services. Emiratis are breaking new ground with their trailblazing creations that have captivated many people, regardless of their walks of life, particularly during coronavirus times.

Recently, a young Emirati photographer and astrophotographer from Abu Dhabi tested a sterilisation robot in the emirate.

Ateeq Alsuwaidi succeeded in developing the robot that could be used in the sterilisation drive to stem the spread of coronavirus in the country.  

An Emirati female student from the Khalifa University of Science and Technology named Mohra Wessam Al Sayegh has invented a model for a spacecraft which uses steam energy to accelerate the launch process. It takes advantage of the high temperatures of the spacecraft’s internal engines while preparing for its launch. It produces a gas called hydroxy, which can be used to accelerate the spacecraft.

The student who has specialised in aerospace engineering said the speed of the spacecraft amounts to 2,900 km per hour, while its weight is approximately 2,200 tonnes, accommodating seven people, and carries about 2,200kg.

A few days ago, Abu Dhabi Civil Defence General Authority used a remote control system, TAF35 Firefighting Robot, to control a fire.

The authority explained that the robot pumps large quantities of water, from 1,500 litres to 4,500 litres per minute in the regular system and water spray system. It can also be used to pull weights up to 3.8 tonnes and is characterised by the presence of a bulldozer at the front.

The first Emirati humanoid robots Samit and Sidra were launched at Gitex last year. The launch of the two robots came in conjunction with UAE’s vision for the year 2031.

The main purpose of the two robots is to provide assistance, interact with clients, and accomplish daily tasks without any human interference.

Three years ago, Dubai Police welcomed a new member on to its force, the world’s first operational robot policeman. He doesn’t need a visa, medical insurance or hours of training, and he is ready to engage with Dubai’s residents and millions of tourists across the city’s streets.

It is these inventions that pitchfork the UAE into the frontline of global technological brilliance. The robots, programmed by Artificial Intelligence, will interact with and evaluate satisfaction of clients through provided services.

The influence of Artificial Intelligence, AI, and smart services has increased around the world while robots, drones and electronic services have become key global tools for controlling the emergence of coronavirus, COVID-19, and preventing its spread.

The UAE has developed an integrated system of electronic and smart services, which has reduced the number of required auditors and physical customers of relevant authorities.

The UAE’s electronic services system has also carved a trailblazing path. It is one of the best in the region, providing leading services, such as clearing government and private transactions, banking services and electronic shopping. This system has played a key role in reducing waiting lines and assisting various government and private entities.

At the root of this remarkable electronic headway is the thirst for exploring new and uncharted terrain in the field of science and technology. The UAE seeks to map the space frontier with its ambitious Hope Probe. The Emirates Mars Mission, the first interplanetary exploration undertaken by an Arab nation, recently announced the scheduled launch of its Mars Hope Probe in a 495,000,000km journey to reach and orbit the Red Planet. The launch of the Hope Probe into space is scheduled for July 15 – in just about a month’s time.

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The Israel Securities Authority (ISA) and the Israel Innovation Authority (IIA) launched a new program this week for fintech startups aimed at offering direct collaboration between regulators, regulated entities, and companies in the industry.

Called Data Sandbox, the program supports Israeli companies that meet “the real-world needs of the industry,” the Innovation Authority said. It is run in collaboration with the TASE (Tel Aviv Stock Exchange) with the support of the Israeli Ministry of Finance.

The Data Sandbox initiative will offer a direct interface between regulators, regulated entities, and high-growth fintech companies. Companies participating in the project could gain access to the databases of the ISA and the TASE.

Five Israeli companies were selected for the program which will run for approximately six months, according to the announcement. They offer solutions in areas such as liquidity provision, digital tools for verification and authentication, identification of trading anomalies, and tools for portfolio investment managers.

The solutions will help meet the challenges faced by the Securities Authority and the capital market, such as bolstering Stock Exchange liquidity, improving compliance processes while reducing affiliated costs, and making data accessible for regulated entities, the statement said.

The companies selected for the program include Zirra.co, a Tel Aviv-based trading analytics firm that offers clear and structured data-driven insights through artificial intelligence — built to determine risk of investment; Scanovate, a Ramat-Gan based firm that will establish an SaaS-based Identity Provider on a cloud for clients of Israeli investment houses; and Correlate Capital, a tech company specializing in the development of trading and liquidity provision systems that created a system that provides liquidity and other products.

Two companies were selected to conduct a pilot with the authority while using trading data received directly from the TASE. Fintica AI will use artificial intelligence and Wizsoft (Israel), which provides enterprise resource planning (ERP) solutions and operates machine learning and data mining systems, will run algorithms for the pilot.

“The goal of this pilot program with the Israel Securities Authority is to allow local high-tech companies to implement their technology for the benefit of the Tel Aviv Stock Exchange and capital market, thus improving the market-readiness and market penetration of their products while also providing solutions to the challenges faced by the Israeli economy,’ said Aharon Aharon, CEO of the Israel Innovation Authority.

“The path to achieving this goal lies primarily in sharing the know-how and data accumulated by the Authority along with additional regulatory entities with Fintech companies which offer technological solutions for the capital market, and to guide the pilot process while providing solutions for regulatory issues,” adds Anat Guetta, chairperson of Israel Securities Authority.

Aharon said that because there is a high demand for the program and the variety of solutions that were submitted, thus both the ISA and the IIA have decided to launch an additional call for proposals, scheduled to close later this year.

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Technology won’t stop innovating, regardless of the economic turbulence ahead, and that’s good news for Internet of Things (IoT) stocks.

If anything, the lockdowns put in place by many countries in the response to the novel coronavirus has only accelerated the importance of technology solutions in many sectors. And even as countries re-open their economies, consumers and businesses alike will hesitate to return to their pre-pandemic purchasing habits.

People will rely more on technology in their everyday lives. Innovative solutions utilizing IoT will rise in importance.

These 7 Internet of Things stocks are set for serious growth in the coming months:

• NXP Semiconductors (NASDAQ:NXPI)
• Apple (NASDAQ:AAPL)
• Intel (NASDAQ:INTC)
• Qualcomm (NASDAQ:QCOM)
• Analog Devices (NASDAQ:ADI)
• Johnson Controls (NYSE:JCI)
• Rockwell Automation (NYSE:ROK)

While Covid-19 might be the direct catalyst for these stocks at the moment, investors ought to remember that secular trends in cloud computing and connected devices were already heading this direction. The pandemic has simply moved up the timetable.

Internet of Things Stocks to Buy: NXP Semiconductors (NXPI)

NXP Semiconductors reported first-quarter revenue of $2 billion.  Industrial and IoT contributed to $376 million in revenue, while Automotive added the most at $994 million in the first quarter. The company forecasts Industrial and IoT revenue growing in the low-single-digits.

China’s return to business will lead to the IoT sell-through. Executive Rick Clemmer said China reapproaching normalcy, “gives [the company] confidence in being able to achieve the 6% sequential growth in the industrial & IoT.“

In automotive, demand for radar and battery management will continue. But NXP has more crossover with IoT and Industrial due to the need for connectivity solutions. The company bought Marvell’s (NASDAQ: MRVL) Wi-Fi and Bluetooth connectivity assets in Dec. 2019.

Looking at valuation, NXP stock compares unfavorably with the S&P 500:

Data Courtesy of Stock Rover

Notice from the table above that the company’s gross margin is above both the industry average and the index. As IoT becomes a bigger contributor to revenue, profits will increase, too. Based on the 20 analysts covering NXPI, the average price target is ~$110 (per Tipranks).

Apple (AAPL)

Apple Watch represents a core IoT play for the smartphone giant. The company’s latest iteration of the wearable device adds many useful features that fit with the IoT innovation.

Apple highlighted the ECG app on the Apple Watch “used to facilitate remote ECG measurements and recordings for telemedicine usage, reducing patient and healthcare provider contact and exposure.”

The business is so big that it is now the size of a Fortune 140 company. Future releases of the device will add more features. For example, the ECG app signals the company’s interest in developing more health-related monitoring. Eventually, it may further integrate patient health data and send it to hospitals. This will give Watch wearers better health care, as its providers may monitor them in a more timely fashion.

More recently, Apple and Google (NASDAQ:GOOG) updated the device operating system to support Covid-19 based tracking. The Application Programming Interface will allow the system to track people, using Bluetooth, who may have been close to infected individuals.

On Wall Street, analysts think the stock already trades at fair value. Conversely, Apple has a high (operating) efficiency score of 99 and a momentum score of 96 on Stock Rover.

The momentum is mainly about the stock’s uptrend.

Intel (INTC)

Intel announced the purchase of Rivet Networks on May 20, 2020. This will enhance INTC’s secure Wi-Fi connectivity solutions and let it add “more connected devices and higher bandwidth applications for gaming, video streaming and content creation, as well as for processing increasingly larger file sizes.”

Having lower latency network interface cards is an important aspect of IoT. Still, the Internet of Things Group (IoTG) underperformed in the first quarter. Intel said that operating income dropped 3% due to lower revenue from industrial and retail.

So, as IoTG lags in the near-term, investors may hold INTC stock for the strong Mobileye unit’s performance. Mobileye reported revenue growth of 22% and operating income growing by 29% year-over-year.

Investors may also look forward to Intel’s 10-nanometer-based solutions. Its Snow Ridge processor adds to its portfolio of 5G capabilities. And since the cloud and network infrastructure for 5G are growing faster than seasonal trends, Intel stock will benefit from revenue growth in segments outside of IoTG.

Qualcomm (QCOM)

In the last quarter, Qualcomm reported increased demand for connectivity due to the work-from-home environment. This trend should continue in the months ahead because more workers will be connected to their offices remotely.

QCOM stock still trades at a fair discount of 14 times forward price-to-earnings ratio. The stock market is still undervaluing the company’s future IoT solutions. For example, Qualcomm Technologies helps its customers commercialize their products using its platforms. This includes smart bodies, smart cities, and smart homes.

The company counts on NFC, Bluetooth, Wi-Fi, LTE modems, and Snapdragon processors as some of its IoT solution offerings.

Analog Devices (ADI)

Analog Devices describes itself as the pioneer of advancing solutions that “sense, measure, interpret, connect and analyze.” So, it optimizes data quality and analytics. That way, data is relevant, accurate, and complete. To foster its investments, Analog Devices partners with its customers to design solutions. As a result, the partners solve tough IoT challenges in their applications.

In the second quarter, ADI reported revenue of $1.32 billion and EPS of $1.08. It spent 19% of its revenue on research and development. 95% of its resources went to B2B market opportunities. Expect more solutions and new technologies emerging as a result of more customer engagements during the quarter.

Investors may assume the following metrics below in a 10-year discounted cash flow EBITDA model.

Data courtesy of finbox

Assuming revenue falls this year due to Covid-19 but recovering in the following years, ADI stock has a fair value of around $117.

Johnson Controls (JCI)

Johnson Controls won the “Industrial IoT Innovator of the Year” last year. Through its Johnson Controls Digital Solutions, it is “working with its customers to pull insights that allow for smarter buildings, increased efficiencies and new business value.”

By helping companies innovate and save money by operating more efficiently, the company is a conservative IoT play. More specifically, its connected equipment and services utilize machine learning and predictive diagnostics to increase reliability. Johnson has over 1,600 customers connected across 5,000 buildings and 75,000 equipment and systems. All of these are on an IoT connected platform and solution.

In the second quarter, Johnson reported cash of over $1 billion. Net debt rose $900 million due to share buybacks. The company is positioned to lower its net debt-to-EBITDA leverage to 2 times. EBIT and margins were flat from last year at 8.1% (from the presentation).

As IoT plays a bigger roll in Johnson’s offerings, profitability will climb. On Wall Street, the average price target on JCI stock is ~$33.00.

Rockwell Automation (ROK)

Rockwell Automation is hovering at yearly highs because it continues to differentiate itself from the marketplace. Customers are noticing their IoT offerings. In the second quarter, Compass Intelligence rewarded the company with the Industrial IoT Company of the Year. Gartner’s annual Magic Quadrant survey listed it as a leader in IoT software.

In the second quarter, Rockwell enjoyed strong margins in its Architecture and Software segment. Its solutions and services businesses reported a 1.10 book-to-bill ratio.  The company ended the quarter with $640 million in cash and had a total debt of $2.1 billion. Its net debt to adjusted EBITDA ratio was 1.0. Rockwell’s strong liquidity suggests that it has the flexibility to cut capital expenditures to align with lower revenue in the near-term. Rockwell also cut costs and expects to save over $150 million for fiscal 2020.

ROK stock may not have any upside, as analysts have an average target price target of $183. Still, the IoT drive should result in long-term growth in the medium term.

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Since data has been established as the new oil, companies have been aggressively investing in ways to collect data from their own workflows and processes as well as along the value chain, right up to the end user or customer.

This ‘big data’ is phenomenal and helps organizations change their understanding of the world around them when compared to how they operated in the past.

For example, in the past, inventory was a function of sales forecasts which themselves were calculated based on last year’s sales and adjusted for rise or fall in demand. With big data, organizations can factor in other metrics that impact sales, making their forecast more reasonable.

So, there’s no doubt that collecting data is helpful. Neglecting to collect or procure data can be quite a disadvantage to most organizations, especially because their competitors have access to it and are constantly looking for creative ways to exploit it for growth.

Truth be told, as a result of technologies such as 5G, the internet of things (IoT), edge computing, and so on, companies using big data to their advantage are doing so by tapping into it and analyzing it in real-time.

In the retail industry, for example, smart shelves can help store managers ensure that customers always find the right items, in the right spot, and at the right time. But if consumer packaged goods (CPG) companies tap into this data, they can get more intelligent with their distribution to ensure deliveries are prioritized to stores where demand is high.

The next victories with big data will come from those using it in real time

Using big data in real time creates specific advantages – but the uses cases must be thought through.

In the case of the retail industry partnering with CPG players, while sensors on smart shelves collect plenty of data, CPG companies could create a smart workflow with edge computing architecture processing all data to make sure only relevant data is passed on to supply chain managers empowered to handle logistics on the ground.

Collecting big data has its advantages, but there’s a cost associated with storing it all (even if it is only archived). Further, if not used quickly, or in real-time, it might not be very useful at all.

CPG players collecting data from smart shelves in retail stores get the most out of it only when they process it in real-time.

If they process it later, it could help identify trends in demand that correlate with other factors such as season, affluency in a neighborhood, and so on, but is unlikely to have a direct and immediate impact on sales.

Further, not every bit of the data might be useful, so organizations that plan to continue collecting data for the foreseeable future need to come up with a plan to assess their data streams and understand which sets need to be saved for the future and which have a short lifespan and must be analyzed immediately and deleted.

Real-time big data analytics, in most cases, isn’t hard. It’s limited by the imagination of business leaders and has already provided outsized returns for those willing to go beyond their comfort zone.

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Business intelligence (BI) is nothing new to enterprises that have been relying on data processing and analysis to deliver insightful reports that reflect business performance.

These tools are a great match for enterprises that value the data their operations generate.

BI software and programs work together to turn data into actionable insights that can drive better business decisions and market strategies and, ultimately, drive revenue as a result.

Combined with the masses of external data amassing every second – whether that’s customers’ feedback and experience, competitor intelligence, seasonal buying habits, or otherwise – businesses can have a huge amount of data at their disposal.

While BI systems draw specific data from pre-defined sources to turn them into insights, big data technologies capture data from a variety of sources in real-time, regardless of their formats or structure.

Unlike BI, big data doesn’t answer critical questions that enterprises have, but rather, it provides them with new information that can prompt new questions that enterprises haven’t thought of asking.

Once enterprises have a better idea of what they want to find out, they can turn to BI tools to deliver the insights, and even make predictions.

Business impact

Since big data came into the picture, BI becomes just that much more conducive.

Through big data, BI can now deliver insights that enable businesses to better understand their customers, improve marketing techniques, make personalization possible and identify issues and opportunities that emerge in real-time.

Marketing strategies no longer have to a shot in the dark. With data varying from customer feedback to their journey records, enterprises can market products with a clear understanding of what customers want and need.

Enterprises can then leverage data alongside real-time market analysis data to develop and deliver new and improved products and services.

Big data technologies can help businesses augment their operations. Data doesn’t have to be externally sourced, it can come from internal systems and programs as well.

Siloes between teams and hiccups in workflows can be detected in real-time, allowing enterprises to redesign their processes effectively.

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Both the experience economy and pace of technological change continues to move at breakneck speed.

Businesses are now expected to deploy new software, services and add new code at a rate that is forcing many leaders to rethink their increasingly dated-looking architectural structures.

Traditionally, if you dared to look under the hood of your most used applications, you would find a client-facing interface, a database, and a server-side application. Although this model has served businesses well for decades, its rigidity is falling out of favor in a world driven by continuous change.

Anything that puts obstacles in the way of isolating services, maintaining code, and applying quick bug fixes will slow down the pace that a business needs to change. Business leaders do not want to hear about slower times to market and difficulty to scale. They want an easier way to help them thrive in the digital age of continuous deployment.

Many believe that developing a monolithic application all in one piece is no longer fit for purpose. However, breaking down software applications into a smaller suite of independently deployable components that are continuously developed and separately maintained looks attractive by comparison.

The migration to microservices

Understandably, businesses are seizing the opportunities that come with this fit for the cloud model to implement agile and customizable software. After years of being burned by lengthy IT projects that over-promised and undelivered, technology is finally helping business goals reach their goals quicker.

These are just a few reasons why businesses around the world are migrating from monolithic to microservice architecture. Everyone from Apple, Netflix to the BBC are reaping the benefits. Predictably the biggest winners of leveraging microservices and DevOps is the tech behemoth Amazon whose engineers deploy new code every 11.7 seconds.

A much more agile approach helped the company dramatically reduce the number and duration of outages. The migration also resulted in increased revenue quickly provided the elusive ROI that many tech projects fail to deliver.

It’s more complicated than you think

Unfortunately, it’s not as simple as diving headfirst into the world of microservice architecture. If we have learned anything from the complicated relationship between business and technology, it’s that there is no such thing as a silver bullet.

Increasing the number of services can result in information silos. As the number of services inevitably increases, the integration and management of everything can quickly become even more complicated. Additional complexities for developers will arise too, especially when faced with mitigating fault tolerance, network latency, load balancing, and the risk of creating distributed spaghetti code.

Dynatrace CEO, John Van Siclen recently advised that 87 percent of businesses have already adopted some form of microservices. Analysts predict that we will continue to build and deploy more apps in the next three years than we have created in the last 40 years. But Siclen warned we could be causing bigger problems for IT and DevOps teams in the future. Automation will need to become mandatory in the unsexy world of application performance management (APM).

The road ahead

Analysts predict that 90 percent of all new apps will feature microservices architecture. Before jumping on the next big thing in tech bandwagon, it’s your unique business requirements that should determine your next move. Unfortunately, a one-size-fits-all architecture solution doesn’t exist. Microservices will probably already be a critical component of your IT strategy, but many of your legacy applications are unlikely to be candidates for improvement.

There is a cultural and technical cost involved when moving to a microservices architecture that needs to be considered to avoid racking up technical debt. According to Gartner, a bi-modal IT strategy will enable microservices and traditional applications to work together in the same environments.

The strategy will enable legacy IT applications to focus on traditional metrics such as stability and uptime. By contrast, the new shiny apps that are more agile with shorter development cycles will be catered for too.

However, splitting IT into two groups has split thinking in the IT community, and some believe that the concept is spreading a myth. Can you imagine a world where Mode 1 manages traditional business requirements such as safety and accuracy? And where teams can switch to Mode 2 for new apps focusing on different apps that need agility and speed?

Here in 2020, buzzwords such as microservices and bi-modal IT will be capturing the headlines and attention of business leaders looking to evolve their technology stack. But the reality facing many organizations will be the need to adapt their strategy to work with both monolithic and microservice approaches for many years to come.

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