For a moment, let’s set aside the abject tragedy of the Covid-19 pandemic and the demoralizing conditions through which the world continues to persevere. Instead, let’s examine the state of affairs from a dispassionate and scientific position. Seismic changes in behavior are erupting as the burden of the pandemic forces transformation. Crippling inefficiencies in industry and volatile projections of markets have led to unprecedented uncertainty.

To fully address any one of the challenges the world now faces would exhaust this medium. However, the role machine learning (ML) will play in people’s lives is fascinating and worthy of discussion, as the field will undoubtedly contribute to the impending metamorphosis. There are three periods of time to consider: the time before the pandemic (BP), the time during the pandemic (DP) and the time after the pandemic (AP). Central to these three epochs are the two approaches to model human behavior through machine learning.

Canonical ML (CML), the first class of interest, represents traditional approaches in pattern recognition, derived from highly structured and labeled data through computational statistics. This approach is used to explain the state of a system or to predict behaviors. Its success often depends on engaged scientists to explain and interpret the model’s ascribed result. CML could be used to predict the weather in a particular region by analyzing the features of the region over time, where each of those features in isolation would fail to fully predict a future state. When CML models are contextualized, you’re able to explain how predictions are made. For example, you can predict rain in Austin tomorrow given the regression and ensemble models you’ve developed for various weather features in aggregate.

I’ll call the second class of techniques reinforcement ML (RML) as it deploys a fundamentally different modeling paradigm: In RML, models self-adjust their individual actions to optimize a collective outcome. These models operate much more autonomously than CML and fully embrace early failures in favor of long-term gains through repeated environment exploration and self-learning. Examples of RML include applications in autonomous driving, gaming, computer vision and even natural language processing. Only recently has RML become tenable for industrial deployments and is still met with much trepidation because of its unexplainable methods and unclear accountability. In other words, when RML models are correct, it is hard to trace what led to that specific output. CML outputs, on the other hand, often are easier to explain. Throughout my career, the applications of CML have represented the overwhelming majority of successful solutions, while RML, in its fledgling state, has only begun to transform the industrial world.

Qualcomm Highlights Mobile Audio With Snapdragon Sound

In 2019 (BP), CML reached its zenith. Nearly every industry was disrupted to some degree by machine learning. From financial services to healthcare to defense, leaders embraced the capabilities of a robust data science solution capitalizing on CML, often materialized into what is commonly known as “deep learning.” Scores of historical data and behavioral modeling contributed to measurable ROI on data science initiatives and applications. Many companies had deployed CML, and some had begun to experiment with RML. Companies around the world were transforming their industries through CML on their own terms. On the surface, the union between science and industry was thriving.

Then the pandemic enveloped the planet. Overnight, the pandemic obliterated the utility of millions of models. Every sector that had benefited from CML was in a difficult position: Companies had to either trust that the ML models their businesses depended on would correct over time or reverse course and manually drive mission-critical insights for their business. I believe CML has failed many businesses across many industries, and the business world has yet to realize the full effects of these failures.

The companies that adopted RML before the pandemic, however, may have an advantage over their peers, as RML models are not as dependent on finely tuned conditions from a scientist, but rather seek to optimize for success as defined by scientists. While RML requires exorbitant amounts of data for training, the increase in the frequency of data collection has eased that challenge in some cases.

Topically, the post-pandemic era will likely resemble the pre-pandemic era but with a heavier slant to digital behavior, as well as customer behavior based on new habits and efficiencies identified during the pandemic. Once industry realizes advantages gained by the firms that adopted RML before the pandemic, I expect there will be an algorithmic arms race. For example, an RML approach for product recommendations for an online retailer will likely adapt to the wildly new engagement model of the post-pandemic epoch. The advantages over the retailer’s CML pre-pandemic competitors will be decisive. Simply put, I believe RML techniques are far more robust at predicting behavior post-pandemic than techniques using CML. Those that are successful in the adoption will have a higher chance to survive and differentiate from their competition.

But what of the explainability of RML? The pressures of the pandemic will greatly shift industry’s willingness to deploy unexplainable or opaque models, or “black box models” as they’re often referred to. As the advantage for the few through RML becomes clear, many firms will likely forgo the accountability of CML in favor of RML’s adaptability. It is the AI equivalent of the adoption of telehealth or remote work during the pandemic, and is arguably much more impactful. Scientists must now work to ensure RML techniques that are deployed can be responsible and accountable or they might compromise the integrity of their operations.

There are many reasons to be excited for the next frontier of commerce. Industries have evolved their priorities, shifted relationships and in many ways removed tedious operations, such as pattern recognition based on outdated labeled data sets that are relics of former industrial epochs. ML will continue to play an integral role in industrial transformation, and as it adapts to the changes people have made in their own lives, I trust my colleagues and peers across industry to ensure we develop this capability in a way that is inspirational, dynamic and responsible.

The post The Pandemic And Its Implications On Industrial Machine Learning appeared first on Artificial Intelligence.

Artificial intelligence is playing a vital role in processing the big data sets of industrial companies.

Artificial intelligence is being used by large industrial companies to analyze their long array of unstructured datasets and put them into smart use. AI is creating analytics models that are creating accurate operating strategies based on variables like pump speed or weather. To be successful in this process, the big industries must know how to create an amenable environment for AI to work properly with their big datasets.

The Making of Smart Data

There is a five-step approach that can be adapted to process the big datasets into smart data. First of all, the steps of the process must be outlined, along with addressing the physical and chemical changes like grinding, heating, oxidation, and polymerization. The process flow of the operation should be labeled using paint schematics or engineering drawing. In the next step, the non-standard operating regimes should be removed. A common data science approach should be used to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this demands a massive number of observations. Instead, teams should prepare the features list to include only those inputs that describe the physical process, and then they should apply deterministic equations to create features that intelligently combine sensor information.

The sensor calibrations should be addressed and a high-quality dataset should be built. The next phase of the process would be to leverage the engineering formulas to combine the sensor data in an intelligent manner.

In the next step, advanced analytic models should be overlaid on engineered data for capturing the stochastic variability. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. The last step includes checking casualties and ensuring the facts that the results are physical.

The Making of Analytics Team

The team responsible for the implementation of AI must have a variety of members from operators to data scientists, automation engineers, and process experts. Companies that are looking to implement AI generally need to rebuild their expert pipeline initially. Knowing the skills is the most important factor when it comes to choosing the perfect process expert. Planning out the model development can be a good exercise to solidify a way of working and avoid linear approaches that include exhaustively completing one stage before proceeding to the next. Later the team can decide what to invest in for the next stage.

Industrial companies are looking to AI to boost their plant operations, reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations. The cross-functional data science teams should include employees who are capable of bridging the gap between machine learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.

The post ARTIFICIAL INTELLIGENCE ACCELERATES DIGITAL REVOLUTION IN INDUSTRIAL COMPANIES appeared first on Artificial Intelligence.

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Connected machines and objects in factories offer the potential for a ‘fourth industrial revolution’, and experts predict more than half of new businesses will run on the IoT by 2020.

IoT encompasses everything connected to the internet, but it is increasingly being used to define objects that “talk” to each other. The industrial sector is entering a new phase of digitalization, where businesses are being driven by data, and analytics, connectivity along with automation, which are propelling trade as the game changers. In such a scenario, the Internet of Things or IoT is being relied on heavily to offer innovation and evolution in the moderately-transforming sectors like logistics, transportation, supply chain, and marine industry. The main objective of utilizing IoT and its applications is to achieve better efficiency in terms of operations, minimize human errors & risks, and hence enhance quality service.

According to a Inmarsat Report: Industrial IoT, it is the maritime industry that has adopted IoT solutions more than any other sector and some sources even reveal that few of the leading shipping organizations plan to invest about $2.5 Million on IoT based solutions in the coming three years with an expectation of achieving around 14% cost-savings over the next 5-7 years.

Presently, with the help of IoT-based solutions, the marine sector is eyeing on reducing the administrative costs of regulatory compliance and enhancing security and safety. Adding more on that note, the technology is also assisting in making cargo handling and preemptive maintenance easier and quicker.

Another area of opportunity for IoT-based solutions in the marine sector is Energy and Fuel Consumption. The smart meters are enabling better and more accurate recording and tracking of the fuels, allowing the sector to not only save on cost but also go environment-friendly.

If we inspect the broader horizon, then most of the transportation and logistics companies have already embraced IoT solutions. Right from utilizing IoT solutions for predictive analytics that help organizations make smarter decisions for route and delivery planning task to identifying the problem-creating areas, creating a smart location management system, real-time inventory tracking and warehousing, introducing self-driven vehicles and drone-based deliveries, IoT is revolutionizing the sector to its very core.

In addition, the technology is also equipping the supply chain management across the globe in various ways. The integration of Radio-frequency Identification commonly referred to as RFID with IoT technology is being seen as one of the biggest impacts of IoT in Supply Chain Management. Through this integration, the supply chain management sector is looking forward to extensively improve its operations. Not only does it allow real-time updates but also helps the industry to combat counterfeit goods, enforce expiration on perishable goods, and detect the various factors that may impact the
quality of the product while in the delivery process.

Moreover, IoT based solutions are also helping the sectors to bring in transparency throughout the transportation system. The introduction of blockchain for supply management within the maritime sector with the help of IoT solutions is something that is currently making heads turn.

Internet of Things is a connected technology, which is filling in the gap between humans, machines, and data; in turn, is providing an edge to the transport sectors as they are now more than ready to embrace the definite role and flexible nature of IoT. It will not be wrong to say that in the foreseeable future IoT will not just transform the businesses but also the ways through which the world carries out its business-be it rail, road, air or sea.

IoT enables real-time tracking and monitoring of cargo at all levels to determine location, delivery and a host of realated matter. While IoT saves time and increases efficiency, security is one of the main challenges of successful IoT implementation.

Deploying and expanding IoT capabilities requires more than just technological breakthroughs. As stakeholders in the shipping and maritime industry advance towards IoT with more vigour, they will need to have their goals set. An effective stratgey, a well-defined goal, robust architecture and security should be on the checklist and the industry embraces IoT.

The post Role of Internet of Things in Shipping and Maritime industry appeared first on Artificial Intelligence.

Jacobs leverages the power of digitization to help facilitate the safe return to on-premise work environments providing location-aware technology and automated intelligence supporting COVID-19 contact tracing efforts in the event of an outbreak.

Jacobs’ ion is an Industrial Internet of Things (IIoT) data integration and visualization platform built on an open API framework to easily integrate with existing solutions or off-the-shelf sensors. By challenging a “business as usual” mindset, the Jacobs team leveraged the platform’s mature technical foundation to add the necessary commercial off-the-shelf sensor components to make the solution viable in an accelerated timeframe.

“This is a great example of Jacobs innovating at the speed of the market, as we leveraged our leading IIoT IP and deep domain knowledge of the clients on-premise operations to solve their toughest challenges with our ion solution and rolled out the first customer ready prototypes in less than a month,” said Jacobs Critical Mission Solutions Senior Vice President and General Manager for Advancing National Security Jennifer Richmond. “We leveraged our scale across Jacobs to rapidly deploy new proprietary features and domain expertise to quickly release new functionality into the platform and rapidly fielded the solution by collaborating with our clients and our market facing operations teams – a partnership that created an immediate feedback loop as we updated the solution in near real-time to shorten the deployment timeline.”

By implementing ion, a large global confidential client improved safety of personnel at the job site through monitoring of social/physical distancing and maximum occupancy for key areas, and by implementing an automated and robust contact tracing capability. The impact on operations has been significant, with one industry executive noting that the innovation is a game-changing solution for return-to-work scenarios in the construction and manufacturing industries in their new operational environment.

The ion platform integrates hardware, IIoT devices, analytics and applications with a robust engine for rules, events, visualization and notifications into a single tailorable platform, available as Software as a Service (SaaS). The solution uses active monitoring to enhance security and reliability and automates processes for personnel safety and accountability with location tracking, mustering and emergency notification. Commercially available wearable technology is used to monitor worker interactions, while taking care to avoid phone and GPS-based solutions that can have a negative impact on personal privacy concerns.

The post Jacobs’ Ion Industrial Internet Of Things Platform Enables A Safe Return To Worksites appeared first on Artificial Intelligence.

The 4th industrial revolution is upon us, and it is time to adjust your portfolio for the next wave of tech that will run our future economy.

The 1st industrial revolution was characterized by mechanization led by water power and the steam engine. The 2nd was mass production powered by electricity driven by oil-based power. The digital revolution or 3rd industrial revolution started 50 years ago, which has shaped the computerized world we live in today. Now, this is coming to an end as the 4th industrial revolution takes form.

This 4th industrial revolution is building on the digital revolution emphasizing intelligence and information. Big data, cloud computing, and the illustrious artificial intelligence are going to drive the world economy as technology and information proliferate.

Big data analytics is becoming crucial for any business to remain competitive in today’s economy. The need for this kind of analytics is going to escalate as the technology behind it becomes increasingly useful. I discussed AI in my previous article: The 4th Industrial Revolution Is Upon Us: Prepare Your Portfolio. In this article, I will discuss some big data stocks that are worth exploring for your portfolio.

Alteryx AYX

AYX has shown remarkable returns to anyone lucky enough to get into these shares before today. Since Alteryx went public 2.5 years ago, it has driven 817% share price appreciation, and just this year the stock has grown an astounding 139% with investors rushing to get into this exciting big data player.

Alteryx provides data analytics and solutions for 5,278 customers in more than 70 countries, serving some of the biggest corporations in the world, including more than 1/4^th of the Global 2,000. It has adopted the subscription-based business model that has become the gold standard for tech today. The platform’s ability to integrate with databases like IBM IBM, Microsoft MSFT, SAP SAP and AWS AMZN, as well as other cloud analytic applications, makes this big data analyzer attractive to any firm.

This company is seeing unbelievable topline consistency that hasn’t faltered below 50% since it went public in April of 2017. The firm is toeing the line of profitability due to a significant increase in sales and marketing spending to establish their brand and develop a best-in-class reputation.

Alteryx offers both on-premise and cloud-based services, depending on customer needs. The firm is investing an increasing amount into its cloud technology to broaden its customer scope and ability.

Everything about this stock is enticing excepted for its ballooning valuation. AYX is trading at 19.6x forward P/S, which is more than 3 times the software industry average and the highest that these shares have been valued at since they went public.

AYX has been driven up by consistent quarterly earnings beats and guidance improvements. This year the company is expected to increase its sales by over 80% and turn its bottom-line deficit into profitability.

I believe that there is a good chance that investors ran this stock up a beyond its current intrinsic, but I see this as an excellent long term play, especially as AYX’s valuation falls. I would wait for a dip before jumping into a position on this overrun stock.

Splunk SPLK

This stock has been trading below its potential all year with an acquisition frenzy instilling concern in investors. I see this as a great opportunity with Splunk’s most recent deals creating synergies beyond the purchase price.

The post The 4th Industrial Revolution Portfolio: Big Data Plays appeared first on Artificial Intelligence.