Category Archives: Ai

Canvas, a company that uses machine learning to install drywall at construction sites, emerged from stealth today. Canvas was founded in 2017 and uses a modified JLG lift, robotic arm, and sensors to automate drywall installation.

Once that task is perfected, Canvas plans to expand into areas like painting and spray-on insulation. The company focuses on commercial construction sites larger than 10,000 square feet, and Canvas founders say its machines operate faster and at a higher level of quality than humans working without a robot.

A lot of our knowledge here comes from working with the U.S. military on surface preparation and finishing and other things like aircraft and ship vehicles, Canvas founder Kevin Albert told VentureBeat in a phone interview.

Whereas some robotics companies sell or rent hardware, Canvas machines are run by trained workers from the International Union of Painters and Allied Trades.

We are for all intents and purposes a tech-enabled subcontractor in our customers eyes, Albert said. Were very excited about the union, and we think thats a great way and a great future for bringing this type of machinery into the world.

Canvas only operates in the San Francisco Bay Area, but it is gearing up to move into other cities. These expansion plans come as the U.S. economy continues to falter due to COVID-19 and mismanagement of the pandemic.

An Associated General Contractors of America survey released earlier this week found declines in major construction projects in large cities across the United States during the pandemic. The survey also found that a majority of firms are expected to cut jobs or freeze hiring in 2021. Conventional construction companies like Caterpillar and Komatsu have also experienced declines in hardware sales this year. But as fewer bulldozers and excavators are sold, companies are turning to AI services for construction, mining, and space.

When asked how Canvas plans to succeed in this environment, Albert said Many months into this crisis we have been growing, and given the type of work we do, we dont expect much of an impact to our growth as things continue.

Canvas has 30 employees and has raised $19 million from investors that include Innovation Endeavors, Obvious Ventures, Brick & Mortar Ventures, and Grit Ventures.

In other compelling robotics news, Walmart recently stopped using Bossa Nova Robotics to scan store shelves and Hyundai reportedly wants to buy Boston Dynamics.

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Canvas emerges from stealth with AI for drywall installation - VentureBeat

It was just two years ago when the Joint Artificial Intelligence Center was created to grab the transformative potential of artificial intelligence technology for the benefit of America's national security, and it has grown substantially from humble beginnings.

Dana Deasy, the Defense Department's chief information officer, and Marine Corps Lt. Gen. Michael Groen, the director of the JAIC, virtually discussed from the Pentagon the growth and goals of JAIC at a FedTalks event during National AI Week.

''One of the things we've wanted to keep in our DNA is this idea that we want to hire a lot of diversity of thought into [JAIC],'' Deasy said, ''but yet do that in a way where that diversity of thought coalesces around a couple of really important themes.''

When JAIC began, it needed to grab hold of some projects that can show people that it can be nimble, agile, and it has the talent to give something that is meaningful back to the Defense Department, he noted.

So JAIC started in a variety of different places, Deasy said. ''But now as we've matured, we really need to focus on what was the core mission for JAIC. And that was, we have to figure out what the role is that AI plays in enabling the warfighter. And I've always said that JAIC should be central to any and all future discussions in that place,'' the CIO said.

''Transformation is our vision,'' Groen said.

''So, it's a big job. We discovered pretty quickly that seeding the environment with lots of small AI projects was not transformational in and of itself. We knew we had to do more. And so, what we're calling JAIC 2.0 is a focused transition in a couple of ways. [For example], we're going to continue to build AI products, because the talent in the JAIC is just superb,'' the JAIC director said.

Groen noted that the JAIC is thinking about solution spaces for a broad base of customers, which really gets it focused.

''There are, you know, the application, and the utilization of AI across the department [that] is very uneven. We have places that are really good. And there, some of the services are just doing fantastic things. And we have some places, large-scale enterprises with fantastic use cases [that] really could use AI, but they don't know where to start. So, we're going to shift from a transformational perspective to start looking at that broad base of customers and enable them,'' he said.

JAIC is going to continue to work with the military services on the cutting edge of AI and AI application, especially in the integration space, where JAIC is bringing together intelligence or intelligence of maneuver, Groen said, ''The warfighting functions have superb stovepipes. But now we need to bring those stovepipes together and integrate them through AI,'' he added.

We have to figure out what the role is that AI plays in enabling the warfighter. And I've always said that JAIC should be central to any and all future discussions in that place.''

The history books of the future will say JAIC was about joint common foundation, Deasy said. ''JAIC could never do all of the AI initiatives with the Department of Defense, nor was it ever created to do that. But what we did say was that people who are going to roll up [their] sleeves, and seriously start trying to leverage AI to help the warfighter every day. at the core of JAIC's success has got to be this joint common foundation,'' he noted.

Deasy noted that the JAIC was powerful and very real.

Into next year, he added, JAIC will have some basic services. And then it's a minimum viable product approach, where JAIC is building some basic services, a lot of native services from cloud providers, but then adding services to that.

''And where we hope to grow the technical platform is a place where people can bring their data, places where we can offer data services, data conditioning, maybe table data labeling and we can start curating data,'' Deasy projected. ''One of the things we'd really like to be able to do for the department is start cataloging and storing algorithms and data. So now we'll have an environment so we can share training data, for example, across programs.''

The modernized software foundation now gives JAIC a platform so it can build AI, Groen said, adding AI has to be a conscious application layer that's applied, leveraging the platform and the things that digital modernization provides.

''But when you think of it that way, holy cow, what a platform to operate from,'' he said.

So now JAIC will really have a have a place where the joint force can effectively operate, he said, adding that the JAIC can now start integrating intel in fires, intel in a maneuver command and control, the logistics enterprise, the combat logistics enterprise and sort of the broad support enterprise, Groen noted.

''You can't do any of that without a platform, and you can't do any of that without those digital modernization tenets,'' the JAIC director said.

If JAIC is going to have the whole force operating at the speed of machines, then it has to start bringing these artificial intelligence applications together into an ecosystem, Groen said, noting that it has to be a trusted ecosystem, meaning "we actually have to know, if we're going to bring data into a capability, we have to know that's good data."

''So how do we build an ecosystem so that we can know the provenance of data, and we can ensure that the algorithms are tested to set in a satisfactory way that we can comfortably and safely integrate data and decision making across warfighting functions,'' the JAIC director asked. ''That's the kind of stuff that I think it's really exciting, because that's the real transformation that we're after.''

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Joint Artificial Intelligence Center Has Substantially Grown To Aid The Warfighter - Department of Defense

Cognite, a global industrial artificial intelligence (AI) software company, and Wood, a global engineering and consulting company, have agreed to a strategic partnership that will accelerate industrial transformation by creating AI solutions that enable more connected, sustainable and data-driven operations for heavy-asset, infrastructure and industrial clients.

The collaboration will deliver value faster and at scale, combining Cognites flagship product, Cognite Data Fusion, with Woods multi-sector domain knowledge, data extraction and technology integration expertise to optimise productivity and performance.

Both companies are committed to deploying performance solutions that address the needs of the energy transition, with the collaboration allowing for greater understanding of existing assets and operations, liberating vast amounts of data trapped in fragmented and legacy systems.

President of Automation and Control at Wood, Mark House, said: Wood and Cognite will leverage physics-based models and AI to quickly provide advanced analytics that drive more profitable and sustainable industrial operations.

Through the partnership, we are addressing a familiar challenge in industry when operational and information technology converge.

John Markus Lervik, CEO of Cognite, said: Working with Wood presents a fantastic opportunity for us to deliver value faster and at scale by playing to each of our strengths. The partnership embraces scalable innovation and value realisation which is accelerated by combining what both Wood and Cognite are best known for in the market.

Through Cognite Data Fusion, data will be transformed from siloed raw information into meaningful digital insights in real-time, to make faster and better-informed business and operational decisions.

Adding Cognites advanced AI data contextualisation and operations product to Woods technology partnership ecosystem is an exciting step as we innovate in connected operations solutions, said Darren Martin, Woods Chief Technology Officer. This collaboration will further enable us to meet the ambitions of our clients and empower them to be future ready now.

Read the article online at: https://www.hydrocarbonengineering.com/refining/20112020/wood-and-cognite-unite-to-unlock-artificial-intelligence-solutions-for-industrial-operations/

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Wood and Cognite unite to unlock artificial intelligence solutions for industrial operations - Hydrocarbon Engineering

After decades of leading the world in sophisticated technological advancements, the United States is now in a precarious position. Our near-peer competitors are catching up, and we can no longer afford to operate in silos, using old formulas that waste precious time and resources. With significant national security consequences on the line, the Department of Defense is embracing enterprise data and decision management to addresses its specific, unique challenges and to make impactful decisions at a moments notice.

The Joint Artificial Intelligence Center is an exemplary group that demonstrates how the Department of Defense needs to work across departments and units as an enterprise. There is no federal agency that faces as many unique business scenarios as the DoD. Its challenges include the highest security requirements, legacy systems, multiple platforms vendors and departments, dark data, interconnected programs, multiyear projections, and fluctuating funding and budget exchanges.

Most government agencies default to spreadsheets, consultants and PowerPoints to make decisions while also using unstructured content documents and data calls to gather enormous columns and rows of information. For the DoD, these decisions are literally foundational and fundamental to operating our armed forces and protecting our citizens from adversaries on physical and digital fronts.

Complex challenges arise from volumes of sensitive information, most of which are siloed due to disparate data sources, insular departmental protocols and legacy systems that cannot keep up. Processes are laden with one-dimensional IT systems, spreadsheets and presentations that require laborious manual edits every time there is a change in budget or a budget drill. Investment decisions are drawn out due to the difficulty of keeping track of authoritative data and the rationale behind decisions, as well as decision parameters at the time of decision.

This is not a criticism but a statement as to the inherent problem of too many sources, platforms and objectives. Its not easy to take non-structured information and make sense of massive amounts of dark data that are not recorded in spreadsheets, such as conversations or the reasons behind decisions found in email, Word documents and correspondence.

The solution is before us: leveraging artificial intelligence and its more nuanced partner, natural language processing. What AI and NLP can do for the DoD is nearly endless, from optimizing existing talent and tracking security threats to synthesizing enterprisewide data for real-time information. What is pivotal is that NLP allows us to integrate dark data, rendering it discoverable and justifiable.

Enterprisewide AI systems create a brain that optimizes data and connections to accomplish the mission and keeps other information in a state of readiness, like nodes and synapses of the nervous system. By leveraging these technologies, entire departments of spreadsheet jockeys or time- and labor-consuming data calls are no longer needed for reporting.

Ingesting the data from multiple sources as well as removing the human factor from data handling and reporting protects it from error corruption. Better still, reports can be generated in moments with real-time data, while still allowing departments the autonomy to manage projects in their existing platforms.

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Traditional approaches are inadequate to process the DoDs massive amount of data and sources. Treating the DoD as an enterprise and leveraging AI and NLP will maximize staff capabilities and make data available, accessible, reliable and secure while providing optimized decisions for budgets, intelligence and actions. Departments can save millions of manhours and dollars so other programs can be funded and staff can utilize the exacting analytics produced.

This solution can also be applied to the budgeting and the program objective memorandum processes. Imagine being able to make precise and specific trade-off analyses to see the effects of budget cuts and their consequences. Solving point solutions around data and automation to get answers, and pulling those applications together, is the formula that the DoD can use to know where programs are connected systemwide. Digital workflows of that information could also assess trade-offs during budget builds, and the real-time data would allow movement within the timeline to inform the current situation and manage decisions.

Our enemies are leveraging technology to work against American defenses. We need to use AI, NLP and other advanced technology to collaborate content and innovate in support of our security and operations. When there is so much innovation and opportunity in the digital stratosphere, matters of national security need every tool at our disposal. Let us not forget the mission is to arm departments and the war fighter with tools for defense, response, communication, action and lethality. Using innovative technology will give our leaders accurate and timely scenarios with a single source of truth and perspective so they have the confidence in the decisions that affect and protect us all.

Dan Naselius is president and chief technology officer at data solutions firm CORAS.

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Unlock AI to transform the Pentagon - C4ISRNet

Date/Time: December 10, 2020 (1-2PM ET / 10-11AM PT)

The challenges facing recycling in the U.S. may seem daunting but cross-sector collaboration is providing a path forward on many of its toughest issues. This kind of collaboration - CPG companies working hand-in-hand with technological innovators, MRF operators and investors - will be critical to solving logjams and current hurdles to improving recycling in the United States. Leaders from AMP Robotics, GFL Environmental, Keurig Dr Pepper and Sidewalk Infrastructures sit down to discuss how their work together is bringing about much needed change to our recycling systems and how this collaborative systems approach proves the power of cross-sector action to address critical issues.

Moderator:

Speakers:

If you can't tune in live, please register and we will email you a link to access the archived webcast footage and resources, available to you on-demand after the webcast.

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How AI and Robotics are Transforming Recycling | Greenbiz - GreenBiz

All 20 lawmakers are smiling in their official photos. Googles top suggested labels noted a smile for only one of the men, but for seven of the women. The companys AI vision service labeled all 10 of the men as businessperson, often also with official or white collar worker. Only five of the women senators received one or more of those terms. Women also received appearance-related tags, such as skin, hairstyle, and neck, that were not applied to men.

Amazon and Microsofts services appeared to show less obvious bias, although Amazon reported being more than 99 percent sure that two of the 10 women senators were either a girl or kid. It didnt suggest any of the 10 men were minors. Microsofts service identified the gender of all the men, but only eight of the women, calling one a man and not tagging a gender for another.

Google switched off its AI vision services gender detection earlier this year, saying that gender cannot be inferred from a persons appearance. Tracy Frey, managing director of responsible AI at Googles cloud division, says the company continues to work on reducing bias and welcomes outside input. We always strive to be better and continue to collaborate with outside stakeholderslike academic researchersto further our work in this space, she says. Amazon and Microsoft declined to comment; both companies services recognize gender only as binary.

The US-European study was inspired in part by what happened when the researchers fed Googles vision service a striking, award-winning image from Texas showing a Honduran toddler in tears as a US Border Patrol officer detained her mother. Googles AI suggested labels including fun, with a score of 77 percent, higher than the 52 percent score it assigned the label child. WIRED got the same suggestion after uploading the image to Googles service Wednesday.

Schwemmer and his colleagues began playing with Googles service in hopes it could help them measure patterns in how people use images to talk about politics online. What he subsequently helped uncover about gender bias in the image services has convinced him the technology isnt ready to be used by researchers that way, and that companies using such services could suffer unsavory consequences. You could get a completely false image of reality, he says. A company that used a skewed AI service to organize a large photo collection might inadvertently end up obscuring women businesspeople, indexing them instead by their smiles.

When this image won World Press Photo of the Year in 2019 one judge remarked that it showed "violence that is psychological." Google's image algorithms detected "fun."

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When AI Sees a Man, It Thinks 'Official.' A Woman? 'Smile' - WIRED

Cyber-criminals are just getting started with their malicious targeting and abuse of artificial intelligence (AI), according to a new report from Europol and the UN.

Compiled with help from Trend Micro, the Malicious Uses and Abuses of Artificial Intelligence report predicts AI will in the future be used as both attack vector and attack surface.

In effect, that means cyber-criminals are looking for ways to use AI tools in attacks, but also methods via which to compromise or sabotage existing AI systems, like those used in image and voice recognition and malware detection.

The report warned that, while deepfakes are the most talked about malicious use of AI, there are many other use cases which could be under development.

These include machine learning or AI systems designed to produce highly convincing and customized social engineering content at scale, or perhaps to automatically identify the high-value systems and data in a compromised network that should be exfiltrated.

AI-supported ransomware attacks might feature intelligent targeting and evasion, and self-propagation at high speed to cripple victim networks before theyve had a chance to react, the report argued.

By finding blind spots in detection methods, such algorithms can also highlight where attackers can hide safe from discovery.

AI promises the world greater efficiency, automation and autonomy. At a time where the public is getting increasingly concerned about the possible misuse of AI, we have to be transparent about the threats, but also look into the potential benefits from AI technology. said Edvardas ileris, head of Europols Cybercrime Center.

This report will help us not only to anticipate possible malicious uses and abuses of AI, but also to prevent and mitigate those threats proactively. This is how we can unlock the potential AI holds and benefit from the positive use of AI systems.

To that end, the paper highlights multiple areas where industry and law enforcement can come together to pre-empt the risks highlighted earlier. These include the development of AI as a crime-fighting tool and new ways to build resilience into existing AI systems to mitigate the threat of sabotage.

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UN and Europol Warn of Growing AI Cyber-Threat - Infosecurity Magazine

Finance leaders are driving digital transformation in their organizations.

This month, I have been speaking with the CFOs of software companies to gain a better sense of how the finance function operates in this industry, as well as the benefits that these CFOs companies products offer to finance professionals across other sectors. For the second part of this series, I spoke with Sue Savage, CFO of Blue Yonder, whose software specializes in supply chain management, manufacturing planning, retail planning and operations. In addition to delving into Blue Yonders market position and how software is impacting the finance field, I also asked Savage about how increased automation has transformed the role of the finance professional, and what this has meant for her as it related to business strategy. I also asked about the challenges she has faced as a female CFO, and how these challenges serve as a catalyst for rethinking the way organizations operate.

Jeff Thomson: One of the biggest challenges for global companies this year has been the disruption of supply chains. As the CFO of a leading company in the supply chain software space, how do you think software can be used by finance leaders to manage disruptions? What does real-time data mean for improved supply chain management?

Sue Savage: Having the right partner is key to mitigating supply chain disruptions. As software moves to the cloud, SaaS solutions enable artificial intelligence (AI) and machine learning (ML) capabilities that are not possible with on-premise software. Finance leaders need to make the commitment to move their company to the new digital offerings, which have tremendous long-term benefits [for] customer experience and the bottom line. AI and ML, combined with real-time data, allow companies to assess a situation, incorporate essential information and make real-time changes to address potential disruptions, ensuring on-time delivery of the right goods and services.

Blue Yonder is committed to helping companies build more resilient supply chains. At the onset of Covid-19, Blue Yonder expanded its offerings to help customers minimize the impacts of the pandemic on their global supply chains, while ensuring critical supplies got to the people who needed them the most. One popular offering was the Covid-19 Supply Chain Risk Response. With little historical data on the virus, Blue Yonders data science team fed data into the AI-driven control tower to help predict current and future impacts of coronavirus. The solution connected to the CDCs live feed to receive daily data on Covid-19 and then layered customer demand, supply and inventory information over it, enabling real-time visibility and impacts [on] the customers supply chain to identify areas where decisions needed to be made. Customers who utilized this solution were able to keep their supply chains moving during a time when consumers were depending on them.

Thomson: Blue Yonder focuses on supply chain management, but software more broadly has driven digital transformation across every area of business. How have you and the finance function played a role in your companys digital transformation? How should CFOs in other companies view their roles in the integration of software into business processes?

Sue Savage, CFO of Blue Yonder

Savage: The digital transformation is a generational change in the way business is conducted. SaaS and cloud solutions allow companies to truly transform to reach new customers, enable omnichannel sales and enhance productivity. CFOs need to take a leadership role in their companys digital transformation by aligning strategic direction with digital capabilities, building the business case for strong return on investment and then ensuring adoption. As a CFO, my imperative is to lead the business to incorporate digitization to improve customer experience, enhance productivity and reduce risk. In addition, the use of clear metrics to assess return on investment can drive accountability and ensure success. Before making any investment, CFOs should work with their internal and external partners to ensure the metrics meet the companys needs and are measurable.

Thomson: A consequence of the digital revolution has been the emergence of the finance professional as a strategic business partner, as routine and time-consuming tasks have been increasingly automated. This enables the finance professional to spend more time on value-added tasks. How have you seen the role of the finance professional transformed over the course of your career? Do you think this requires a rethink of how finance professionals are educated and trained, with an eye toward enhancing technology skills and proficiency? What about soft skills like communication and leadership?

Savage: The days of limiting finance to the realm of bookkeeping are gone, if they ever truly existed.Transactional tasks were digitized long ago. AI and ML are automating reporting and analysis. In addition to functional capabilities, finance professionals now need leadership, strategic vision, influencing skills and strong technology capabilities to be successful. Business schools incorporate business strategy, technology capabilities, communications and leadership skills, and interpersonal dynamics into their curriculum. Many companies also provide on-the-job training to enhance development in these areas, such as courses on situational leadership or influencing others.At Blue Yonder, our Finance team has developed technology assessments to use as additional data in our hiring decisions, as well as to provide recommended learning paths for current associates.Ultimately, developing and refining these skills is a lifelong process, requiring awareness and focus.

Thomson: Before joining Blue Yonder, you worked with software company Autodesk and led or participated in two major initiatives: the switch from a perpetual license to a subscription-based business model and redesigning sales compensation and partner incentive plans. Could you discuss how finance professionals can bring unique perspectives to business model transformation initiatives? What insights do you have for CFOs and other finance professionals as they are called upon to contribute to these high-level initiatives?

Savage: Understanding the business strategy and objectives is key to adding value to any project. While our views as finance professionals may differ from other parts of the company, they are equally valuable. We bring unique questions, perspective and background to every project. I remember my first meeting at Autodesk on sales compensation. I felt like I had been dropped into a new world with a totally different language. By focusing on the objectives of the compensation plan and how it aligned with company strategy, I was able to dig in, ask questions and bring value right away. My business partner at the time appreciated my orthogonal view.

Having a strong network is also helpful to tap into for benchmarking and keeping a pulse on developments in the industry. Solid external relationships allow you to ask questions and gain insights, as well as lessons learned, from those who may have implemented similar initiatives. Leaning on this network can be very helpful when implementing high-level initiatives.

Thomson: Women are typically under-represented in the finance function. What challenges did you face as a female finance professional and what helped you overcome those obstacles to succeed in becoming a CFO? What can organizations and leaders do to help other women do the same?

Savage: I feel very lucky because I dont think I have been treated differently for being a woman. In fact, I had very strong female role models and mentors in my career including the two female CEOs for whom I worked: Carol Bartz and Ruann Ernst. The biggest challenge I faced was trying to manage a growing career while raising my daughter. People talk about balancing their work and home lives, but for me flexibility was key. Balance implies some perfect split of time and attention, and life isnt like that. Flexibility allowed me to focus on what was most important in the moment. This included working from home when I needed to or stepping out in the middle of the day for a few hours for a school performance, sporting event or doctor appointment, and then working from home after everyone was in bed to catch up. Having flexibility allowed me to support my daughter and do my job on my schedule. Now I encourage this flexibility in my workplace as a tool to increase engagement and retention. Companies should consider how flexibility ultimately increases their ability to attract and retain more diverse talent.

This article has been edited and condensed.

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Why Data, Software And AI Are Key To Growth: A Q&A With Blue Yonders CFO - Forbes

The future of corporate cybersecurity seems to lie in artificial intelligence (AI) and machine learning (ML) solutions, a new report from global IT company Wipro suggests.

According to Wipros annual State of Cybersecurity Report (SOCR), almost half (49 percent) of all cybersecurity-related patents filed in the last four years have centered on AI and ML application.

Almost half of the 200 organizations that participated in the report also said they are expanding cognitive detection capabilities to tackle unknown attacks in their Security Operations Centers (SOC).

From a global perspective, one of the main threats for organizations in the private sector seems to be potential espionage attacks from nation-states. Almost all (86 percent) cyberattacks that came from state-sponsored actors fall under the espionage category and almost half (46 percent) of those attacks targeted the private sector.

IT security teams are also stretched too thin, as emphasized by the pandemic. Too many endpoints, complex and cumbersome infrastructure and employees that choose convenience over security all contribute to the problem.

Security experts are hoping that, with AI and ML, they will at least be able to better differentiate between false positives and actual red flags, and be able to delegate repetitive tasks to software, while freeing up time for more important tasks.

Excerpt from:

AI could be the next big defence against cybercrime - ITProPortal

The views expressed here are solely those of the author and do not necessarily represent the views of FreightWaves or its affiliates.

In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore how Pathmind, an early-stage startup based in San Francisco, is helping companies apply simulation and reinforcement learning to industrial operations.

I asked Chris Nicholson, CEO and founder of Pathmind, What is the problem that Pathmind solves for its customers? Who is the typical customer?

Nicholson said: The typical Pathmind customer is an industrial engineer working at a simulation consulting firm or on the simulation team of a large corporation with industrial operations to optimize. This ranges from manufacturing companies to the natural resources sector, such as mining and oil and gas. Our clients build simulations of physical systems for routing, job scheduling or price forecasting, and then search for strategies to get more efficient.

Pathminds software is suited for manufacturing resource management, energy usage management optimization and logistics optimization.

As with every other startup that I have highlighted as a case in this #AIinSupplyChain series, I asked, What is the secret sauce that makes Pathmind successful? What is unique about your approach? Deep learning seems to be all the rage these days. Does Pathmind use a form of deep learning? Reinforcement learning?

Nicholson responded: We automate tasks that our users find tedious or frustrating so that they can focus on whats interesting. For example, we set up and maintain a distributed computing cluster for training algorithms. We automatically select and tune the right reinforcement learning algorithms, so that our users can focus on building the right simulations and coaching their AI agents.

Echoing topics that we have discussed in earlier articles in this series, he continued: Pathmind uses some of the latest deep reinforcement learning algorithms from OpenAI and DeepMind to find new optimization strategies for our users. Deep reinforcement learning has achieved breakthroughs in gaming, and it is beginning to show the same performance for industrial operations and supply chain.

On its website, Pathmind describes saving a large metals processor 10% of its expenditures on power. It also describes the use of its software to increase ore preparation by 19% at an open-pit mining site.

Given how difficult it is to obtain good quality data for AI and machine learning systems for industrial settings, I asked how Pathmind handles that problem.

Simulations generate synthetic data, and lots of it, said Slin Lee, Pathminds head of engineering. The challenge is to build a simulation that reflects your underlying operations, but there are many tools to validate results.

Once you pass the simulation stage, you can integrate your reinforcement learning policy into an ERP. Most companies have a lot of the data they need in those systems. And yes, theres always data cleansing to do, he added.

As the customer success examples Pathmind provides on its website suggest, mining companies are increasingly looking to adopt and implement new software to increase efficiencies in their internal operations. This is happening because the industry as a whole runs on very old technology, and deposits of ore are becoming increasingly difficult to access as existing mines reach maturity. Moreover, the growing trend toward the decarbonization of supply chains, and the regulations that will eventually follow to make decarbonization a requirement, provide an incentive for mining companies to seize the initiative in figuring out how to achieve that goal by implementing new technology

The areas in which AI and machine learning are making the greatest inroads are mineral exploration using geological data to make the process of seeking new mineral deposits less prone to error and waste; predictive maintenance and safety using data to preemptively repair expensive machinery before breakdowns occur; cyberphysical systems creating digital models of the mining operation in order to quickly simulate various scenarios; and autonomous vehicles using autonomous trucks and other autonomous vehicles and machinery to move resources within the area in which mining operations are taking place.

According to Statista, The revenue of the top 40 global mining companies, which represent a vast majority of the whole industry, amounted to some 692 billion U.S. dollars in 2019. The net profit margin of the mining industry decreased from 25 percent in 2010 to nine percent in 2019.

The trend toward mining companies and other natural-resource-intensive industries adopting new technology is going to continue. So this is a topic we will continue to pay attention to in this column.

Conclusion

If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, wed love to tell your story at FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.

Dig deeper into the #AIinSupplyChain Series with FreightWaves:

Commentary: Optimal Dynamics the decision layer of logistics? (July 7)

Commentary: Combine optimization, machine learning and simulation to move freight (July 17)

Commentary: SmartHop brings AI to owner-operators and brokers (July 22)

Commentary: Optimizing a truck fleet using artificial intelligence (July 28)

Commentary: FleetOps tries to solve data fragmentation issues in trucking (Aug. 5)

Commentary: Bulgarias Transmetrics uses augmented intelligence to help customers (Aug. 11)

Commentary: Applying AI to decision-making in shipping and commodities markets (Aug. 27)

Commentary: The enabling technologies for the factories of the future (Sept. 3)

Commentary: The enabling technologies for the networks of the future (Sept. 10)

Commentary: Understanding the data issues that slow adoption of industrial AI (Sept. 16)

Commentary: How AI and machine learning improve supply chain visibility, shipping insurance (Sept. 24)

Commentary: How AI, machine learning are streamlining workflows in freight forwarding, customs brokerage (Oct. 1)

Commentary: Can AI and machine learning improve the economy? (Oct. 8)

Commentary: Savitude and StyleSage leverage AI, machine learning in fashion retail (Oct. 15)

Commentary: How Japans ABEJA helps large companies operationalize AI, machine learning (Oct. 26)

Authors disclosure: I am not an investor in any early-stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.

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Commentary: Pathmind applies AI, machine learning to industrial operations - FreightWaves