Daily Archives: November 20, 2020

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

The news: A new AI model for summarizing scientific literature can now assist researchers in wading through and identifying the latest cutting-edge papers they want to read. On November 16, the Allen Institute for Artificial Intelligence (AI2) rolled out the model onto its flagship product, Semantic Scholar, an AI-powered scientific paper search engine. It provides a one-sentence tl;dr (too long; didnt read) summary under every computer science paper (for now) when users use the search function or go to an authors page. The work was also accepted to the Empirical Methods for Natural Language Processing conference this week.

AI2

The context: In an era of information overload, using AI to summarize text has been a popular natural-language processing (NLP) problem. There are two general approaches to this task. One is called extractive, which seeks to find a sentence or set of sentences from the text verbatim that captures its essence. The other is called abstractive, which involves generating new sentences. While extractive techniques used to be more popular due to the limitations of NLP systems, advances in natural language generation in recent years have made the abstractive one a whole lot better.

How they did it: AI2s abstractive model uses whats known as a transformera type of neural network architecture first invented in 2017 that has since powered all of the major leaps in NLP, including OpenAIs GPT-3. The researchers first trained the transformer on a generic corpus of text to establish its baseline familiarity with the English language. This process is known as pre-training and is part of what makes transformers so powerful. They then fine-tuned the modelin other words, trained it furtheron the specific task of summarization.

The fine-tuning data: The researchers first created a dataset called SciTldr, which contains roughly 5,400 pairs of scientific papers and corresponding single-sentence summaries. To find these high-quality summaries, they first went hunting for them on OpenReview, a public conference paper submission platform where researchers will often post their own one-sentence synopsis of their paper. This provided a couple thousand pairs. The researchers then hired annotators to summarize more papers by reading and further condensing the synopses that had already been written by peer reviewers.

To supplement these 5,400 pairs even further, the researchers compiled a second dataset of 20,000 pairs of scientific papers and their titles. The researchers intuited that because titles themselves are a form of summary, they would further help the model improve its results. This was confirmed through experimentation.

AI2

Extreme summarization: While many other research efforts have tackled the task of summarization, this one stands out for the level of compression it can achieve. The scientific papers included in the SciTldr dataset average 5,000 words. Their one-sentence summaries average 21. This means each paper is compressed on average to 238 times its size. The next best abstractive method is trained to compress scientific papers by an average of only 36.5 times. During testing, human reviewers also judged the models summaries to be more informative and accurate than previous methods.

Next steps: There are already a number of ways that AI2 is now working to improve their model in the short term, says Daniel Weld, a professor at the University of Washington and manager of the Semantic Scholar research group. For one, they plan to train the model to handle more than just computer science papers. For another, perhaps in part due to the training process, theyve found that the tl;dr summaries sometimes overlap too much with the paper title, diminishing their overall utility. They plan to update the models training process to penalize such overlap so it learns to avoid repetition over time.

In the long-term, the team will also work summarizing multiple documents at a time, which could be useful for researchers entering a new field or perhaps even for policymakers wanting to get quickly up to speed. What we're really excited to do is create personalized research briefings, Weld says, where we can summarize not just one paper, but a set of six recent advances in a particular sub-area.

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An AI helps you summarize the latest in AI - MIT Technology Review

By Tessa Venell '08Nov. 19, 2020

Imagine a classroom in the future where teachers are working alongside artificial intelligence partners to ensure no student gets left behind.The AI partners careful monitoring picks up on a student in the back who has been quiet and still for the whole class and the AI partner prompts the teacher to engage the student. When called on, the student asks a question. The teacher clarifies the material that has been presented and every student comes away with a better understanding of the lesson.This is part of a larger vision of future classrooms where human instruction and AI technology interact to improve educational environments and the learning experience.James Pustejovsky, the TJX Feldberg Professor of Computer Science, is working towards that vision with a team led by the University of Colorado Boulder, as part of the new $20 million National Science Foundation-funded AI Institute for Student-AI Teaming.The research will play a critical role in helping ensure the AI agent is a natural partner in the classroom, with language and vision capabilities, allowing it to not only hear what the teacher and each student is saying, but also notice gestures (pointing, shrugs, shaking a head), eye gaze, and facial expressions (student attitudes and emotions).

Pustejovsky took some time to answer questions from BrandeisNOW about his research.

How does your research help build this classroom of the future?For the past five years, we have been working to create a multimodal embodied avatar system, called Diana, that interacts with a human to perform various tasks. She can talk, listen, see, and respond to language and gesture from her human partner, and then perform actions in a 3D simulation environment called VoxWorld. This is work we have been conducting with our collaborators at Colorado State University, led by Ross Beveridge in their vision lab. We are working together again (CSU and Brandeis) to help bring this kind of embodied human computer interaction into the classroom. Nikhil Krishnaswamy, my former Ph.D. student and co-developer of Diana, has joined CSU as part of their team.How does it work in the context of a classroom setting?At first its disembodied, a virtual presence on an iPad, for example, where it is able to recognize the voices of different students. So imagine a classroom: Six to 10 children in grade school. The initial goal in the first year is to have the AI partner passively following the different students, in the way they're talking and interacting, and then eventually the partner will learn to intervene to make sure that everyone is equitably represented and participating in the classroom.Are there other settings that Diana would be useful in besides a classroom?Let's say I've got a Julia Child app on my iPad and I want her to help me make bread. If I start the program on the iPad, the Julia Child avatar would be able to understand my speech. If I have my camera set up, the program allows me to be completely embedded and embodied in a virtual space with her so that she can help me.

Screenshot of the embodied avatar system Diana."

How does she help you?She would look at my table and say, Okay, do you have everything you need. And then Id say, I think so. So the camera will be on, and if you had all your baking materials laid out on your table, she would scan the table. She'd say, I see flour, yeast, salt, and water, but I don't see any utensils: you're going to need a cup, you're going to need a teaspoon. After you had everything you needed, she would tell you to put the flour in that bowl over there. And then she'd show you how to mix it.

Is that where Diana comes in?Yes, Diana is basically becoming an embodied presence in the human-computer interaction: she can see what you're doing, you can see what she's doing. In a classroom interaction, Diana could help with guiding students through lesson plans, through dialogue and gesture, while also monitoring the students progress, mood, and levels of satisfaction or frustration.Does Diana have any uses in virtual learning in education?

Using an AI partner for virtual learning could be a fairly natural interaction. In fact, with a platform such as Zoom, many of the computational issues are actually easier since voice and video tracks of different speakers have already been segmented and identified. Furthermore, in a Hollywood Squares display of all the students, a virtual AI partner may not seem as unnatural, and Diana might more easily integrate with the students online.What stage is the research at now?Within the context of the CU Boulder-led AI Institute, the research has just started. Its a five-year project, and its getting off the ground. This is exciting new research that is starting to answer questions about using our avatar and agent technology with students in the classroom.

The research is funded by the National Science Foundation, and partners with CU Boulder on the research include Brandeis University, Colorado State University, the University of California, Santa Cruz, UC Berkeley, Worcester Polytechnic Institute, Georgia Institute of Technology, University of Illinois at Urbana-Champaign, and University of Wisconsin-Madison.

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Artificial intelligence and the classroom of the future | BrandeisNOW - Brandeis University

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasnt changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

One of the big pain points [businesses] had was, look, I have data scientists and I have my models that Ive built in-house. My data scientists have built them on laptops, but I dont know how to push them to production. I dont know how to maintain and keep models in production. I think pretty much every startup now is thinking of that problem, Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so its making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didnt really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didnt have a large enough sales team. And raising a little early made sense to build up the sales team, she said.

Reddy also stressed that unlike some of the companys competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That and the engineering talent to build it doesnt come cheap.

Image Credits: Abacus.AI

Its no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model and is seeing good traction with small- and medium-sized companies you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AIs unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups, Yanda Erlich, a p artner at Coatue Ventures told me. Abacus.AIs end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.

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Abacus.AI raises another $22M and launches new AI modules - TechCrunch

A top researcher at the Massachusetts Institute of Technology on Thursday said that artificial intelligence systems developed for medicine must be more transparent and judged against a set of common standards to ensure fairness and equity.

Its really important, whenever we are developing these models, to train them on diverse populations and report their accuracy, slicing and dicing for different subpopulations, Regina Barzilay, a professor of engineering and computer science at MIT, said Thursday at the STAT Summit. Its very easy with these models, if theyre trained on one population and then applied on anotherto provide inequitable care.

Unlock this article by subscribing to STAT+ and enjoy your first 30 days free!

STAT+ is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.

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A leading AI researcher calls for standards to ensure equity and fairness - STAT

No truck maker has had a more tumultuous 2020 than Nikola Corp. The maker of battery-electric and fuel-cell electric drivetrain components and trucks, went from zero-emissions transport messiah at the end of spring, when the company went public, to beingcompared to Theranosthe fraudulent and now defunct medical device manufacturer once valued at $10 billionshortly after Labor Day.

Thats when a short seller called Hindenburg Research released its now infamousreport levying claimsthat the innovative hydrogen fuel-cell technology purported to make zero-emission long haul possible and inevitable was a sham and that Trevor Milton, Nikolas outspoken firebrand founder and chairman, was a charlatan.

It could not have come at a worse time, as the company had broken ground on its Coolidge, Ariz., factory in August and just days before the Hindenburg Report dropped, had announced a groundbreaking partnership with General Motors, who would supply Nikolas under-development Class 8 semis and pickup trucks with batteries and fuel cells.

Miltonbriefly fought the allegations before resigning. A November regulatory filing revealed the Securities and Exchange Commission and Department of Justice issued subpoenas related to the alleged fraud, which basically accused Nikola of misrepresenting its progress to investors. Nikola denies these claims.

The damage was done, though, as Nikola shares halved from $50 to Sept. 8, when the GM deal was announced, to around $25 currently.

Despite all of these tectonic shifts, Nikola's new CEO Mark Russell portrayed Nikola as still having a foundation solid enough from which to disrupt the clean trucking sector and make a serious impact on climate change when he kicked off the latest ACT Virtual event on Nov. 17.

Nikola CEO Mark RussellPhoto: Nikola Corp.

The companys mission remains the same.

Nikola was founded back in 2015, with a passion and a vision for a future that would be sustainable, that would be clean, and that would leave a better planet for all of those who come after us, Russell said.

Here is the current timeline in which Nikola believes its solutions will be available to aid that sustainability effort:

The CEO pointed to partnerships and investments from Bosch, CNH Industrial and Hanwha totaling $480 million.

Financial analysts agreed that network of partners is a major plus for Nikola.

We think this core criticism is misconceived,JP Morgan said in a statementshortly after the Hindenburg report was released. We believe the ability to execute by leveraging other companies core competences specifically GM, CNH and Bosch is a major positive, not a negative. We also think speed, flexibility and unencumbered readiness to change course quickly with evolving circumstance in a fast-developing market are fundamental and desirable attributes of a disruptor.

If the GM deal, worth $2 billion in Nikola stock, is not approved in December, Russell had previously stated Bosch and Romeo Power would provide the battery and fuel-cell components.

Once the company starts producing trucks next year, initially rolling out the Class 8 Nikola Tre in Ulm, Germany, out of an Iveco plant.

The first batch of prototypes will be completed this year, and will begin validation and testing and regulatory approvals, Russell said. We'll have another batch of those prototypes next year, and then we'll move into serial production next year.

Once that happens, Nikola will have a very large customer base hungry for ZETs.

Carbon emissions from heavy trucking and from transportation in general have grown faster than any other segment of the economy, Russell noted during the ACT keynote. He added that like European nations that have already set forth carbon neutral goals in the next 15 years (with Norway aggressively targeting 2025), the U.S. must get rid of fossil fuels and start moving towards a zero emission future for heavy transportation and commercial trucking.

Many states have already taken broad steps to push the adoption of zero-emission trucks (ZETs), with California leading the way. Through the California Air Resources Board, the state has provided funding for several projects, including battery-electric truck pilots with OEMs Daimler Trucks North America and Volvo Trucks North America, respectively. DTNAs eCascadias, due out at the end of 2021, have accrued half a million miles.BYD also announced DHLwill deploy its Class 8 electric trucks in the Los Angeles area. DHL projects its deployment could remove 300 metric tons of greenhouse gas emissions annually.

Now youve got other states catching up to and even talking about passing California, and other forward-thinking leaders are talking about making these kind of things nationwide, which we support, Russell said.

California and 14 other states, along with Washington D.C., signed theMulti-State Medium- and Heavy-Duty Zero-Emission Vehicle Memorandum of Understandingto incrementally increase the sales of ZETs to 30% in those areas by 2030. And with the Biden-Harris administration poised to take over the executive branch in January, a national push towards electrification is a foregone conclusion. President-elect Joe Bidens infrastructure plan prominently features electric vehicles and charging infrastructure, while Vice President-elect Kamala Harris co-sponsored the Green New Deal.

The Tre and BEV version of the Badger pickup truck will benefit from any incentives and electric infrastructure provided by the government, though it remains unclear how much federal effort and funding hydrogen vehicles will receive in the next four years.

The inaugural crop of electric trucks are limited to a range of under 300 miles, keeping them bound to regional and drayage duty cycles, so for zero-emissions trucking to become a universal solution by the current timetables, hydrogen must also be developed.

Hydrogen has many advantages besides the fact that the zero emission, Russell said. It's so energy dense as a fuel. When batteries are trying to move something heavy far distances, you start to run into the problem of energy density.

The amount of energy in a kilo of hydrogen is the equivalent of about three kilos of fossil fuels, he continued. It's the only thing out there that we know of that can beat fossil fuels as a transportation fuel.

Breakdown of energy density by weight among diesel, battery-electric and fuel cell electric trucks

Recently, most of the traditional heavy-duty OEMs have also explored fuel-cell powertrains.Kenworth and Toyotahave had a partnership in place since 2019 to fit the Kenworth T680 with fuel cells. Toyota Motors North America and Hinowill also begin testing fuel-cell electric trucks (FCET) based off the Hino XL Series on North American roads in 2021.

A fuel cell-powered version of the Hino XL Series is a win-win for both customers and the community. It will be quiet, smooth and powerful while emitting nothing but water, said Tak Yokoo, senior executive engineer with Toyota research and development

Daimler AG and Volvo Groupalso formed a strategic partnership this summer, which became a binding agreement in November.

For us at Daimler Truck AG and our intended partner, the Volvo Group, the hydrogen-based fuel-cell is a key technology for enabling CO2-neutral transportation in the future, said Martin Daum, chairman and member of the board of management at Daimler Truck AG. We are both fully committed to the Paris Climate Agreement for decarbonizing road transport and other areas, and to building a prosperous jointly held company that will deliver large volumes of fuel-cell systems.

Daimler has tried to develop a feasible way to convert liquid hydrogen to electricity since the 1990s. Series production on the first Class 8 FCET, the Mercedes-Benz GenH2 Truck, is slated to begin sometime after 2024.

Navistar has also partnered with Cumminsto develop a fuel-cell optionof the International RH Series.

This vehicle will feature our next generation fuel-cell configuration and provides a springboard for us to advance our hydrogen technology for line haul trucks, said Amy Davis, vice president and president of new power at Cummins.

Hyundai Motor Co. may be the first to market with itsXCIENT Fuel Cell trucksin 2022. Testing should begin next year.

While the questions around electric truck charging are currently being practically figured out and vetted, understanding how to realistically install the hydrogen stations to power fuel-cell electric trucks is still in the nascent stages.

We had to solve the chicken-and-egg problem that's always been involved when you're talking about hydrogen fuel-cell vehicles, Russell said.

They believe they have solved that problem and have 700 hydrogen stations planned in the U.S. and more plans to grow in the European market over the next decade in three phases.

Nikola has also joined a consortium to fuel heavy-duty FCETs that includes Hyundai, Toyota, Air Liquide, Nel and Shell. This will help standardize components such as hoses, nozzles and receptacles, Russell said. The fact that FCETs can fill up in the same time as a diesel truck, versus several hours for EVs, is another major selling point.

Comparison of charging time among diesel, battery-electric and fuel cell electric trucksGraph: Nikola Corp.

Theres one thing that helps Nikola stand apart.

What's unique about Nikola is that we offer a bundled lease, Russell explained. We're going to sell you a fuel-cell truck for your long-range transportation needs, we're going to service and support and maintain that truck for you, and we're going to provide the fuel in one bundled lease.

Russell noted that right now the trucking industry spends $100 billion a year on fuel and maintenance. His predecessor Milton said last Maythat bundling these costs could make a Nikola FCET 20 to 30% less than a diesel truck.

More importantly, Russell stressed, is that the companys overall mission to improve air quality is still on track.

Nikola is about establishing a safe, clean, sustainable, zero emission future for all of us on this planet and helping to solve one of the toughest problems, which is how do you get heavy things, long ways across far distances, Russell concluded.

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Do You Have A Vision For AI? - IndustryWeek

Reading scientific papers is a tough job. It might contain language and sections that you might not understand, and not all of it would be interesting to you.

My colleague Tristan is great at traversing through these papers, but Im just a novice. So, I would want a quick summary of a research document to decide if I want to dedicate more time reading it.

Thankfully, researchers at theAllen Institute for Artificial Intelligencehave developed a new model to summarize text from scientific papers, and present it in a few sentences in the form of TL;DR (Too Long Didnt Read).

[Read: Neurals market outlook for artificial intelligence in 2021 and beyond]

The team has rolled this model out to the Allen Institutes Semantic Scholar search engine for papers. Currently, youll only see these TL;DR summaries on papers related to computer science on search results or the authors page.

AI takes the most important parts from the abstract, introduction, and conclusion section of the paper to form the summary.

Researchers first pre-trained the model on the English language. Then they created aSciTLDR data set of over 5,400 summaries of computer science papers. It was further trained on more than 20,000 titles of research papers to reduce dependency on domain knowledge while writing a synopsis.

The trained model was able to summarize documents over 5,000 words in just 21 words on an average thats a compression ratio of 238. The researchers now want to expand this model to papers in fields other than computer science.

You can try out the AI on the Semantic Scholar search engine. Plus, you can read more about summarizing AI in this paper.

Published November 20, 2020 10:35 UTC

The rest is here:

TL;DR: This AI summarizes research papers so you dont have to - The Next Web

In one Telegram group chat about the bot, its owner says that Telegram has blocked mentions of its name. However, WIRED was unable to confirm this or any action taken by Telegram. Neither Telegrams spokesperson or the services founder, Pavel Durov, responded to requests for comment. The company, which is believed to be based in Dubai but has servers all around the world, has never publicly commented about the harm caused by the Telegram bot or its continued position to allow it to operate.

Since it was founded in 2013, Telegram has positioned itself as a private space for free speech, and its end-to-end encrypted mode has been used by journalists and activists around the world to protect privacy and evade censorship. However, the messaging app has run into trouble with problematic content. In July 2017, Telegram said it would create a team of moderators to remove terrorism-related content after Indonesia threatened it with a ban. Apple also temporarily removed it from its App Store in 2018 after finding inappropriate content on the platform.

I think they [Telegram] have a very libertarian perspective towards content moderation and just any sort of governance on their platform, says Mahsa Alimardani, a researcher at the Oxford Internet Institute. Alimardani, who has worked with activists in Iran, points to Telegram notifying its users about a fake version of the app created by authorities in the country. It seems that the times that they have actually acted, it's when state authorities have got involved.

On October 23, Italys data protection body, the Garante per la Protezione dei dati Personali, opened an investigation into Telegram and has asked it to provide data. In a statement, the regulator said the nude images generated by the bot could cause irreparable damage to their victims. Since Italian officials opened their investigation, Patrini has conducted more research looking for deepfake bots on Telegram. He says there are a number of Italian-language bots that appear to offer the same functionalities as the one Sensity previously found, however they do not appear to be working.

Separate research from academics of at the University of Milan and the University of Turin has also found networks of Italian-language Telegram groups, some of which were private and could only be accessed by invitation, sharing non-consensual intimate images of women that dont involve deepfake technology. Some groups they found had more than 30,000 members and required members to share non-consensual images or be removed from the group. One group focused on sharing images of women that were taken in public places without their knowledge.

Telegram should look inward and hold itself accountable, says Honza ervenka, a solicitor at law firm McAllister Olivarius, which specializes in non-consensual images and technology. ervenka says that new laws are needed to force tech companies to better protect their users and clamp down on the use of abusive automation technology. If it continues offering the Telegram Bot API to developers, it should institute an official bot store and certify bots the same way that Apple, Google, and Microsoft do for their app stores. However, ervenka adds there is little government or legal pressure being put in place to make Telegram take this kind of step.

Patrini warns that deepfake technology is quickly advancing, and the Telegram bot is a sign of what is likely to happen in the future. The bot on Telegram was the first time this type of image abuse has been seen at such a large scale, and it is easy for anyone to useno technical expertise is needed. It was also one of the first times that members of the public were targeted with deepfake technology. Previously celebrities and public figures were the targets of non-consensual AI porn. But as the technology is increasingly democratized, more instances of this type of abuse will be discovered online, he says.

This was one investigation, but we are finding these sorts of abuses in multiple places on the internet, Patrini explains. There are, at a smaller scale, many other places online where images are stolen or leaked and are repurposed, modified, recreated, and synthesized, or used for training AI algorithms to create images that use our faces without us knowing.

This story originally appeared on WIRED UK.

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Telegram Still Hasn't Removed an AI Bot That's Abusing Women - WIRED