Daily Archives: January 12, 2020

2020,a new year and this one brings along a sea change for those who deal with thesea business. Keeping in view the business as well as the environmentalaspects, the authorities concerned, have rolled out several directions that thecompanies on and off-the shores need to follow.

Amidstthe new rules and guidelines follow there are, of course, institutions growthtargets. Recounting a few directions from the authorities concerned, with theChief Operating Officer of Greensteam Learning Technology Simon Whitford, herewe have his insights on how is Greensteam taking up the challenges.

Recently,the UN announced that global GHG emissions must be reduced by 7.6% every yearfor the next decade. With regard to GreenSteams fuel saving figures, MrWithford talks about solutions they have and explains:

GreenSteams ML platform calculates a 30% non-propulsion fuel use for an average non-optimised deep sea voyage. bad weather accounts for half of this fuel use (15%) and poor vessel optimisation makes up the rest (15%). Right now, GreenSteam has tools to measure and improve vessel optimisation. The 3 target areas for vessel optimisation are trim, speed and hull cleanliness. We have solutions for trim and hull cleanliness and we are beta testing a speed optimisation solution. That means we can address this 15% of fuel wastage.

To survive this period of upheaval and changein a sector already grappling with heightened security risks, recruitmentchallenges and rising operational costs, machine learning will be a vital toolin the arsenal of any shipping company, regardless of size, location, or modeof operation.

Embracing machine learning can no longer beregarded as a nice-to-have: in the post-2020 world, it is a Must-have.

Talkingon the future strategies of the company, Mr Withford says, On our roadmap wewill apply our decade in development ML platform to route. Lets say this canhelp avoid 3% of poor weather losses. That would allow shipping companies tomeaningfully address 18% of fuel use with our zero capex, zero down-timemachine learning tools.

From 2020, shipping companies operatingoutside emissions control areas (ECAs) have to meet tighter regulations in theshape of the well-documented global 0.50% sulphur cap. The IMO 2020 has broughtthe current sulphur limit outside ECAs down from 3.5%. Technology, machinelearning to be more specific, has the solutions.

Machine learning is an advanced form ofartificial intelligence in which systems use powerful algorithms and logic tocut through the noise. By learning from experience, and by being able toidentify patterns and discover trends, decisions can be made with minimal humaninvolvement.

MrWithford, when asked about challenges replies with the term Not Exactly. Hechooses to put it very differently. He states: There is an adoptionperiod for the technology when the algorithm is learning about the ship andcrew are learning about the technology. Legacy models have to discard 90% of(good) data to create their simple, shallow stereotype of each vessel. Thiscreates a very poor image of the way the vessel will react in variousconditions, and means they cannot measure fuel wastage with any accuracy, andforecasting performance is impossible.

He adds The difficulty with machine learning is the initial 3 month learning period to capture the behaviour of the ship across a wide variety of operating conditions but this is necessary to thoroughly understand how the individual vessel will operate, divide up the areas of fuel wastage and to enable speed, trim and fouling forecasts.

Theera of the machines is finally in full swing. There is proof all over that machinelearning models are highly adaptive. They are continuously revised and refined,becoming more accurate as new data enriches the dataset. Solutions based onmachine learning enable businesses solve complex problems quickly, and muchmore effectively, than has been possible traditionally.

To sum it up, its all about improving efficiency to survive during and beyond rapid and deep change. By helping human experts to make better-informed decisions, accurate predictive automated modelling frees up the creative human for alternative tasks, which may include responding to emergencies or other unforeseen events.

Sea News Feature, January 13

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Informed decisions through machine learning will keep it afloat & going - Sea News

SAN MATEO and MOUNTAIN VIEW, Calif., Jan. 7, 2020 /PRNewswire/ --SiFive, Inc., the leading provider of commercial RISC-V processor IP and silicon solutions and CEVA, Inc. (NASDAQ: CEVA), the leading licensor of wireless connectivity and smart sensing technologies, today announced a new partnership to enable the design and creation of ultra-low-power domain-specific Edge AI processors for a range of high-volume end markets. The partnership, as part of SiFive's DesignShare program, is centered around RISC-V CPUs, CEVA's DSP cores, AI processors and software, which will be designed into SoCs targeting an array of end markets where on-device neural networks inferencing supporting imaging, computer vision, speech recognition and sensor fusion applications is required. Initial end markets include smart home, automotive, robotics, security and surveillance, augmented reality, industrial and IoT.

Machine Learning Processing at the EdgeDomain-specific SoCs which can handle machine learning processing on-device are set to become mainstream, as the processing workloads of devices increasingly includes a mix of traditional software and efficient deep neural networks to maximize performance, battery life and to add new intelligent features. Cloud-based AI inference is not suitable for many of these devices due to security, privacy and latency concerns. SiFive and CEVA are directly addressing these challenges through the development of a range of domain-specific scalable edge AI processor designs, with the optimal balance of processing, power efficiency and cost.

The Edge AI SoCs are supported by CEVA's award-winning CDNN Deep Neural Network machine learning software compiler that creates fully-optimized runtime software for the CEVA-XM vision processors, CEVA-BX audio DSPs and NeuPro AI processors. Targeted for mass-market embedded devices, CDNN incorporates a broad range of network optimizations, advanced quantization algorithms, data flow management and fully-optimized compute CNN and RNN libraries into a holistic solution that enables cloud-trained AI models to be deployed on edge devices for inference processing. CEVA will also supply a full development platform for partners and developers based on the CEVA-XM and NeuPro architectures to enable the development of deep learning applications using the CDNN, targeting any advanced network, as well as DSP tools and libraries for audio and voice pre- and post-processing workloads.

SiFive DesignShare ProgramThe SiFive DesignShare IP program offers a streamlined process for companies seeking to partner with leading vendors to provide pre-integrated premium Silicon IP for bringing new SoCs to market. As part of SiFive's business model to license IP when ready for mass production, the flexibility and choice of the DesignShare IP program reduces the complexities of contract negotiation and licensing agreements to enable faster time to market through simpler prototyping, no legal red tape, and no upfront payment.

"CEVA's partnership with SiFive enables the creation of Edge AI SoCs that can be quickly and expertly tailored to the workloads, while also retaining the flexibility to support new innovations in machine learning," said Issachar Ohana, Executive Vice President, Worldwide Sales at CEVA. "Our market leading DSPs and AI processors, coupled with the CDNN machine learning software compiler, allow these AI SoCs to simplify the deployment of cloud-trained AI models in intelligent devices and provides a compelling offering for anyone looking to leverage the power of AI at the edge."

"Enabling future-proof, technology-leading processor designs is a key step in SiFive's mission to unlock technology roadmaps," said Dr. Naveed Sherwani, president and CEO, SiFive. "The rapid evolution of AI models combined with the requirements for low power, low latency, and high-performance demand a flexible and scalable approach to IP and SoC design that our joint CEVA / SiFive portfolio is superbly positioned to provide. The result is shorter time-to-market, while lowering the entry barriers for device manufacturers to create powerful, differentiated products."

AvailabilitySiFive's DesignShare program, including CEVA-BX Audio DSPs, CEVA-XM Vision DSPs and NeuPro AI processors, is available now. Visit http://www.sifive.com/designshare for more information.

About SiFiveSiFive is on a mission to free semiconductor roadmaps and declare silicon independence from the constraints of legacy ISAs and fragmented solutions. As the leading provider of market-ready processor core IP and silicon solutions based on the free and open RISC-V instruction set architecture SiFive helps SoC designers reduce time-to-market and realize cost savings with customized, open-architecture processor cores, and democratizes access to optimized silicon by enabling system designers in all markets to build customized RISC-V based semiconductors. Founded by the inventors of RISC-V, SiFive has 16 design centers worldwide, and has backing from Sutter Hill Ventures, Qualcomm Ventures, Spark Capital, Osage University Partners, Chengwei, Huami, SK Hynix, Intel Capital, and Western Digital. For more information,please visit http://www.sifive.com.

Stay current with the latest SiFive updates via LinkedIn, Twitter, Facebook, and YouTube.

About CEVA, Inc.CEVA is the leading licensor of wireless connectivity and smart sensing technologies. We offer Digital Signal Processors, AI processors, wireless platforms and complementary software for sensor fusion, image enhancement, computer vision, voice input and artificial intelligence, all of which are key enabling technologies for a smarter, connected world. We partner with semiconductor companies and OEMs worldwide to create power-efficient, intelligent and connected devices for a range of end markets, including mobile, consumer, automotive, robotics, industrial and IoT. Our ultra-low-power IPs include comprehensive DSP-based platforms for 5G baseband processing in mobile and infrastructure, advanced imaging and computer vision for any camera-enabled device and audio/voice/speech and ultra-low power always-on/sensing applications for multiple IoT markets. For sensor fusion, our Hillcrest Labs sensor processing technologies provide a broad range of sensor fusion software and IMU solutions for AR/VR, robotics, remote controls, and IoT. For artificial intelligence, we offer a family of AI processors capable of handling the complete gamut of neural network workloads, on-device. For wireless IoT, we offer the industry's most widely adopted IPs for Bluetooth (low energy and dual mode), Wi-Fi 4/5/6 (802.11n/ac/ax) and NB-IoT. Visit us at http://www.ceva-dsp.comand follow us on Twitter, YouTube,Facebook, LinkedInand Instagram.

Logo: https://mma.prnewswire.com/media/74483/ceva__inc__logo.jpg

SOURCE CEVA, Inc.

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SiFive and CEVA Partner to Bring Machine Learning Processors to Mainstream Markets - PRNewswire

Rodney Brooks (previously) is a distinguished computer scientist and roboticist (he's served as as head of MIT's Computer Science and Artificial Intelligence Laboratory and CTO of Irobot); two years ago, he published a list of "dated predictions" intended to cool down some of the hype about self-driving cars, machine learning, and robotics, hype that he viewed as dangerously gaseous.

Every year, Brooks revisits those predictions to see how he's doing (to "self certify the seriousness of my predictions"). This year's scorecard is characteristically curmudgeonly, and shows that Brooks's skepticism was well-warranted, revealing much of the enthusiasm for about AI to have been mere froth: "I had not predicted any big milestones for AI and machine learning for the current period, and indeed there were none achieved... [W]e have seen warnings that all the over-hype of machine and deep learning may lead to a new AI winter when those tens of thousands of jolly conference attendees will no longer have grants and contracts to pay for travel to and attendance at their fiestas"

Some of the predictions are awfully fun, too, like "The press, and researchers, generally mature beyond the so-called 'Turing Test' and Asimov's three laws as valid measures of progress in AI and ML" (predicted for 2022; last year's update was, "I wish, I really wish.").

Brooks is pretty bullish on the web for piercing hype-bubbles, noting that it provides "outlets... for non-journalists, perhaps practitioners in a scientific field, to write position papers that get widely referenced in social media... During 2019 we saw many, many well informed such position papers/blogposts. We have seen explanations on how machine learning has limitations on when it makes sense to be used and that it may not be a universal silver bullet."

Bruce Sterling's actually pretty comfortable with tech hype: "Ive come to see tech-hype as a sign of social health. Its kinda like being young and smitten by a lot of random pretty people, only, youre not gonna really have relationships with most of them, and also, the one you oughta marry and have children with, that is probably not the one who seems most fantastically hot and sexy. Also, if nothing at all seems fantastically hot and sexy, then you probably have a vitamin deficiency. Its all part of the marvelous pageant of life, ladies and gentlemen."

I made my predictions because at the time I saw an immense amount of hype about these three topics, and the general press and public drawing conclusions about all sorts of things they feared (e.g., truck driving jobs about to disappear, all manual labor of humans about to disappear) or desired (e.g., safe roads about to come into existence, a safe haven for humans on Mars about to start developing) being imminent. My predictions, with dates attached to them, were meant to slow down those expectations, and inject some reality into what I saw as irrational exuberance.

Predictions Scorecard, 2020 January 01 [Rodney Brooks]

(via Beyond the Beyond)

(Image: Gartner; Cryteria, CC-BY, modified)

Machine learning systems are notorious for cheating, and there's a whole menagerie of ways that these systems achieve their notional goals while subverting their own purpose, with names like "model stealing, rewarding hacking and poisoning attacks."

The Boeing 737 Next Generation has a gnarly bug: on instrument approach to seven specific runways, the six cockpit display units used to guide the pilots to their landing go suddenly black and they remain black until the pilots choose a different runway to land on.

Every year, the AI Now Institute (previously) publishes a deep, thoughtful, important overview of where AI research is and the ethical gaps in AI's use, and makes a list of a dozen urgent recommendations for the industry, the research community, and regulators and governments.

Theres no shortage of turn-based strategy games on the market. But few of them have the scope of Sid Meiers Civilization, whose title says it all. Your goal is nothing less than the shepherding of an entire nation from its first village to global dominance and beyond. The Civ series is now on its []

Thousands of businesses use Cisco as the platform for their network, and theres a good reason for that. As data and security needs evolve, Cisco evolves to keep up with them but that means the administrators and IT professionals that implement it have to evolve, too. Thats not just a figure of speech. As []

Surfing the web, is one of those casual terms that actually says a lot about the way we use the internet. Surfing, after all, sounds fun, almost passive. Just let the wave of information carry you, it seems to say. But what if we actually need to stay put for a while? Sadly, the layout []

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AI, machine learning, and other frothy tech subjects remained overhyped in 2019 - Boing Boing

Constraint solvers take a set of hard and soft constraints in an organization and formulate the most effective plan, taking into account real-time problems.

When a business looks to implement an artificial intelligence strategy, even proper expertise can be too narrow. Its what has led many businesses to deploy machine learning or neural networks to solve problems that require other forms of AI, like constraint solvers.

Constraint solvers take a set of hard and soft constraints in an organization and formulate the most effective plan, taking into account real-time problems. It is the best solution for businesses that have timetabling, assignment or efficiency issues.

In a RedHat webinar, principal software engineer, Geoffrey De Smet, ran through three use cases for constraint solvers.

Vehicle Routing

Efficient delivery management is something Amazon has seemingly perfected, so much so its now an annoyance to have to wait 3-5 days for an item to be delivered. Using RedHats OptaPlanner, businesses can improve vehicle routing by 9 to 18 percent, by optimizing routes and ensuring drivers are able to deliver an optimal amount of goods.

To start, OptaPlanner takes in all the necessary constraints, like truck capacity and driver specialization. It also takes into account regional laws, like the amount of time a driver is legally allowed to drive per day and creates a route for all drivers in the organization.

SEE ALSO: Machine Learning Algorithms Help Couples Conceive

In a practical case, De Smet said RedHat saved a technical vehicle routing company over $100 million in savings per year with the constraint solver. Driving time was reduced by 25 percent and the business was able to reduce its headcount by 10,000.

The benefits [of OptaPlanner] are to reduce cost, improve customer satisfaction, employee well-being and save the planet, said De Smet. The nice thing about some of these are theyre complementary, for example reducing travel time also reduces fuel consumption.

Employee timetabling

Knowing who is covering what shift can be an infuriating task for managers, with all the requests for time off, illness and mandatory days off. In a place where 9 to 5 isnt regular, it can be even harder to keep track of it all.

RedHats OptaPlanner is able to take all of the hard constraints (two days off per week, no more than eight-hour shifts) and soft constraints (should have up to 10 hours rest between shifts) and can formulate a timetable that takes all that into account. When someone asks for a day off, OptaPlanner is able to reassign workers in real-time.

De Smet said this is useful for jobs that need to run 24/7, like hospitals, the police force, security firms, and international call centers. According to RedHats simulation, it should improve employee well-being by 19 to 85 percent, alongside improvements in retention and customer satisfaction.

Task assignment

Even within a single business department, there are skills only a few employees have. For instance, in a call center, only a few will be able to speak fluently in both English and French. To avoid customer annoyance, it is imperative for employees with the right skill-set to be assigned correctly.

With OptaPlanner, managers are able to add employee skills and have the AI assign employees correctly. Using the call center example again, a bilingual advisor may take all calls in French for one day when theres a high demand for it, but on others have a mix of French and English.

For customer support, the constraint solver would be able to assign a problem to the correct advisor, or to the next best thing, before the customer is connected, thus avoiding giving out the wrong advice or having to pass the customer on to another advisor.

In the webinar, De Smet said that while the constraint solver is a valuable asset for businesses looking to reduce costs, this shouldnt be their only aim.

Without having all stakeholders involved in the implementation, the AI could end up harming other areas of the business, like customer satisfaction or employee retention. This is a similar warning given from all analysts on AI implementation it needs to come from a genuine desire to improve the business to get the best outcome.

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Forget Machine Learning, Constraint Solvers are What the Enterprise Needs - - RTInsights

Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels | PCWorld ');consent.ads.queue.push(function(){ try { IDG.GPT.addDisplayedAd("gpt-superstitial", "true"); $('#gpt-superstitial').responsiveAd({screenSize:'971 1115', scriptTags: []}); IDG.GPT.log("Creating ad: gpt-superstitial [971 1115]"); }catch (exception) {console.log("Error with IDG.GPT: " + exception);} }); This business workhorse has a lot to like.

Dell Latitude 9510 hands-on: The three best features

Dell's Latitude 9510 has three features we especially love: The integrated 5G, the Dell Optimizer Utility that tunes the laptop to your preferences, and the thin bezels around the huge display.

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The Dell Latitude 9510 is a new breed of corporate laptop. Inspired in part by the companys powerful and much-loved Dell XPS 15, its the first model in an ultra-premium business line packed with the best of the best, tuned for business users.

Announced January 2 and unveiled Monday at CES in Las Vegas, the Latitude 9510 weighs just 3.2 pounds and promises up to 30 hours of battery life.PCWorld had a chance to delve into the guts of the Latitude 9510, learning more about whats in it and how it was built. Here are the coolest things we saw:

The Dell Latitude 9510 is shown disassembled, with (top, left to right) the magnesium bottom panel, the aluminum display lid, and the internals; and (bottom) the array of ports, speaker chambers, keyboard, and other small parts.

The thin bezels around the 15.6-inch screen (see top of story) are the biggest hint that the Latitude 9510 took inspiration from its cousin, the XPS 15. Despite the size of the screen, the Latitude 9510 is amazingly compact. And yet, Dell managed to squeeze in a camera above the displaythanks to a teeny, tiny sliver of a module.

A closer look at the motherboard of the Dell Latitude 9510 shows the 52Wh battery and the areas around the periphery where Dell put the 5G antennas.

The Latitude 9510 is one of the first laptops weve seen with integrated 5G networking. The challenge of 5G in laptops is integrating all the antennas you need within a metal chassis thats decidedly radio-unfriendly.

Dell made some careful choices, arraying the antennas around the edges of the laptop and inserting plastic pieces strategically to improve reception. Two of the antennas, for instance, are placed underneath the plastic speaker components and plastic speaker grille.

The Dell Latitude 9510 incorporated plastic speaker panels to allow reception for the 5G antennas underneath.

Not ready for 5G? No worries. Dell also offers the Latitude 9510 with Wi-Fi 6, the latest wireless networking standard.

You are constantly asking your PC to do things for you, usually the same things, over and over. Dells Optimizer software, which debuts on the Latitude 9510, analyzes your usage patterns and tries to save you time with routine tasks.

For instance, the Express SignIn feature logs you in faster. The ExpressResponse feature learns which applications you fire up first and loads them faster for you. Express Charge watches your battery usage and will adjust settings to save bettery, or step in with faster charging when you need some juice, pronto. Intelligent Audio will try to block out background noise so you can videoconference with less distraction.

The Dell Latitude 9510s advanced features and great looks should elevate corporate laptops in performance as well as style.It will come in clamshell and 2-in-1 versions, and is due to ship March 26. Pricing is not yet available.

Melissa Riofrio spent her formative journalistic years reviewing some of the biggest iron at PCWorld--desktops, laptops, storage, printers. As PCWorld's Executive Editor she leads PCWorlds content direction and covers productivity laptops and Chromebooks.

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Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels - PCWorld

Artificial intelligence and machine learning, with an eye toward task automation.

For Senior Data Scientist James Buban at iHerb, those are just a couple of the tech trends hell be watching in 2020.

As companies enter a new decade, its important for their leaders to anticipate how the latest tech trends will evolve in order to determine how they can benefit their businesses and their customers. CEO of 20spokes Ryan Fischer said his company uses machine learning data to provide a better user experience for our clientscustomers by leveraging data on individual user behavior.

We asked Buban, Fischer and other local tech execs which trends theyre watching this year and how theyll be utilizing them to enhance their businesses. From natural language processing to computer vision, these are the trends that will be shaping tech in 2020.

As a development agency, 20spokes specializes in helping startups plan, build and scale innovative products. CEO Ryan Fischer said he is looking to AI and machine learning to design better chatbots and wrangle large data sets.

What are the top tech trends you're watching in 2020? What impact do you think these trends will have on your industry in particular?

In 2020, we expect AI to play an even bigger role for our clients. When we talk about AI, we are really discussing machine learning and using data to train a model to use patterns and inference.

Working with machine learning continues to get easier with many large providers working on simpler implementations, and we expect the barrier to entry to continue to lower in 2020. We also have more user data which allows us to use machine learning to design more tailored and intelligent experiences for users.

We areusing machine learning to improve chatbots to create more dynamic dialogue.

How are you applying these trends in your work in the year ahead?

At 20spokes, we use machine learning to provide a better user experience for our clients' customers by leveraging data on individual user behavior to make more accurate recommendations and suggestions. We're continuing to look at how we can apply it to different sets of data, from providing better insights of reports for large data sets to sending us real-time updates based on trained patterns. We are also using machine learning to improve chatbots to create more dynamic dialogue.

In order to deliver trusted insights on consumer packaged goods, Label Insights Senior Data Scientist James Buban said they have to first process large amounts of data. Using machine learning and automation, data collection processes can be finished quickly and more accurately for customers.

What are the top tech trends you're watching in 2020?

The top tech trends that well be watching in 2020 are artificial intelligence and machine learning, with an eye toward task automation. In particular, we are interested in advancements in computer vision, such as object detection and recognition. We are also interested in natural language processing, such as entity tagging and text classification. In general, we believe that machine learning automation will play a big role in both the data collection industry and in e-commerce, particularly in the relatively new addition of the food industry in the retail space.

We plan to use computer vision and natural language processing toautomate tasksthroughout 2020.

How are you applying these trends in your work in the year ahead?

At Label Insight, we are building up a large database of attributes for consumables based on package information. To do so, we first need to collect all package data, which has traditionally been accomplished through a team of dedicated data entry clerks. Due to the huge volume of products that need to be added to our database, this data entry process is expensive, tedious and time-consuming.

Therefore, we plan to use computer vision and natural language processing to begin automating these tasks throughout 2020. We are also planning to use this technology to make our e-commerce solutions more scalable.

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Technology Trends to Keep an Eye on in 2020 - Built In Chicago