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Fictron Industrial Supplies Sdn Bhd
5-6, Jalan USJ 9/5Q,
Subang Business Centre,
47620 UEP Subang Jaya,
Selangor, Malaysia.
+603-8023 9829
+603-8023 7089
Selangor Office:
36, Jalan Puteri 5/12,
Bandar Puteri,
47100 Puchong,

Penang Office:
44A Jalan Besi,
11600 Green Lane,
Penang, Malaysia.

Latest News

Samsung Elec Launches Fail-Proof SSD That ˇ°Never Diesˇ±

Sep 20, 2019
Samsung Elec Launches Fail-Proof SSD That ˇ°Never Diesˇ±
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Samsung Electronics has unveiled its latest PCIe Gen4 solid state drive (SSD) series that are estimated to further widen the technology gap with its rivals thanks to three key software technologies integrated in the new system, making the drive fail-proof, faster and more flexible.
The South Korean chipmaker said Thursday it has circulated 19 versions of the industry’s highest-performing SSDs backed by the latest three software innovations.
With fail-in-place (FIP) technology, the latest SSD models “never die” regardless if a chip fails on the drive, the company explained. The FIP technology solves the problem of having to replace an entire SSD even if a failure occurs in just one of several hundred NAND chips. The new SSD can detect a faulty chip, scan for any damaged data, and transfer the original data onto normally operating chips, automatically activating an “error-handing algorithm” to maintain stable high performance, the company added. 
The FIP technology marks a “new milestone” in the 60-year history of storage, saving system downtime and drive replacement costs even when errors occur at the chip level, a Samsung Electronics official said.
One other core technology, virtualization technology, offers independent virtual workspaces to several users, permitting a single SSD to be subdivided into independent virtual workspaces for various users. V-NAND machine learning technology leverages big data analytics to precisely read and verify data at high speeds.
Samsung Electronics said it commenced last month the mass production of its next-generation PCIe Gen4 PM1733 and PM 1735 SSDs that run at twice the speed of existing SSDs, fully incorporating the three technologies. The company plans to cement its dominance in the premium storage market by introducing more innovative software to its SSDs for servers and data centers, it claimed.

Malaysia Open To Huawei For 5G Equipment, Authority Chief Says

Sep 20, 2019
Malaysia Open To Huawei For 5G Equipment, Authority Chief Says
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Malaysia has no objection to China's Huawei Technologies partaking in the country's 5G network rollout, as Western countries continue to make accusations that the company's equipment could facilitate data leaks to Beijing. Southeast Asia's third-largest economy is likely to announce spectrum allocations for 5G service providers in the second half of 2020, Al-Ishsal Ishak, chairman of the Malaysian Communications and Multimedia Commission told the Nikkei Asian Review.
Huawei is a longtime supplier of broadband routers, network controllers and data center switches to Malaysian telecommunications companies. Al-Ishsal stated a comprehensive report on an investigation into allegations that Huawei's 5G equipment could aid Chinese spies has been submitted to Malaysian Prime Minister Mahathir Mohamad by the National Cyber Security Agency, a bureau of the National Security Council.
There is 'no objection for Huawei to participate' in Malaysia's 5G rollout, Al-Ishsal highlighted, adding that Mahathir's recent visit to Huawei's research center near Beijing testifies to Malaysia's cordial approach toward the Chinese tech company. 'Cybersecurity is an issue that involves all service providers,' Al-Ishsal said. 'Thus Huawei is not special. We continue to monitor [the matter] because these things are dynamic and can [constantly] change. If we find any wrong behavior by any parties, not only Huawei, then we will call it out and advise the government and the National Cyber Security Agency.
The awarding of 700 MHz spectrum has been slowed since the first quarter of 2018, following the government's move to call for a public inquiry. Among the front-runners are Malaysia's top three phone service providers: Maxis, DiGi.Com and Celcom Axiata. Al-Ishsal said the 700 MHz allocation will empower telcos to roll out both 4G and 5G services, dependant upon the capability of the respective telco and needs on the ground.
Huawei, one of the few global 5G equipment providers, is mainly a smartphone maker but is growing both businesses in tandem. The alleged risks of Huawei's 5G equipment facilitating the eavesdropping on corporations and other concerns have forced Western countries to banish Huawei from providing the equipment. Australia, New Zealand, the U.K. and U.S. have prohibited Huawei from participating in their 5G networks.
5G is considered the next iteration of mobile connectivity, the successor to 4G Long-Term Evolution (LTE). The technology will enable for seriously faster smartphone data connectivity, enable autonomous vehicles and smart home networks, and improve rural fixed internet connections. While Huawei is blacklisted from bidding for government contracts, peculiarly 5G projects, in the United States, the Donald Trump administration has warned other advanced economies that Huawei's 5G equipment may contain 'back doors' that could be used for cyberespionage.
Late last year, parts of equipment supplied by Huawei were taken away from an emergency services' communications system developed in the United Kingdom. Germany was reported to think about stricter security standards to effectively block the Chinese company from its 5G rollout, and the Netherlands' largest telco has mentioned that Huawei will not be allowed to supply core 5G equipment, though the company is open to procuring 'less-sensitive products.'

Fujifilm SonoSite Wants to Bring AI to Ultrasound

Sep 20, 2019
Fujifilm SonoSite Wants to Bring AI to Ultrasound
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Have you ever needed an IV and had to undertake many pricks before the nurse could find a vein? Technology in order to avoid that painful trial and error is in the works. Fujifilm’s ultrasound diagnostics arm SonoSite announced that it had partnered with a startup company to advance artificial intelligence that can interpret ultrasound images on a mobile phone.
The companies say the first target for their AI-enabled ultrasound will be finding veins for IV (intravenous) needle insertion. The technology would enable technicians to hold a simple ultrasound wand over the skin while software on a connected mobile device locates the vein for them.
For this project, Fujifilm SonoSite tapped the Allen Institute for Artificial Intelligence (AI2), which has an incubator for AI startup companies. “Not only do we have to come up with a very accurate model to analyze the ultrasound videos, but on top of that, we have to make sure the model is working effectively on the limited resources of an android tablet or phone,” says Vu Ha, technical director of the AI2 Incubator.
In an interview with IEEE Spectrum, Ha did not share the name of the startup that will be taking on the task, saying the fledgling company is still in “stealth mode.”
Ha tells the AI2 startup will take on the project in two stages: First, it will train a model on ultrasound images without any resource restraints, with the purpose of making it as reliable as possible. Then, the startup will go through a collection of experiments to simplify the model by controlling the number of hidden layers in the network, and by trimming and compressing the network until it is not difficult to operate on a mobile phone. The trick will be to shrink the model without sacrificing too much accuracy, Ha says.
If successful, the device could help clinicians diminish the number of unsuccessful attempts at finding a vein, and enable less trained technicians to start IVs as well. Hospitals that do a big volume of IVs often have highly trained staff capable of eyeballing ultrasound videos and using those images to help them to find small blood vessels. But the number of these highly trained clinicians is very small, says Ha.
“My hope is that with this technology, a less trained person will be able to find veins more reliably” using ultrasound, he says. That could broaden the availability of portable ultrasound to rural and resource-poor areas. 
SonoSite and AI2 are homes to two of the numerous groups of researchers putting AI to work on medical imaging and diagnostics. The U.S. Food and Drug Administration (FDA) has certified for commercial use a deep learning algorithm to analyze MRI images of the heart, an AI system that looks for signs of diabetic retinopathy in the images of the retina, an algorithm that analyzes X-ray images for signs of wrist fracture, and software that looks for indicators of stroke in CT images of the brain, to name a few.  
More importantly, the FDA in 2017 also approved for commercial use smartphone-based ultrasound technology distributed by Butterfly. The device, which costs less than $2000, can be used to take sonograms for 13 various clinical applications, including blood vessels. Butterfly has announced publicly that it is developing deep learning–based AI that will assist clinicians with image interpretation. But the company has not yet commercially launched the technology. 
Not less than four other portable or mobile device–based ultrasound technologies have been recognized by the FDA, including that of Fujifilm SonoSite, and the Lumify from Philips. But the adoption of these devices has been relatively slow. As Eric Topol, director of the Scripps Research Translational Institute, told Spectrum recently, the smartphone ultrasound is a “brilliant engineering advance” that’s “hardly used at all” in the health care system. Complex challenges such as reimbursement, training, and the old habits of clinicians often hinder the uptake of new gadgets, despite engineers’ best efforts.

Annual Investments in Robots Rose to World Record $16.5 Billion

Sep 20, 2019
Annual Investments in Robots Rose to World Record $16.5 Billion
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Robot deliveries are forecasted to leap 39% from 2018 to 2022 from a record total annual sales level of $16.5 billion last year, as reported by the World Robotics report.
More than a third of global installations were in China and the top five countries hold 74% of the market. Japan, Korea, U.S and Germany round out this group. China’s investment in robots climbed to $5.4 billion last year.
“We saw a dynamic performance in 2018 with a new sales record, even as the main customers for robots – the automotive and electrical-electronics industry – had a difficult year,” says Junji Tsuda, President of the International Federation of Robotics. “The U.S.-China trade conflict imposes uncertainty to the global economy – customers tend to postpone investments.”
Around 420,000 robots were installed last year and that figure is believed to soar to 584,000 by 2022. In 2013, the number of robots in place was 178,000.
In terms of robot density, or number of robots per 10,000 manufacturing employees, Singapore and Korea hold a significant lead in highly automated industrial production.

Unlicensed Spectrum May Be Critical to 5G

Sep 19, 2019
Unlicensed Spectrum May Be Critical to 5G
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When the International Telecom Union (ITU) detailed the key objectives for 5G, the 3GPP faced the difficult task of expanding the capabilities of the present wireless network under the constraint of limited spectrum. Spectrum equates to bandwidth, and the industry needs even more spectrum to improve data rates and address specific use cases beyond 4G. However, there isn’t much unoccupied spectrum below 6 GHz. Due to this fact, the 3GPP introduced the concept of using mmWave frequencies as a mobile access vehicle early in the standardization process.
The link between 5G and mmWave
5G and mmWave have been connected since the beginning. mmWave spectrum offers a path to realize 5G data rates on the order of 10X or more, in comparison to today’s networks. It may come as no surprise, but mmWave for mobile access is fraught with challenges. A lot of people debate whether these challenges have been addressed cost effectively. And yet, early 5G mmWave deployments target two unique use cases: indoor “hot spots” and fixed wireless access (FWA). This basically means, not mobile access in the context of LTE. 
Although mmWave contains much promise, more work must be done to actualize its potential, and the 3GPP continues to investigate other frequency options to unleash more spectrum for 5G use. Along with mmWave, the 3GPP is investigating unlicensed spectrum with the NR-U study item. In LTE or 4G, the 3GPP defined a coexistence path for WiFi and LTE using the unlicensed bands, where an LTE subscriber could use the unlicensed 2.4 or 5 GHz bands to supplement data throughput. Known as LAA, or license assisted access, the path proves LTE and WiFi can coexist, although it’s unclear how many users take advantage of this technology today. 3GPP’s 5G NR-U proposals go way beyond the prior work on 4G, and there is additional motivation to take NR-U further.
Maybe not coincidentally, the FCC has issued a notice of proposed rulemaking to investigate the use of the 6 GHz band covering 5.925–7.125 GHz as unlicensed and a possible home for 5G use. Today, this spectrum is used by cable operators for distribution of services, radars, and dedicated microwave communication links. By designating this spectrum as unlicensed, 5G operators and others could take full advantage of it to create new networks or augment already deployed networks. The combination of 2.4, 5, and now 6 GHz potentially creates over 1 GHz of spectrum for 5G use. 
Nevertheless, unlicensed bands for 5G come with stipulations. Any 5G device using unlicensed spectrum must:
  • Comply with lower power emission requirements that limit signal propagation and in-band interference, constraining the coverage area
  • Share spectrum with incumbent users, adding technical complexity to 5G terminals so that all devices can coexist
  • Make use of Dynamic Frequency Selection (DFS) and Transmit Power Control (TPC) techniques to facilitate coexistence, like WiFi devices do today
  • Likely adopt the LTE or 4G coexistence techniques, such as Listen Before Talk (LBT), to work side-by-side with WiFi devices
In the event where this spectrum is used lightly, 5G NR-U deployments could be strategically placed, helping the creation of dedicated-use 5G NR networks offering benefits above and beyond current technologies to address the key performance objectives of the 3GPP—faster data rates, higher reliability, and low latency — to increase the 5G ecosystem further, albeit in localized regions and perhaps explicitly for special use cases.
At first sight, NR-U adds complexity, and the 3GPP members must weigh the benefits, costs, and potential drawbacks of this approach. Nevertheless, it provides a large amount of spectrum, and spectrum is key to realizing the potential of 5G. If the 3GPP is successful in moving forward with NR-U, there are abundant amounts of unlicensed mmWave spectrum that can also benefit from this work once mmWave technologies have matured. NR-U is just one of multiple study items for 3GPP Release 16 and beyond, but it may be critical to the 5G initiative—perhaps much more important than many of us initially thought. 
It is clear that 5G NR, with new bands, wider bandwidth, and new beamforming technology, presents important design and test challenges that require powerful tools to accelerate innovation.

No Programming, No Problem: Chinese TV Makers Join 8K Club

Sep 19, 2019
No Programming, No Problem: Chinese TV Makers Join 8K Club
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Chinese manufacturers will soon market their own 8K high-definition televisions, regardless of a dearth of content at this moment available for the next-generation technology. Haier will put its 8K TVs on the market in China by the end of this year, next in Europe and various other regions opening as soon as next year. Hisense will also roll out 8K models in Europe as early as next year, and TCL said it will release its own offering that same year.
The players are trying to gain a very early foothold in an ever growing market. The market for 8K TVs worldwide will increase to 8.26 million units in 2023, up from just 28,000 last year, according to the Japan Electronics and Information Technology Industries Association.
The Chinese TVs were featured at the IFA electronics expo held in Berlin through Wednesday. Haier launched its 75-inch product while Hisense showed off how the dual-layered LCD screens can produce rich contrasts and depth. The 8K TVs showcased by TCL utilize quantum dots, or nano-sized particles engineered to spark and emit certain colors. The quantum dots reproduce brilliant imagery in 65-inch, 75-inch and 85-inch models.
But, 8K TVs accounted for less than 1% of all flat-panel TVs worldwide, and the ratio will only inch up to 3% in 2023. On the other hand, the market for 4K TVs exceeded 90 million units last year, constituting just about 40% of all TVs. Limiting demand for 8K TVs is a lack of programming. In Japan, the public broadcaster NHK began airing 8K shows in 2018, but no TV network abroad has followed suit, according to NHK.
Sharp, the earliest company to release 8K TVs, is determined to help expand the market. The Osaka-based company debuted a small 8K video camera and a notebook computer that can edit footage. Sharp plans to sell the equipment to video production companies. 4K TVs started to take off after Netflix and other video distributors began to put out content in the high-definition standard. But Netflix says there are no plans at the moment to release 8K programming.
'We will immediately demonstrate that the technology to make [8K models] is available,' said a representative at Chinese TV maker Konka Group.

Hyundai Motor To Deploy Self-Developed Center Side Airbag In New Cars

Sep 19, 2019
Hyundai Motor To Deploy Self-Developed Center Side Airbag In New Cars
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Hyundai Motor Group announced on Wednesday new passenger car releases will be built with self-developed center side airbag to divide the space between driver and passenger and stop head injuries.
The center side airbag situated alongside the driver’s seat will spread in 0.03 second by the time the impact is sensed. Lab test results showed the center side airbag can lessen head injuries caused by passengers colliding with each other by about 80 percent, the Korean conglomerate said.
Hyundai Motor Group said it used newly patented technology to the airbag to be certain system stability, while achieving the industry’s smallest and lightest airbag. A new technology was created to streamline the design and reduce the weight of components by about 50 percent compared to competing products, it said.
The smaller size of the airbag means the group’s design team has way more flexibility in the type of seat design for future mobility products. Hyundai Motor Group will roll out the new center side airbag in impending automobiles.

ABB Builds $150m Robotics Plant In Shanghai

Sep 19, 2019
ABB Builds $150m Robotics Plant In Shanghai
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Swedish-Swiss engineering group ABB has initiated construction of a $150 million plant here to build automation equipment, eyeing the sector's growth opportunity in China and the rest of Asia. The 67,000-sq.-meter facility, when completed in 2021, will substitute a site that is one of the group's three global production factories. ABB outlined its new plant in Shanghai as the 'factory of the future where robots will make robots.'
Distinct from the convention linear system, production will be automated with robots moving flexibly from station to station for greater customization. It will feature cutting-edge technologies including digital twin, a feature that allows the analysis of data by connecting the physical and virtual worlds to ease production. The factory will also host a research and development center to boost innovation in artificial intelligence, ABB said.
The construction of the Shanghai plant happens shortly after a recent dip in ABB's order book for robotics in China, reflecting the group's optimistic view of the sector. 'Despite short-term market challenges, China's development as a global manufacturing hub, the ongoing trend toward mass customization and a rising shortage in skilled labor will continue to create strong and lasting demand for automation solutions in the region,' Sami Atiya, head of ABB's robotics business, said on Thursday. 
ABB's order book for robotics declined 14% per annum to $883 million in the second quarter, mostly due to weaker demand in China, the group said in a July statement. Its robotics solutions serve industries that include electronics, food and beverage, pharmaceuticals and automotive.
China is the world's biggest market, churning out 133,200 units of industrial robots in 2018, based upon the International Federation of Robotics in Germany, exceeding rival Japan's 52,400 units and America's 38,100 units. ABB projected that the global robotics market will grow from the current $80 billion annually to $130 billion in 2025.
'In the years ahead, we estimate the breadth and depth of our portfolio will nearly double,' Atiya said.
The group has spent over $2.4 billion in China since 1992, among them a $300 million manufacturing hub for electrification products in the southern city of Xiamen. The 425,000-sq.-meter site initiated operation in November. In China, ABB competes against the likes of Siemens, which opened its first AI lab outside of Germany in Beijing in May, as the German engineering group hastened the adoption of AI-related solutions. ABB also operates robotics factories in Sweden and the U.S.

Why IIoT Is A Priority For Manufacturers

Sep 18, 2019
Why IIoT Is A Priority For Manufacturers
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The Internet of Things — exclusively in an industrial capacity — is reaching a primary tipping point in terms of accessibility, capabilities and usefulness.
Industry-wide, use cases have been tested, IIoT platforms have shown their effectiveness and potential, and roadmaps to success have been developed in comparison to the mere visions imagined just a few years ago. Generally, this means that manufacturers who have not formerly invested in IIoT will be empowered to quickly catch early entrants and fast followers in IIoT, while still finding comparable value. For manufacturers who have not harnessed the power of IIoT yet, the environment will not be better than it is now.
Why IIoT Deployment Is So Vital
Year after year, manufacturers are faced with the unrelenting need to decrease operating costs through operational improvements. While many of the same operational cost-reduction strategies may continue to work, IIoT initiatives present new ways to obtain more benefits. 
As an example, having real-time insights into operations helps manufacturers to more quickly react to manufacturing chain complications and also allows for the mitigation of time- and cost-consuming shutdowns or other problems. On top of that, manufacturers can create new products and iterate on existing lines based on data collected from devices connected to the IIoT. 
Those deploying IIoT products or investing in IIoT platforms for the first time may possibly not have the expertise of those already leading the way, but they aren't too far behind. Manufacturers who purchase and deploy the latest IIoT products can still maintain parity with their early-adopter competitors by moving rapidly. But the window is closing. Manufacturers who do not invest in IIoT will significantly find it to be an uphill climb to catch up to those who have embraced the new technologies because of having to navigate a maturity curve.
Investments have been made and projects have been created and implemented in a variety of industrial and manufacturing capacities. Those who have already implemented IIoT into their business and those who do so over the next year will have an unique advantage due to the institutional knowledge flowing through the organizations. As that knowledge gap grows, it will be even more difficult for other manufacturers to catch up with the leaders, early-adopters, and those who are just starting to embrace IIoT.
How To Invest In IIoT
Those who are taking their first foray into IIoT need to have the right strategy when putting money into new products and platforms. The very first investments should be made with IIoT device connectivity and basic real-time analytics. This makes manufacturers to not merely deploy new technologies but to have a complete understanding of what is needed to generally bring devices onto the platform and then track what is happening with them once they are on the platform.
The first investment in IIoT in connectivity and analytics is critical not simply for getting a platform deployed but likewise for closing the knowledge gap. Time and time again, manufacturers have invested in platforms without also emphasizing connectivity and analytics. Sadly, those businesses have had to spend one to two years creating simple analytics and trying to find ways to securely connect devices. This is why there are still opportunities for those deploying IIoT for the first time catch up. Selecting the correct, all-encompassing platform for their requirements can close the knowledge gap and the technology gap.
Manufacturers Must Implement Smart Strategies With Their Smart Technologies
Manufacturers must be intelligent not only how they invest in IIoT, but also how they deploy the solutions they want to use to optimize their business. Many organizations who deploy new technologies, products, or software do so in a fragmented, improvised manner and have to fix costly errors down the line. The best advice for manufacturers who decide to deploy IIoT is to begin with small while thinking big. Simply put, understand your starting point but always have an end target to serve as a beacon.
Original projects should be co-used across teams and operational processes and carefully scoped out in order to deliver the highest impact and value over the shortest amount of time possible. Additionally it is important that manufacturers deploying IIoT for the first time are aware that they should be ready to take action immediately if it is not working with their business as predicted. If tangible value takes more than three months to obtain then either the project is too large in scope or the IIoT platform has a major deficiency. Adjust accordingly.
As for instance, if an industrial equipment manufacturer wants to offer product contracts based on outcomes rather than the just selling the product, this will require an enormous amount of capabilities including predictive analytics, machine learning, and automating a sizeable number of processes. As opposed to focus on this implementing the IIoT solutions as THE objective, use it as a target with an initial objective of connecting existing equipment and providing condition-based alerts to drive service actions. Creating and launching the least possible viable product for the latter should take between four to six weeks which gives a long lead time to observe whether or not value has been obtained inside the three-month threshold.
To conclude, manufacturers should be smart and assess their IIoT deployment. Doing this the right way can have an incredible impact on the bottom line of the business immediately and distance manufacturers from competitors for many years.

Using Slack on the Shop Floor

Sep 18, 2019
Using Slack on the Shop Floor
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Since its origin in 2013, Slack’s collaboration platform has turned business communication, with more than 10 million daily active users. Adoption has been confined basically to office workers, but Slack can also help manufacturers expedite problem resolution on the shop floor, support continuous improvement activities, and even improve customer relationships.
That has been the experience for us at MBX Systems, a build-to-order server hardware manufacturer and integrator. Slack has helped us reduce production bottlenecks and secure valid delivery of finished product in its extreme high-variability environment. All MBX personnel including plant employees were onboarded at the Slack rollout three years ago to enhance cross-company collaboration about operations, including production and supply chain issues. MBX has since added customers to Slack conversations, enabling near-real-time discussion among line workers, supervisors, platform engineers, account managers, customers and other key players.
In addition, MBX has rolled out a Slack app that allows customers to integrate hardware supply chain-related discussions into their company’s own Slack instance. Customers can now get real-time notifications of MBX engineering changes, work in progress and order updates in their Slack data stream for fast information access.
Carl Nothnagel, MBX vice president of operations, says that Slack has made it very easy to communicate both internally and with customers, “whether we're asking a question about a component, troubleshooting an assembly or software imaging issue, or discussing an unexpected production delay.” All stakeholders can see all the messages, keeping everyone in the loop. It also allows one person to pick up where another left off, and makes it easier to track a conversation as three or four different people respond throughout the day.
Communicating by Channel
Most Slack communication takes place in persistent chat rooms called channels that are created by each organization based on their unique needs and searchable for quick knowledge-sharing. Each channel functions as a workspace for team members involved in that particular project or area of business. 
On top of establishing conventional Slack discussion channels to expedite business communication on issues such as company announcements and project collaboration for front and back office personnel, MBX has established manufacturing-related channels dedicated to each work center, specific customers, new product introductions, production issues, corrective actions and other matters relevant to plant operation and optimization.
With this plan, questions about build instructions, component changes, supply chain issues, production problems, line interruptions, and other manufacturing concerns are routed through the relevant channel for smoother cross-functional communication.
System builders can promptly consult with platform engineers, manufacturing supervisors or sales personnel to resolve issues without leaving the plant floor. Managers can organize team improvement events without the back-and-forth of group email. Builders can communicate with the product documentation technician to update a discrepancy in the build instructions. Abilities like these save time and expand agility.
Bringing Customers on Board
Adding customers to relevant Slack channels has yielded additional benefits for system troubleshooting, delivery, and operational transparency that helps increase customer trust and satisfaction. One example is a customer that supplies cutting-edge simulation solutions for military aviation training. Custom hardware systems ranging from single rackmount servers to wall-sized racks with up to 36 integrated servers are manufactured and loaded with site-specific aerial images based on the end purchaser’s aircraft and geographic training needs.
On one recent full-rack build, technicians found a possible cooling issue on the manufacturing line. The manufacturing team had to have a timely resolution that would require collaboration between engineering and the customer’s team.  They reached out to the customer through an open Slack channel they’d set up for them and could actually collaborate exclusively with the customer to solve the problem quickly.
Accelerating Information Access
The app that MBX developed for Slack presents even more efficiencies to customer interactions with the manufacturer. These stem from the app’s integration with MBX Hatch, the company’s manufacturing orchestration software toolset.
Now in its first iteration, the integration makes engineering change notices and additionally shipment updates documented in Hatch to be automatically pushed to the customer’s Slack channel. This eliminates the need to check email or open the Hatch toolset. Other Hatch-based integrations that will bring manufacturing program insights into the Slack environment are on the MBX roadmap. We have only scratched the surface of capabilities and already realized major benefits for ourselves and our customers.

Universal Robots Introduces Its Strongest Robotic Arm Yet

Sep 18, 2019
Universal Robots Introduces Its Strongest Robotic Arm Yet
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Universal Robots, undoubtedly the dominant force in collaborative robots, is warming up its muscles in an effort to further expand its reach in the cobots market. The Danish company is introducing today the UR16e, its strongest robotic arm yet, with a payload capability of 16 kilograms (35.3 lbs), reach of 900 millimeters, and repeatability of +/- 0.05 mm.
Universal says the new “heavy duty payload cobot” will enable customers to automate a broader range of processes, such as packaging and palletizing, nut and screw driving, and high-payload and CNC machine tending.
At the beginning of 2015, Universal introduced the UR3, its smallest robot, which joined the UR5 and the flagship UR10, offering a payload capability of 3, 5, and 10 kg, respectively. Now the company is going in the other direction, announcing a bigger, stronger arm. “With Universal joining its competitors in extending the reach and payload capacity of its cobots, a new standard of capability is forming,” Rian Whitton, a senior analyst at ABI Research, in London, tweeted.
Like its predecessors, the UR16e is part of Universal’s e-Series platform, which features 6 degrees of freedom and force/torque sensing on the tool flange. The UR family of cobots have stood out from the competition by being versatile in a variety of applications and, most important, easy to deploy and program. Universal did not release UR16e’s price, saying only that it is about 10 percent higher than that of the UR10e, which is about $50,000, depending on the configuration.
Jürgen von Hollen, president of Universal Robots, says the company determined to launch the UR16e after studying the market and talking to customers about their needs. “What came out of that process is we understood payload was a true barrier for a lot of customers,” he tells IEEE Spectrum. The 16 kg payload will be mainly useful for applications that require mounting specialized tools on the arm to perform tasks like screw driving and machine tending, he explains. Customers that could benefit from such applications include manufacturing, material handling, and automotive companies.
“We’ve added the payload, and that will open up that market for us,” von Hollen says.
The difference between Universal and Rethink
Universal has grown by leaps and bounds since its starting in 2008. By 2015, it had sold more than 5,000 robots; that number was close to 40,000 as of last year. During the same period, revenue more than doubled from about $100 million to $234 million. Some time later when a string of robot makers have shuttered, including most notably Rethink Robotics, a cobots pioneer and Universal’s biggest rival, Universal finds itself in an respectable position, having amassed a commanding market share, estimated at between 50 to 60 percent.
About Rethink, von Hollen says the Boston-based company was a “good competitor,” helping disseminate the benefits and possibilities of cobots. “When Rethink basically ended it was more of a negative than a positive, from my perspective,” he says. In his view, a large difference between the two companies is that Rethink focused on delivering full-fledged applications to customers, whereas Universal focused on delivering a product to the market and letting the system integrators and sales partners deploy the robots to the customer base.
“We’ve always been very focused on delivering the product, whereas I think Rethink was much more focused on applications, very early on, and they added a level of complexity to their company that made it become very de-focused,” he says.
The collaborative robots market: massive growth
But still, despite its success, Universal is still tiny when you compare it to the giants of industrial automation, which include companies like ABB, Fanuc, Yaskawa, and Kuka, with revenue in the billions of dollars. Although some of these companies have added cobots to their product portfolios—ABB’s YuMi, for example—that market represents a drop in the bucket when you consider global robot sales: The size of the cobots market was estimated at $700 million in 2018, whereas the global market for industrial robot systems (including software, peripherals, and system engineering) is close to $50 billion.
Von Hollen notes that cobots are expected to go through an impressive growth curve — nearly 50 percent year after year until 2025, when sales will reach between $9 to $12 billion. If Universal can maintain its dominance and capture a big slice of that market, it will add up to a nice sum. To get there, Universal is not alone: It is backed by U.S. electronics testing equipment maker Teradyne, which acquired Universal in 2015 for $285 million.
“The amount of resources we invest year over year matches the growth we had on sales,” von Hollen says. Universal currently has more than 650 employees, most based at its headquarters in Odense, Denmark, and the rest scattered in 27 offices in 18 countries. “No other company [in the cobots segment] is so focused on one product.”

Silicon Takes Center Stage At The AI Hardware Summit

Sep 18, 2019
Silicon Takes Center Stage At The AI Hardware Summit
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With the appeal of AI flourishing in the last few years, so too have the volume of conferences either dedicated to machine learning and artificial intelligence or at least discussing the topics as a major portion of the agenda. Nevertheless, the inaugural AI Hardware Summit stood out from the crowd last year. At the conference, not just did attendees hear from the major technology players, some start-ups like Habana emerged from stealth mode with innovative solutions. The second annual AI Hardware Summit presented by Kisaco Research assures more of the same.
This year’s conference promises to deliver numerous types of solutions for AI, not simply at the chip level but also some of the supporting products and services for AI solutions. Additionally, there will be presentations on training and inference, the edge and the cloud, and driving applications, such as autonomous vehicles. The conference will also offer a diverse set of solutions demonstrating how AI is pushing the boundaries of technology with everything from innovative digital architectures to analog, photonics, and neuromorphic computing.
One of many companies scheduled to present is Cerebras, which just came out of stealth mode a few weeks ago at Hot Chips with the world’s largest processor. Other relative newcomers include GrAI Matter Labs, Rain Neuromorphics, and SambaNova Systems. More recognizable start-ups include Flex Logix with scalable FPGA solutions, Mythic with its unique analog computing architecture, Graphcore with its Intelligent Processing Unit (IPU) with massive amounts of on-die memory, and Habana with its Goya inference and Gaudi training processors.
Not to be surpassed by the start-ups, the more established technology leaders Alibaba, Cadence, IBM, Intel, Google, Facebook, Nvidia, Microsoft, Qualcomm, Mentor (now a division of Siemens), Synopsys, and Uber will also be touting their technology. Other manufacturers include those enabling the AI platforms, such as compiler and OS provider Applied Brain Research (ABR), power delivery supplier Vicor, ASIC chip designer and manufacturer eSilicon, and memory and storage providers Crossbar, Pure Storage, and Rambus. And companies developing solutions around AI like Optum for medical services and Medallia for customer experience.
In all, the AI hardware Summit promises to be a thrilling conference. As part of Tirias Research’s coverage of AI, will be attending the conference and delivering more details on some of the announcements that will be shaping AI technology in the near future.

The Used of Computer Vision in Business

Sep 17, 2019
The Used of Computer Vision in Business
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It turns out that something most humans take for granted — the ability to see, process and then act on visual input — is immensely hard to replicate in machines. That is specifically what computer vision (CV) aims to do. While perhaps not as advanced as human sight, computer vision has advanced to the point it is very useful in business today. Here’s more about what computer vision is and how it is used in business.
What is computer vision?
Computer vision describes the process when a computer using artificial intelligence algorithms can recognize and process images (photos, videos, etc.) and then create an appropriate output from the analysis because the computer can basically 'understand' the content. Especially, computer vision can classify, identify, verify, and detect objects. The developments with computer vision in recent years were facilitated by machine learning technology—in particular, the iterative learning process of neural networks—and significant leaps in computing power, data storage, and high-quality yet inexpensive input devices.
There are three main components to computer vision.
1.   Acquire the image: When a digital camera captures an image, it creates a digital file composed of zeros and ones.
2.   Process it: Algorithms are used to determine basic geometric elements to build images out of the binary data.
3.   Analyze and understand: In this final step of computer vision, the data is analyzed. High-level algorithms are used to then make decisions based on the images.
Since the very youngest of humans can process images and understand them, the challenges of replicating that ability in machines were underestimated. What first seemed like a simple problem to solve, turned-out to require decades of research. There is so much complexity in the visual world, and there is still many unknown about how human vision works and how the brain perceives visual input.
Although the field of computer vision has overcome many challenges so far, there are still hurdles to overcome based on what the computer vision is being used for and the data it’s able to acquire. Computer vision requires loads of data processing power and memory, plus its results can be impacted by the quality of the images/data. Computer scientists are still working on optimizing computer vision for all applications.
How is computer vision used in business?
There are unlimited applications where the ability to extract meaning from “seeing” visual data is useful. Computer vision combines with other technologies such as augmented and virtual realities to enable additional capabilities.
Facial recognition, powered by computer vision, is used for surveillance and security systems as well as the technology behind Facebook that identifies people to 'tag' in photos. China uses facial recognition technology in police work, payment portals, and more. Even retail stores use the technology to monitor inventory, track customers through the store, and allow customers to bypass the cash register by paying virtually when facial recognition technology puts the items on their bill.
Numerous car manufacturers from Ford to Tesla are scrambling to get their version of the autonomous vehicle into mass production. Computer vision is an essential technology that makes autonomous vehicles possible. The systems on autonomous vehicles constantly process visual data from road signs to seeing vehicles and pedestrians on the road and then determine what action to take.
Computer vision in medicine helps in diagnosing disease and other ailments and extends the sight of surgeons during operations. There are now smartphone apps that allow you to diagnose skin condition using the phone's camera. In fact, 90 percent of all medial data is image-based—X-rays, scans, etc. and a lot of this data can now be analyzed using algorithms.
Digital marketing: By using computers to sort and analyze through millions of online images, marketers can bypass traditional demographic research and still target marketing to the right online audience and do this work dramatically quicker than humans could. Marketers even use computer vision to ensure ads are not placed near content that is contradictory or problematic for its audience. Financial institutions use computer vision to prevent fraud, allow mobile deposits, and display numerical information visually.
In manufacturing, computer vision makes things more effective, effective, and safe. It is used in predictive maintenance to decide an issue before any breakdowns occur as well as in quality control measures. The quantity of items a machine can verify outpaces human's ability to do the same substantially.
The agriculture industry uses computer vision to make operations more productive by monitoring fields looking for signs of disease or pests so swift action can be taken to eliminate it. John Deere introduced a semi-autonomous combine harvester that can find the optimal route through crops after analyzing the quality of grains that are harvested. Handwriting extraction and analysis: Computer vision can translate handwritten meeting notes or creative brainstorming into digital formats which make it easier to share with others in the company. The applications of computer vision are so varied that it is hard to imagine a business that couldn't benefit from it.

The Ultimate Lean Workforce

Sep 17, 2019
The Ultimate Lean Workforce
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Industry 4.0 and the digital age were induced by an avalanche of technological advances, such as for example the Internet of Things (IoT), the Cloud, and Artificial Intelligence (AI). These technological advances are welcoming in the emergence of a new type of workforce that leverages the availability of new tools, devices and gadgets. 
While it is part of human nature to always look for, and benefit from, whichever new tools are created in order to make life and work easier, it's not merely technological advances that have brought about this new era. There are other elements that also contribute to the emergence of a new type of workforce, for example the sharing economy, that promote the culture and ethos of teamwork, collaboration and synergy.
Collectively, all of these points demand a workforce that excels and thrives with the advent of new technologies. It can be viewed as the “ultimate lean workforce” or, as I like to call it, the “Autonomous Workforce.” Members of the Autonomous Workforce are masters of the new technologies and the backbone of Smart Manufacturing, Smart Factories and Smart Cities.
The implications of such a workforce are many — from management delayering to new attitudes toward continual improvement and learning. Although the idea of a lean workforce has been in existence since the 1980s, the definition of the lean organization has changed considerably due to new technologies, the speed of work, communications and cultural changes.
Present and future generations of employees will demand decentralized, team-based organizational structures, compared with traditional pyramid structures. This newer thinking envisions people working together as self-directed work teams to make a better world in contrast to following a boss just to increase company profit. Peer-to-peer teaming relationships will be the ultimate method to get a lean system to be as quick and agile as possible.
This dynamic dynamic doesn't mean bosses or supervisors will not be needed. On the other hand, their duties will change into those of planners, coaches, facilitators, problem solvers, trainers, etc. Management will still need to provide strategic plans, reports, etc., on the status of the organization.
The truth is, autonomous workers have been around for a long period. Smaller companies have always had little choice but to allow their workers the autonomy to make their own decisions due to lack of resources. What is different is that this thinking  is now taking hold at larger enterprises. Those companies are asking “why” and “how” they can advance their autonomous workforce.
Why an Autonomous Workforce?
As the lean value stream quickens, information is now available immediately and processes are more consistent, controlled and standardized. Under these circumstances, a traditional organizational structure is not anymore cost competitive and is too slow. Even with technology, communications up and down the management chain are too cumbersome. Plus, why pay for supervision? Supervision is a non-value-added expense. We no longer can afford people whose only mission is to direct others.
Under these new conditions, employees should be trained to operate as self-directed work teams. Teams are small groups (5 to 12 employees) that work together inside of a product family, along a supply chain, within and outside the organization. Processes and/or product information that requires action needs to be clear, available and visible. If you find that teams are not able to make decisions on their own, this is an opportunity for improvement that should be addressed.
How to Develop an Autonomous Workforce
Switching from a traditional organizational model to an autonomous one involves a formal understanding of how to get there, as well as support and commitment from senior management backed by a shared strategic plan.
As an organization matures on its lean journey, it will get to the point that an autonomous workforce seems sensible. It’s the ultimate lean organization. During this transformation, an autonomous implementation team needs to look at the total supply chain and determine what parts are ready to evolve into an autonomous work team. Start with pilot areas. Learn, adjust, and roll out to the suppliers or customers of that first team.
To make it work, team members will have to take on additional responsibilities earlier shouldered by supervisors. They will volunteer and rotate to take on scheduling, reporting, safety, quality, leadership, discipline, continuous improvement, etc. This will require training in both business and human understanding that will result in a very knowledgeable and supportive workforce.
Essential to the success of an autonomous workforce initiative is employees knowing strategic objectives, having a clear and attractive reward (and discipline) system, belong to an appropriate team, and being accountable to company and supply chain results. A culture of continuous training/improvement that focuses on using visual systems and developing and maintaining real trust is also a must. The Autonomous Workforce is mostly about the need to be globally competitive. It hastens the delivery of value, reduces costs, improves quality, and results in happier workers and ultimately customers.

Network Upgrade Insights

Sep 17, 2019
Network Upgrade Insights
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Like many technologies in the industrial automation sphere, networking technologies are generally installed for the long haul. In essence, if it ain’t broke, don’t fix it.
But with the advance of Industry 4.0 and Industrial Internet of Things initiatives bringing IT and OT (operational technologies) closer together, a move toward more regular upgrades of industrial network technologies looks to be underway - at least in some verticals.
According to Schaffer, the frequency of network upgrades across industries does, of course, tend to deviate - due to “the nature of the work in a particular vertical and the amount of capex involved. On the low end, it tends to be every five years, but typically ranges from 10-20 years. In the IT space, where I’ve spent much of my career, the standard is to upgrade every 3-5 years to keep up with technology changes.”
Speaking about upgrade practices in particular industry verticals, Schaffer said the water/wastewater and electric power industries tend to have longest intervals between upgrades cycles. In these industries, going “20 years between upgrades is not uncommon due to specialized network design” and the relevant attitude around NTAR, i.e., never touch a running system.
On the contrary, the oil and gas industry refreshes a lot more regularly, particularly over the past a few years with the upsurge in this industry’s profits. “They’re also embracing a much more data centric model of operation,” said Schaffer. “To get access to that data, they need to upgrade more frequently. They’ve also seen crippling effects of cybersecurity attacks - like the one impacting Saudi Aramco (in 2012), which is making them much more proactive. On the discrete side of industry, automotive is leading the charge because they’ve been actively embedding IT into their OT ranks. So, they have more of that three- to five-year upgrade mentality.”
Beyond the technological benefits, Schaffer said one of the biggest business advantages of a network upgrade is that it provides the perfect excuse to update, validate, and clean up documentation. “Too many times I’ve been in plants asking about what devices are connected to the network and what they’re connected to on the network only to find that the documentation is out of date. No one knows the answer—so it’s difficult to manage the network from an operational and cybersecurity vantage point. I’m a big believer in knowing your network. Whenever you do an update, it gives you the perfect opportunity to re-acquaint yourself with the infrastructure that makes your plant tick.”
The biggest impacts to be obtained from a network upgrade will take place on the higher end, where IT and OT meet, said Schaffer. “The closer you are to the high end of network - where data is going to edge or cloud - that’s where you see a change in the mindset in the past couple of years. If you want to take advantage of these new capabilities, you need to upgrade regularly here.”
Schaffer also suggests taking security into account as part of your network upgrade. He suggests three best practices here:
  • Follow the principle of least privilege (or least authority). A device should only be allowed to communicate with what it needs to communicate with. Give it the connections and access rights it needs and nothing more.
  • Proactive defense in depth.Layer your defenses with different and various techniques and technologies. Having just one firewall with no defenses behind it is not ideal.
  • Know your network. Logging, auditing, monitoring, performing baselines, and understanding what your network should look like normally is a huge benefit when something goes wrong. For example, if your network normally sees 7 mbps traffic levels and you see it spike to 27 mpbs, you can focus on the devices generating the extra traffic.
When it's about answering the reader question about how often industrial networks should be upgraded, Schaffer noted that, “while mileage may vary, I suggest patching once per year at least, with once per quarter being best, and doing a full technology refresh every 5-7 years.”

Key Industrial IoT Terms Every Manufacturer Should Know

Sep 17, 2019
Key Industrial IoT Terms Every Manufacturer Should Know
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As a manufacturer, you don’t necessarily need to be an expert on the technology behind IIoT. You are most concerned with precisely how the technology will probably enable you to deliver quality products on time, keep costs contained, and improve productivity. It helps to be as informed as possible so you know enough to be an informed consumer of this technology.
With that in mind, here are key Industrial Internet of Things (IIoT) terms that will be helpful for you:
Data Terms
The majority of Industrial IoT terminology revolves around data - mainly, the massive amounts of data that it generates:
  • Big data. A very large data set that can be analyzed for patterns and trends.
  • Streaming data. Data that is continuously generated by different sources.
  • Sensor data. The output of a device that detects and responds to some type of input from the physical environment. The output may be used to provide information or input to another system, or to guide a process.
  • Time-series data. Data that collectively represents how a system, process, or behavior changes over time.
Business Process Terms
You’ll also hear significant amounts of terminology that defines special business processes:
  • Predictive maintenance. Techniques that are designed to help determine the condition of in-service equipment to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.
  • Operational intelligence. A category of real-time, dynamic business analytics that delivers visibility and insight into data, streaming events, and business operations.
  • Overall equipment effectiveness (OEE). A measure of how well a manufacturing operation is utilized (facilities, time, and material) compared to its full potential, during the periods when it is scheduled to run.
  • Asset monitoring. The process of monitoring all activity associated with a particular machine. Including but not limited to production, performance, quality, health, etc.
Technical Terms
This is where the terms get somewhat technical so it’s good to educate yourself on these:
  • Application Programming Interface (API). A set of functions or procedures that allow one application to access / interact with the features or data of another application or service
  • Programmable Logic Controller (PLC). An industrial digital computer that continuously monitors the state of input devices to make decisions (to control manufacturing processes and equipment) based on pre-programmed logic. 
  • Radio Frequency Identification (RFID). A wireless communication technology that uses radio frequency to power passive tags (small circuit antenna) to uniquely identify people or objects.
  • Supervisory Control and Data Acquisition (SCADA). A control system architecture that uses computers and networked data communications to monitor and control factory floor equipment.
Security and Standards Terms
When your data is being stored in the cloud, security comes to be so very important. That’s why it is good to familiarize yourself with these terms:
  • Identity and Access Management (IAM). A framework of business processes, policies, and technologies that manage digital identities (for e.g. used for authentication and access management)
  • Message Queuing Telemetry Transport (MQTT). A messaging protocol that works on top of TCP/IP. Designed for use cases with a low code footprint or limited network bandwidth.
  • Transmission Control Protocol/Internet Protocol (TCP/IP). The language used to access the Internet.
  • Ethernet IP. One of the manufacturing communication protocols used for transmitting information between electronic devices. Ethernet IP was originally developed by Rockwell Automation.
  • Hyper Text Transfer Protocol (HTTP). The underlying protocol used by the World Wide Web. HTTP defines how messages are formatted and transmitted, and what actions Web servers and browsers should take in response to various commands.
With an understanding of these terms, you are well prepared to keep researching and determining your options.

Why Every Business Owner Should Adopt An AI Approach

Sep 13, 2019
Why Every Business Owner Should Adopt An AI Approach
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Artificial intelligence is the most transformative business trend in the world today. Hold on, you say — AI is not a new idea. In the 1940s the great mathematician and code breaker Alan Turing predicted that digital computers in the future would be capable of logical reasoning. Commercial interest in AI began in the 1960s and waxed and waned over the next several decades.
Why is AI a big deal now? Partly, it’s because of the rapid advances in computer and communications hardware, says Pat Gelsinger, CEO of VMware, a provider of cloud computing solutions based in Palo Alto, California. Gelsinger knows hardware. In the 2000s he was the chief technology officer at Intel.
“The faster pace of change in AI today is because you now have data at scale and computing at scale,” he says. Data at scale, he says, comes from the 30 billion or so computerized sensors in the world that are constantly gathering information. Computing at scale comes from cheap rentable supercomputers offered by Amazon, Microsoft, Google, Alibaba and others.
These, along side the coming 5G wireless speeds, are superpowers, says Gelsinger. If your company doesn’t tap into these powers, your competitor will. CEOs and boards, take note. Stop relegating your company’s IT challenges to a 30-minute discussion within the audit committee. The superpowers that drive AI will accelerate business evolution.
Envision two rival companies: A and B. Company A has invested in AI across its organization, at times a painstaking process, and is getting 10% smarter per year. That is 10% smarter about its customers, its opportunities, its costs and its risks. Company B, which is old school and run by penny pinchers, thinks technology is a commodity and not a strategic weapon. Company B’s cheap approach saves money, but at a steep cost. Company B is basically getting 2% smarter per year. Which company do you want to be?
This begs the question: Which countries are ahead in AI? The question often comes up these days at highest levels of governments world wide. I asked Silicon Valley venture capitalist Jim Breyer, who is an investment advisor in IDG’s China funds, which has about 200 investments in China, including some of the country’s most interesting AI companies, for his opinion.
“The innovation in China is extraordinary,” Breyer says. “There really is a space-like race going on in AI between the U.S. and China. Both have deep capabilities. There are areas in China where some of the facial-recognition AI companies are the most advanced in the world. There are other technologies in the U.S., such as IoT and machine learning, where AI is more advanced. But it is a race.” In coming columns I will write about the Silicon Valley-Asia connection, and why — whether you are a CEO, entrepreneur or investor — you’ll fall behind quickly if you don’t tap into this amazing pipeline of innovation.

US-Japan Trade Deal Allows for Antitrust Actions on Big Tech

Sep 13, 2019
US-Japan Trade Deal Allows for Antitrust Actions on Big Tech
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Japan and the U.S. will agree not to require technology companies to divulge software secrets under their bilateral trade deal, except in cases of possible antitrust law violations, Nikkei learned Thursday. The in-principle ban on forced disclosures, part of the section of the draft agreement pertaining to digital trade, shows an endeavor to hit a balance between high-tech competition and government's role in intervening to stop data from becoming concentrated in the hands of a few companies.
Japanese Prime Minister Shinzo Abe and U.S. President Donald Trump are poised to sign the trade agreement on the sidelines of the United Nations General Assembly meeting in New York later this month. Details on farm and factory good tariffs are still being hammered out following a basic agreement reached by the two leaders on Aug. 25.
The rules on data, an important determiner of competitiveness for tech companies, are among the most closely watched parts of the trade deal's digital provisions. Japan's competition regulator recently published new enforcement guidelines meant to circumvent abuses of consumer data by platform companies - a category that includes U.S. tech giants Google, and Facebook.
The risk of government seizures of software source codes, proprietary algorithms and other tech secrets poses a barrier to business expansion. Japan and the U.S. have pushed for international rules on this front at the World Trade Organization and other forums, harboring particular concerns over China. But Tokyo and Washington will leave room in their trade deal for exceptions to the ban on forced disclosures, according to the draft document. Companies could be required to hand over data in cases in which consumer safety is at risk or in possible violation of competition or privacy laws.

Samsung Unveils Its First 5G-Integrated Mobile Processor Exynos 980

Sep 13, 2019
Samsung Unveils Its First 5G-Integrated Mobile Processor Exynos 980
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Samsung Electronics Co. Wednesday introduced its mass production of 5G-intergrated mobile processor Exynos 980 powering 5G modem and intelligent processing performance in a single chip. The Exynos 980 is Samsung Electronics’ first artificial intelligence (AI) mobile processor with an integrated 5G modem. Rather than being coupled with a separate 5G modem, the new mobile processor not only helps lessen power consumption but also enhances the space efficiency within a device.
The new mobile processor’s powerful modem supports 5G to 2G networks, offering a fast gigabit downlink speed in 4G LTE and up to 2.55-gigabits per seconds in sub-6-gigahertz 5G. The modem also supports E-UTRA-NR Dual Connectivity (EN-DC), which combines 2CC LTE and 5G connectivity to improve mobile downlink speed of up to 3.55Gbps, the company said.
The neural processing unit (NPU) features elevated performances of up to 2.7 times compared to its predecessor and is built into the Exynos 980 to provide new levels of on-device intelligence. With the NPU readily available on-chip, AI tasks are processed right from the device instead of off-loaded to a server, thereby providing better data privacy and security, the company explained.
The NPU offers enhancements to applications such as secure user authentication, content filtering, mixed reality, intelligent camera, and much more. Samsung Electronics plans to start mass producing the Exynos 980 within this year ahead of rival system chip makers such as Qualcomm and Media Tek that have also developed their own 5G-inetergrated mobile processors.?

7 Types Of Artificial Intelligence

Sep 13, 2019
7 Types Of Artificial Intelligence
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Artificial Intelligence is certainly the most complex and astounding creations of humanity yet. And that is disregarding the fact that the field stays largely undiscovered, which means that every amazing AI application that we see today shows purely the tip of the AI iceberg, as it were. While this fact may have been stated and restated numerous times, it is still hard to adequately gain perspective on the potential impact of AI in the future. The reason for this is the revolutionary impact that AI is having on society, even at such a relatively early stage in its evolution.
AI’s rapid growth and powerful capabilities have made people paranoid about the inevitability and proximity of an AI takeover. Moreover, the transformation brought about by AI in different industries has made business leaders and the mainstream public think that we are close to achieving the peak of AI research and maxing out AI’s potential. However, understanding the types of AI that are possible and the types that exist now will give a clearer picture of existing AI capabilities and the long road ahead for AI research.
Understanding the types of AI classification
Since AI research purports to make machines emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used as the criterion for determining the types of AI. Which means, subject to how a machine compares to humans in terms of versatility and performance, AI can be divided under one, among the different types of AI. Under such a system, an AI that can perform more human-like functions with equivalent levels of proficiency will be considered as a more evolved type of AI, while an AI that has limited functionality and performance would be considered a simpler and less evolved type.
Based on this criterion, there are two ways in which AI is normally classified. One type is based on classifying AI and AI-enabled machines based on their likeness to the human mind, and their ability to “think” and perhaps even “feel” like humans. According to this system of classification, there are four types of AI or AI-based systems: reactive machines, limited memory machines, theory of mind, and self-aware AI.
1. Reactive Machines
These are the oldest forms of AI systems that have extremely limited capability. They emulate the human mind’s ability to respond to different kinds of stimuli. These machines don't have memory-based functionality. This implies such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A prominent example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997. 
2. Limited Memory
Limited memory machines are machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of AI. All present-day AI systems, such as those using deep learning, are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems. For instance, an image recognition AI is trained using thousands of pictures and their labels to teach it to name objects it scans. When an image is scanned by such an AI, it uses the training images as references to understand the contents of the image presented to it, and based on its “learning experience” it labels new images with increasing accuracy.
Almost all present-day AI applications, from chatbots and virtual assistants to self-driving vehicles are all driven by limited memory AI.
3. Theory of Mind
While the previous two types of AI have been and are found in abundance, the next two types of AI exist, for now, either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently engaged in innovating. A theory of mind level AI will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. While artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers, achieving Theory of mind level of AI will require development in other branches of AI as well. This is because to truly understand human needs, AI machines will have to perceive humans as individuals whose minds can be shaped by multiple factors, essentially “understanding” humans.
4. Self-aware
This is the final stage of AI development which currently exists only hypothetically. Self-aware AI, which, self explanatorily, is an AI that has evolved to be so akin to the human brain that it has developed self-awareness. Creating this type of Ai, which is decades, if not centuries away from materializing, is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. And this is the type of AI that doomsayers of the technology are wary of. Although the development of self-aware can potentially boost our progress as a civilization by leaps and bounds, it can also potentially lead to catastrophe. This is because once self-aware, the AI would be capable of having ideas like self-preservation which may directly or indirectly spell the end for humanity, as such an entity could easily outmaneuver the intellect of any human being and plot elaborate schemes to take over humanity.
The alternate system of classification that is more generally used in tech parlance is the classification of the technology into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).
5. Artificial Narrow Intelligence (ANI)
This kind of artificial intelligence represents all the existing AI, including even the most complicated and capable AI that has ever been created to date. Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities. These machines can do nothing more than what they are programmed to do, and thus have a very limited or narrow range of competencies. According to the aforementioned system of classification, these systems correspond to all the reactive and limited memory AI. Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.
6. Artificial General Intelligence (AGI)
Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being. These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training. This will make AI systems just as capable as humans by replicating our multi-functional capabilities.
7. Artificial Superintelligence (ASI)
The development of Artificial Superintelligence will probably mark the pinnacle of AI research, as AGI will become by far the most capable forms of intelligence on earth. ASI, in addition to replicating the multi-faceted intelligence of human beings, will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, and decision-making capabilities. The development of AGI and ASI will lead to a scenario most popularly referred to as the singularity. And while the potential of having such powerful machines at our disposal seems appealing, these machines may also threaten our existence or at the very least, our way of life.
At this point, it is hard to picture the state of our world when more advanced types of AI come into being. However, it is clear that there is a long way to get there as the current state of AI development compared to where it is projected to go is still in its rudimentary stage. For those holding a negative outlook for the future of AI, this means that now is a little too soon to be worrying about the singularity, and there's still time to ensure AI safety. And for those who are optimistic about the future of AI, the fact that we've merely scratched the surface of AI development makes the future even more exciting.

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