Kim was a trade minister before transferring to the Blue House in February. 'Japan also relies on us for many parts. For example, we have 72.4% of market share for DRAM,' said Kim in a TBS radio program. 'If the supply of DRAM is halted for two months, the world will have problems producing 230 million smartphones. So we may use our dominance as an option.'

 

Kim's comments reflect Seoul's increasingly hard-line stance amid the increasing diplomatic tiff, which has spilled into the trade sphere. As recently as Monday, observers had thought the trade ministry would exempt DRAM from its list of export controls. South Korea is home to the world's two largest DRAM chipmakers, Samsung Electronics and SK Hynix. Samsung held 42.7% of market share in the first quarter, followed by SK Hynix with 29.9%, according to market research company Statista. Micron Group of the U.S. was third with 23%.

 

The deputy director's comments came shortly after the Ministry of Trade, Industry and Energy decided on Monday to downgrade Japan to A-2 in its export control system next month. This means that South Korean companies have to obtain special governmental approval before exporting more than 1,700 strategic materials and products to Japan. Analysts say Japanese companies will try to increase stocks of DRAM chips before they are subject to the tougher regulations.

 

'Companies will stock up on DRAM to hedge against uncertainty, which will help boost the global DRAM market,' said Lee Jae-yoon, an analyst at Yuanta Securities. 'Many Japanese companies import DRAM chips from South Korea for smartphones, PCs and servers.' Lee said that Japanese companies will likely try to source DRAM from Micron, which will lead to rising DRAM prices.

 

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'We can start using our Harmony OS anytime for smartphone and the migration from Google's Android to our own Harmony OS is not that difficult... We can do it in one to two days,' Yu said.

 

Harmony OS is an essential weapon in Huawei's fightback against the campaign by the U.S. government to restrict the technological development of the world's second biggest smartphone maker. It will allow the group to offer a common ecosystem of services and applications across all of its consumer devices. Nevertheless, in an implicit admission that Harmony OS could struggle in a consumer segment where 80% of all smartphones carry the Google system, Yu said Huawei would continue to prioritize using Android for its smartphones if allowed.

 

Huawei expidited development of Harmony OS after the U.S. government blacklisted China's biggest tech company to restrict its access to American technologies in May. Several Japanese carriers had postponed selling the new Huawei models amid worries about whether the Android system and popular apps like Gmail and Youtube would have access to Google upgrades. Most have since pressed ahead with sales, although it is still not clear whether the upgrades will be available.

 

Huawei intended to make Harmony open source to encourage wider use. 'We want it [HarmonyOS] to be global so we want to invite developers to join us as we build out this new ecosystem. We could together build a leading OS in the world.' To realize the goal, the company reported Friday it would spend $1 billion to support developers building a bigger ecosystem with Huawei.

 

Huawei established developing the system two years ago and now has 4,000 to 5,000 engineers working on Harmony OS, according to Yu. With a more unified ecosystem across various devices, it could offer better security against hacks than Android, Yu said. However, analysts said big challenges remained. 'Harmony OS's biggest weakness is that it has not yet grown into an ecosystem. It does not yet have apps developed for the OS. That's why Huawei said the OS will be first available on smart screens under its sub-brand, instead of on smartphones,' said Chiu Shih-fang, a veteran smartphone and supply chain analyst at Taiwan Institute of Economic Research.

 

If the Chinese company is unable to secure access to Google's service later this year, it is possible that sales of its upcoming smartphone lineups will take a hit in overseas markets as a result of a lack of confidence among telecom operators and consumers, the analyst said. 'However, it is still the right thing to do for Huawei in the longer term, to build its own operating system and take down its reliance on American companies, given the fast-changing geopolitical tensions between Washington and Beijing,' Chiu said.

 

Huawei has up until now shown some resilience in the face of the U.S. restrictions but the Washington ban is still weighing on its smartphone business. Yu acknowledged that his company could not overtake Samsung Electronics as the world's biggest smartphone maker this year as it had hoped due to the trade tension and uncertainties in the market. Huawei's extremely expected foldable smartphone, Mate X, a rival to Samsung's Galaxy Fold, will be available for customers as soon as next month but it could still be delayed as the device required additional tests on a 5G network, Yu told a small group of reporters in Dongguan on Friday. Samsung also delayed the launch of the Galaxy Fold to September due to quality issues. Both devices were revealed in February.

 

'We hope to still stay at the current position of the second-largest smartphone maker for 2019 but we could not achieve our previous goal to become world's No. 1 by the end of this year,' Yu said.

 

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Their analysis followed technology undercurrents to determine the geography and topology of innovation, which is the term the organization uses to refer to how the future workforce will deal with automation and other trends. They also looked at the different skill demands of employers embedded in job-postings data to understand their emerging workforce needs.

 

They then provide a review of the gaps between employers’ skill demand and worker skillsets to distinguish occupations that represent promising inclusion points for underrepresented categories. Highlights of the report include discussion of:

 

New innovation cities:

New innovation cities are making a grand entrance on the innovation hub scene and offer promising opportunities for startup pipelines and new job creation.

 

Skill combinations and transferability:

Innovation jobs and the skills required of an innovation workforce will be markedly different from those of the past. Employers are in the market for a combination of foundational STEM and tech-specific skills along with non-STEM skills, the portability of which offers workers opportunities to enrich their skill portfolios without starting over.

 

New policy lenses:

Increasing visibility into skill portability opens new policy lenses for identifying skill-based entry points and meaningful pathway progressions to quality jobs.

 

Targeted interventions:

Solving the STEM pipeline problem requires a multi-pronged approach to level the playing field, including shifting from generic STEM policies toward targeted interventions.

 

The organization offers their data as the ground for educators, policymakers and workforce planners to both perceive the current landscape and better prepare for the future. A skills-based topological examination of the job market provides a nuanced way for policy planners to assess the scale and breadth of emerging trends within local job markets and formulate policy responses that support workers and their innovation ecosystems.

 

Projecting labor market data onto a network uncovers specific pipeline junctures well-suited to high-impact, inclusionary policy strategies. Skill–occupation networks and skill gaps make clear that solving the STEM pipeline problem takes a multi-pronged approach to level the playing field. Including minorities in the innovation economy requires that Black, Hispanic, and Native American students have equitable access to core math and science training that, at a minimum, puts them on par with their Asian American peers.

 

Raising the absolute numbers of minority STEM graduates and employees will maximize their representation in core STEM occupations, but these policies alone are inadequate to address pipeline flows. Alternatively, policymakers need to expand their toolset to include holistic policies that strategically improve minorities’ skill competitiveness and close crucial STEM education gaps.

 

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So how will all of this affect the way we work? Leaders are unsure.  While 50% of business leaders agree that automated systems will free up their time, just as many 50% don’t agree. Of those that see automation lessen the workload they feel they can offload these areas;

 

1) Inventory management

2. Financial admin (i.e. invoices, POs)

3. Troubleshooting

4. Logistics/supply chain (i.e. delivery drivers)

5. Administration (i.e. scheduling meetings, data input)

6. Product design

7. Customer service

8. Marketing & communications

9. HR admin (recruitment and training)

10. Medical/health diagnoses

 

Whether this new working environment will lead to job satisfaction isn't clear either as only 42% believe they’ll have more job satisfaction in the future by offloading the tasks they don’t want to do to machines. Opinion is also split on whether workers will be more productive due to more collaboration with machines. And only half think they will learn on the job with AR. 

 

However, there is complete agreement on the fact that human and machines will work as integrated teams.  More than eight in ten (82%) leaders expect humans and machines will work as integrated teams within their organization inside of five years (26% say their workforce and machines are already successfully working this way. Looking at the future needs of the workforce and how  technical skills will fit into this scheme, 56% say schools will need to teach how to learn rather than what to learn to prepare students for jobs that don’t exist yet (corroborating IFTF’s forecast that 85% of jobs that will exist in 2030 haven’t been invented yet) - but 44% disagree.

 

In conclusion, across the results of the survey, these differing viewpoints could make it difficult for business leaders to confidently prepare for a future that’s in flux.

 

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AI researchers need each advantage they can get when dealing with the unprecedented computational requirements of deep learning. GPU processing power has advanced aggressively, and chips at first designed to render images have become the workhorses powering world-changing AI research and development. Many of the linear algebra routines that are necessary to make Fortnite run at 120 frames per second are now powering the neural networks at the heart of cutting-edge applications of computer vision, automated speech recognition, and natural language processing.  

 

Now, the movement toward microchip specialization is changing into an arms race. Gartner projects that specialized chip sales for AI will double to around US $8 billion in 2019 and achieve more than $34 billion by 2023. Nvidia’s internal projections place the market for data center GPUs (which are almost solely used to power deep learning) at $50 billion in the same time frame. In the next five years, we are going to see significant investments in custom silicon come to fruition from Amazon, ARM, Apple, IBM, Intel, Google, Microsoft, Nvidia, Qualcomm. There are also a slew of startups in the mix. CrunchBase forecasts that AI chip companies, including Cerebras, Graphcore, Groq, Mythic AI, SambaNova Systems, and Wave Computing, have jointly raised more than $1 billion. 

 

To be clear, specialized AI chips are both important and welcomed, as they’re catalysts for transforming cutting-edge AI research into real-world applications. And yet, the flood of new AI chips, each one faster and more specialized than the next, will also seem like a throwback to the rise of enterprise software. We can expect cut-throat sales deals and software specialization focused at locking developers into working with just one vendor. Imagine if, 15 years ago, the cloud services AWS, Azure, Box, Dropbox, and GCP all came to market within 12 to 18 months. Their mission would have been to lock in as many businesses as possible — because once you are on one platform, it is tough to switch to another. This type of end-user gold rush is about to happen in AI, with tens of billions of dollars, and priceless research, at stake. 

 

Chipmakers won’t be short on promises, and the benefits will be real. But it is important for AI developers to understand that new chips that require new architectures could make their products slower to market — even with faster performance. In most cases, AI models are not going to be portable between different chip makers. Developers are well aware of the vendor lock-in risk posed by adopting higher-level cloud APIs, but in the past, the actual compute substrate has been standardized and homogeneous. This situation is going to change dramatically in the world of AI development.

 

It is rather possibly that more than half of the chip industry’s revenue will soon be driven by AI and deep learning applications. Just as software begets more software, AI begets more AI. We have seen it many times: Companies at first focus on one problem, but in the end solve many. For example, major automakers are striving to bring autonomous cars to the road, and their cutting-edge work in deep learning and computer vision is already having a cascading effect; the research is leading to such offshoot projects as Ford’s delivery robots. As specialized AI chips come to market, the current chip giants and major cloud companies will probably strike exclusive deals or acquire top performing startups. This trend will fragment the AI market rather than unifying it. All that AI developers can do now is understand what’s about to happen and plan how they’ll weigh the benefits of a faster chip with the costs of building on new architectures.

 

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The median forecast was for 0.4%, according to a study by Nikkei Quick News. The slowdown was attributed to weak exports, which offset solid consumer spending and private investment. Japan had a 10-day holiday in May to mark Emperor Naruhito's ascension, which perhaps weighed on consumption and production, economists said.

 

The global economic outlook remains not certain in the face of heightened U.S.-China tensions. The International Monetary Fund in July lowered its overseas growth forecast for 2019 by 0.1 percentage point to 3.2%, although it said it expects growth to pick up to 3.5% in 2020. The Cabinet Office in July cut its forecast for Japanese growth to 0.9% from 1.3% for the year through March, 2020, quoting weak exports. That is equal to the latest IMF outlook.

 

Japan's exports fell, year on year, for the seventh straight month in June, as China's GDP growth slowed to 6.2% for April to June, the slowest pace since it began publishing data in 1992. Against this backdrop, Bank of Japan Gov. Haruhiko Kuroda said last week that he is 'more positive' about easing policy further to reach the central bank's 2% inflation target.

 

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“We’re ready to start taking orders, and I already have a few,” Beckhoff said to a large group of end users and machine builders who raised hands quickly to be next in line. Attendees witnessed hovering magnets specifically carrying glass vials, packaged meat, and other consumer goods while flying with six degrees of freedom above a flat surface area with no rails, visible paths, or configurations.

 

“This intelligent transport technology does not replace conveyor belts,” said Uwe Prüssmeier, Beckhoff’s senior product manager, Drive Technology. “But XPlanar clears the way for completely new innovative plant concepts. It could completely change the way you build systems.”

 

The XPlanar principle is free floating, non-contact planar movers that hover over a surface area made up of either steel, glass, or plastic tiles — depending on the end user or OEM’s preference, industry, or application. Traveling magnetic fields generated in the planar tiles specifically move objects in any kind of pattern through various tracks, enabling batch one applications and flexibility in product handling. The movers are available in different formats to fit a range of applications, and the tiles move chiefly to accommodate production and the individual goods by automatically lifting, lowering, weighing, tilting, or rotating while traveling.

 

XPlanar is driven by Beckhoff’s TwinCAT 3 software platform for control and engineering and the company’s industrial PC scalable hardware platform. The contactless movers enable maximum freedom of movement, according to the company. Because the movers are controlled by magnetic fields, there are no mechanically moving parts, which eliminate the possibility of wear, vibration, and noise. The XPlanar can also be used in demanding environments since the surface area tiles can be hygienically-designed for food and pharmaceutical applications.

 

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Advantech is using Nvidia’s Jetson platform in three AI edge systems to be produced later this year — the MIC-710IVA, MIC-720AI, and MIC-730AI. According to Advantech, these systems will grant AI application developers to rapidly create unique AI solutions based on Jetson. For cloud applications, Advantech’s SKY-6000 series servers with Nvidia’s graphics processing unit (GPU) enable the creation of high-density systems for big data aggregation. Featuring Advantech’s industrial design and Nvidia’s T4 GPU, these servers can resolve thermal issues mainly created by high-density GPU computing, according to Advantech.

 

These new announcements build on Advantech and Nvidia’s release earlier on this year of their AI Network Video Recording platform incorporating Advantech’s MIC-710IVA with Nvidia’s Jetson Nano. Nvidia describes Jetson Nano as a small computer on which different neural networks can be run in parallel for applications like image classification, object detection, segmentation, and speech processing. It can process using as little as 5 watts of power. “Advantech’s partnership with Nvidia is taking big steps forward in making AI a reality for manufacturing, transportation, and smart city applications. Through close collaboration in AI product development, Advantech and Nvidia are driving innovative transformations for AI applications,” said Linda Tsai, president of Advantech Industrial IoT Group.

 

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The real hallmark of success for an automated self-service system is its capability to understand cases where an automated response is not appropriate. It needs to adequately recognize the need for human intervention, and seamlessly put the customer through to an agent where necessary. When situations escalate or get emotional, this is when swift access to a live agent is required.

 

What AI Requires to Do Well

 

An AI-based system learns the best way to handle situations based on data and experience. This means chatbots are only as good as both the data they are given to learn from and the process steps that have been pre-programmed to automate customer service requests. So many background data is required for an AI chatbot to be able to answer any given customer issue. There may be many hundred different ways that a customer may phrase a query, and several response options available to resolve the issue. For instance, a leading credit card company analyzed how many different ways customers asked for their present account balance. The outcome was 2,100 distinct ways—a surprising amount of variation for what seems a pretty prescriptive request at first glance.

 

It is true that machine learning allows AI to bit by bit improve performance over time. But at the outset, initial training is needed to customize the algorithms — the rules — for certain use cases.  Live chat and email logs can provide real-world data to capture customer intents.  Once these are evaluated and understood, organizations can then move towards a machine-learning chatbot implementation.

 

AI-Based Triage

 

When deciding where to apply chatbot technology, concentrating on processing only a small amount of very high-volume customer requests can often create bigger business wins. For instance, 50% of all customer inquiries may in fact relate to just three very common, regularly seen issues or faults.  Pointing chatbot learning and training to recognize and triage these inquiries properly will instantly improve the customer experience, while reducing agent workloads.

 

Natural language processing (NLP) in an integrated platform that balances human and AI-assisted service can discover whether an interaction would be better resolved by a human agent or AI. If human, it will route it to the best-skilled advisor; if AI, it will use Robotic Process Automation (RPA) to process the requests and answer to the customer.  NLP technology can also extend beyond a chatbot — playing an integral role in email customer service support or online chat, automating and accelerating response times.

 

Self-service technology is not here to replace or diminish communication with agents — it is as an alternative a tool to manage interactions more successfully. By judiciously applying intelligent routing with AI, those customers who need or expect agent intervention receive it flawlessly with a better experience — while those who have a routine query can rapidly access the information they need.

 

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