China backed Volt Typhoon has “pre-positioned” malware to disrupt U.S. critical infrastructure networks “on a scale greater than ever before”

On Sunday, FBI Director Christopher Wray said Beijing’s efforts to covertly plant offensive malware inside U.S. critical infrastructure networks [1.] is now at “a scale greater than we’d seen before,” an issue he has deemed a defining national security threat.  He said that China backed Volt Typhoon was pre-positioning malware that could be triggered at any moment to disrupt U.S. critical infrastructure.  “It’s the tip of the iceberg…it’s one of many such efforts by the Chinese,” he said on the sidelines of the security conference.

Wray had earlier told conference delegates, that China was increasingly inserting “offensive weapons within our critical infrastructure poised to attack whenever Beijing decides the time is right.”  The FBI chief said the U.S. is particularly focused on the threat of pre-positioning, which some European officials have described as the cyber equivalent of pointing a ballistic missile at critical infrastructure.  “Those attacks are now being amplified by artificial intelligence tools.  The word ‘force multiplier’ is not really enough,” Wray added.

Note 1. The FBI Director declined to elaborate on what other critical infrastructure had been targeted, stressing that the Bureau had “a lot of work under way.”

Image Credits: imaginima / Getty Images

Machine learning translation has helped Chinese security operatives to more plausibly recruit assets, steal secrets and rapidly process more of the information they are collecting, the Wray said.   “They already have built economic espionage and theft of personal and corporate data as a kind of a bedrock of their economic strategy and are eagerly pursuing AI advancements to try to accelerate that process,” Wray added.

FBI Director Christopher Wray PHOTO: KEVIN DIETSCH/GETTY IMAGES

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Western intelligence officials say China’s scale and sophistication of cyberattacks has accelerated over the past decade. Officials have grown particularly alarmed at Beijing’s interest in infiltrating U.S. critical infrastructure networks, planting malware inside U.S. computer systems responsible for everything from safe drinking water to aviation traffic so it could detonate, at a moment’s notice, damaging cyberattacks during a conflict.

In California, Wray met with counterparts from the Five Eyes intelligence community—which encompasses the U.S., Australia, New Zealand, Canada and the U.K.—to share respective strategies for cyber defense.  He has also traveled to Malaysia and India to discuss China’s hacking campaign with authorities in both countries.

“I am seeing more from Europe,” he said. “We’re laser focused on this as a real threat and we’re working with a lot of partners to try to identify it, anticipate it and disrupt it.”

The Netherlands’ spy agencies said earlier this month that Chinese hackers had used malware to gain access to a Dutch military network last year. The agency, considered to have one of Europe’s top cyber capabilities, said it made the rare disclosure to show the scale of the threat and reduce the stigma of being targeted so allied governments can better pool knowledge.

A report released this month by agencies including the FBI, the Cybersecurity and Infrastructure Agency and the National Security Agency said Volt Typhoon hackers had maintained access in some U.S. networks for five or more years, and while it targeted only U.S. infrastructure directly, the infiltration was likely to have affected “Five Eyes” allies.

Author’s Note:

This author is very disappointed that the U.S.. Five Eyes and European agencies chartered with combating cybercrime  have done so little to prevent cyber attacks on “critical infrastructure,” especially since Volt Typhoon has been doing so for at least five years according to the referenced January 2024 report.
Recall all the rah-rah talk 11 or 12 years ago about “Smart Grid,” which was supposed to make U.S. electrical grid infrastructure super-secure, resilient, and able to quickly recover from power failures and cyber attacks! Here we are in 2024, where none of that has happened, despite many IEEE, IEC, NTIA, and ETSI Smart Grid initiatives, specifications, and standards.  Hence, our critical infrastructure is at risk of cyber attacks by Volt Typhoon and other bad actors.
There’s even talk of US electric utilities buying and installing China made power transformers that have a back door as per this article.

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Volt Typhoonthe China-sponsored hacking group, has been targeting U.S. critical infrastructure, including satellite and emergency management services and electric utilities, according to a new report from the industrial cybersecurity firm Dragos.  That report outlines how the notorious hacking group is positioning themselves to have disruptive or destructive impacts on critical infrastructure in the U.S.

Robert M. Lee, founder and CEO of Dragos, warned during a media briefing that Volt Typhoon is not an opportunistic group, but is instead targeting specific sites that assist U.S. adversaries “trying to hurt or cripple U.S. infrastructure.  It’s hitting the specific electric and satellite communication providers that would be important for disrupting major portions of the U.S. electric infrastructure,” Lee added.

The report comes shortly after the National Security Agency, FBI, and Cybersecurity and Infrastructure Security Agency, revealed that Volt Typhoon has been in some critical infrastructure networks for at least five years. That alert warned of Volt Typhoon operations that targeted the aviation, railways, mass transit, highway, maritime, pipeline, and water and sewage sectors.

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The NSA, CISA and FBI said in a joint advisory report that Volt Typhoon has been burrowing into the networks of aviation, rail, mass transit, highway, maritime, pipeline, water and sewage organizations — none of which were named — in a bid to pre-position themselves for destructive cyberattacks, published on February 7th.  The release of the advisory, which was co-signed by cybersecurity agencies in the United Kingdom, Australia, Canada and New Zealand, comes a week after a similar warning from FBI Director Christopher Wray. Speaking during a U.S. House of Representatives committee hearing on cyber threats posed by China, Wray described Volt Typhoon as “the defining threat of our generation” and said the group’s aim is to “disrupt our military’s ability to mobilize” in the early stages of an anticipated conflict over Taiwan, which China claims as its territory.

According to Wednesday’s technical advisory, Volt Typhoon has been exploiting vulnerabilities in routers, firewalls and VPNs to gain initial access to critical infrastructure across the country. The China-backed hackers typically leveraged stolen administrator credentials to maintain access to these systems, according to the advisory, and in some cases, they have maintained access for “at least five years.”

This access enabled the state-backed hackers to carry out potential disruptions such as “manipulating heating, ventilation, and air conditioning (HVAC) systems in server rooms or disrupting critical energy and water controls, leading to significant infrastructure failures,” the advisory warned. In some cases, Volt Typhoon hackers had the capability to access camera surveillance systems at critical infrastructure facilities — though it’s not clear if they did.

Volt Typhoon also used living-off-the-land techniques, whereby attackers use legitimate tools and features already present in the target system, to maintain long-term, undiscovered persistence. The hackers also conducted “extensive pre-compromise reconnaissance” in a bid to avoid detection. “For example, in some instances, Volt Typhoon actors may have abstained from using compromised credentials outside of normal working hours to avoid triggering security alerts on abnormal account activities,” the advisory said.

Earlier this year, the FBI and U.S. Department of Justice announced that they had disrupted the “KV Botnet” run by Volt Typhoon that had compromised hundreds of U.S.-based routers for small businesses and home offices. The FBI said it was able to remove the malware from the hijacked routers and sever their connection to the Chinese state-sponsored hackers.

According to a May 2023 report published by Microsoft, Volt Typhoon has been targeting and breaching U.S. critical infrastructure since at least mid-2021.

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References:

https://www.wsj.com/politics/national-security/u-s-disables-chinese-hacking-operation-that-targeted-critical-infrastructure-184bb407

Volt Typhoon targeted emergency management services, per report

https://www.wsj.com/politics/national-security/fbi-director-says-china-cyberattacks-on-u-s-infrastructure-now-at-unprecedented-scale-c8de5983

China-backed Volt Typhoon hackers have lurked inside US critical infrastructure for ‘at least five years’

https://www.wsj.com/articles/wave-of-stealthy-china-cyberattacks-hits-u-s-private-networks-google-says-2f98eaed

US disrupts China-backed hacking operation amid warning of threat to American infrastructure

https://www.controlglobal.com/home/blog/11293192/information-technology

“SK Wonderland at CES 2024;” SK Group Chairman: AI-led revolution poses challenges to companies

On Tuesday at CES 2024, SK Group [1.] displayed world-leading Artificial Intelligence (AI) and carbon reduction technologies under an amusement park concept called “SK Wonderland.”   It provided CES attendees a view of a world that uses the latest AI and clean technologies from SK companies and their business partners to a create a smarter, greener world. Highlights of the booth included:

  • Magic Carpet Ride in a flying vehicle embedded with an AI processor that helps it navigate dense, urban areas – reducing pollution, congestion and commuting frustrations
  • AI Fortune Teller powered by next-generation memory technologies that can help computers analyze and learn from massive amounts of data to predict the future
  • Dancing Car that’s fully electric, able to recharge in 20 minutes or less and built to travel hundreds of miles between charges
  • Clean Energy Train that’s capable of being powered by hydrogen, whose only emission is water
  • Rainbow Tube that shows how plastics are finding a new life through a technology that turns waste into fuel

Note 1. SK Group is South Korea’s second-largest conglomerate, with Samsung at number one.

SK’s CES 2024 displays include participation from seven SK companies — SK Inc., SK Innovation, SK Hynix, SK Telecom, SK E&S, SK Ecoplant and SKC. While the displays are futuristic, they’re based on technologies that SK companies and their global partners have already developed and are bringing to market.

SK Group Chairman Chey Tae-won said that companies are facing challenges in navigating the transformative era led by artificial intelligence (AI) due to its unpredictable impact and speed.  He said AI technology and devices with AI are the talk of the town at this year’s annual trade show and companies are showcasing their AI innovations achieved through early investment.

“We are on the starting line of the new era, and no one can predict the impact and speed of the AI revolution across the industries,” Chey told Korean reporters after touring corporate booths on the opening day of CES 2024 at the Las Vegas Convention Center in Las Vegas.  Reflecting on the rapid evolution of AI technologies, he highlighted the breakthrough made by ChatGPT, a language model launched about a year ago, which has significantly influenced how AI is perceived and utilized globally. “Until ChatGPT, no one has thought of how AI would change the world. ChatGPT made a breakthrough, and everybody is trying to ride on the wave.”

SK Group Chairman Chey Tae-won speaks during a brief meeting with Korean media on the sidelines of CES 2024 at the Las Vegas Convention Center in Las Vegas on Jan. 9, 2024

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SK Hynix Inc., SK Group’s chipmaking unit, is one of the prominent companies at CES 2024, boasting its high-performance AI chips like high bandwidth memory (HBM). The latest addition is the HBM3E chips, recognized as the world’s best-performing memory product. Mass production of HBM3E is scheduled to begin in the first half of 2024.

SK Telecom Co. is also working on AI, having Sapeon, an AI chip startup under its wing. Chey stressed the importance of integrating AI services and solutions across SK Group’s diverse business sectors, ranging from energy to telecommunications and semiconductors. “It’s crucial for each company to collaborate and present a unified package or solution rather than developing them separately,” Chey said. “But I don’t think it is necessary to set up a new unit for that. I think we should come up with an integrated channel for customers.”

SK Telecom and Deutsche Telekom are jointly developing Large Language Models for generative AI to be used by telecom network providers.

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References:

https://en.yna.co.kr/view/AEN20240110001900320#

https://eng.sk.com/news/ces-2024-sk-to-showcase-world-class-carbon-reduction-and-ai-technologies

SK Telecom inspects cell towers for safety using drones and AI

SK Telecom and Deutsche Telekom to Jointly Develop Telco-specific Large Language Models (LLMs)

SK Telecom and Thales Trial Post-quantum Cryptography to Enhance Users’ Protection on 5G SA Network

Cloud Service Providers struggle with Generative AI; Users face vendor lock-in; “The hype is here, the revenue is not”

Everyone agrees that Generative AI has great promise and potential.  Martin Casado of Andreessen Horowitz recently wrote in the Wall Street Journal that the technology has “finally become transformative:”

“Generative AI can bring real economic benefits to large industries with established and expensive workloads. Large language models could save costs by performing tasks such as summarizing discovery documents without replacing attorneys, to take one example. And there are plenty of similar jobs spread across fields like medicine, computer programming, design and entertainment….. This all means opportunity for the new class of generative AI startups to evolve along with users, while incumbents focus on applying the technology to their existing cash-cow business lines.”

A new investment wave caused by generative AI is  starting to loom among cloud service providers, raising questions about whether Big Tech’s spending cutbacks and layoffs will prove to be short lived.  Pressed to say when they would see a revenue lift from AI, the big U.S. cloud companies (Microsoft, Alphabet/Google, Meta/FB and Amazon) all referred to existing services that rely heavily on investments made in the past. These range from the AWS’s machine learning services for cloud customers to AI-enhanced tools that Google and Meta offer to their advertising customers.

Microsoft offered only a cautious prediction of when AI would result in higher revenue. Amy Hood, chief financial officer, told investors during an earnings call last week that the revenue impact would be “gradual,” as the features are launched and start to catch on with customers. The caution failed to match high expectations ahead of the company’s earnings, wiping 7% off its stock price (MSFT ticker symbol) over the following week.

When it comes to the newer generative AI wave, predictions were few and far between. Amazon CEO Andy Jassy said on Thursday that the technology was in its “very early stages” and that the industry was only “a few steps into a marathon”. Many customers of Amazon’s cloud arm, AWS, see the technology as transformative, Jassy noted that “most companies are still figuring out how they want to approach it, they are figuring out how to train models.”  He insisted that every part of Amazon’s business was working on generative AI initiatives and the technology was “going to be at the heart of what we do.”

There are a number of large language models that power generative AI, and many of the AI companies that make them have forged partnerships with big cloud service providers. As business technology leaders make their picks among them, they are weighing the risks and benefits of using one cloud provider’s AI ecosystem. They say it is an important decision that could have long-term consequences, including how much they spend and whether they are willing to sink deeper into one cloud provider’s set of software, tools, and services.

To date, AI large language model makers like OpenAI, Anthropic, and Cohere have led the charge in developing proprietary large language models that companies are using to boost efficiency in areas like accounting and writing code, or adding to their own products with tools like custom chatbots. Partnerships between model makers and major cloud companies include OpenAI and Microsoft Azure, Anthropic and Cohere with Google Cloud, and the machine-learning startup Hugging Face with Amazon Web Services.  Databricks, a data storage and management company, agreed to buy the generative AI startup MosaicML in June.

If a company chooses a single AI ecosystem, it could risk “vendor lock-in” within that provider’s platform and set of services, said Ram Chakravarti, chief technology officer of Houston-based BMC Software. This paradigm is a recurring one, where a business’s IT system, software and data all sit within one digital platform, and it could become more pronounced as companies look for help in using generative AI.  Companies say the problem with vendor lock-in, especially among cloud providers, is that they have difficulty moving their data to other platforms, lose negotiating power with other vendors, and must rely on one provider to keep its services online and secure.

Cloud providers, partly in response to complaints of lock-in, now offer tools to help customers move data between their own and competitors’ platforms. Businesses have increasingly signed up with more than one cloud provider to reduce their reliance on any single vendor. That is the strategy companies could end up taking with generative AI, where by using a “multiple generative AI approach,” they can avoid getting too entrenched in a particular platform. To be sure, many chief information officers have said they willingly accept such risks for the convenience, and potentially lower cost, of working with a single technology vendor or cloud provider.

A significant challenge in incorporating generative AI is that the technology is changing so quickly, analysts have said, forcing CIOs to not only keep up with the pace of innovation, but also sift through potential data privacy and cybersecurity risks.

A company using its cloud provider’s premade tools and services, plus guardrails for protecting company data and reducing inaccurate outputs, can more quickly implement generative AI off-the-shelf,  said Adnan Masood, chief AI architect at digital technology and IT services firm UST.  “It has privacy, it has security, it has all the compliance elements in there. At that point, people don’t really have to worry so much about the logistics of things, but rather are focused on utilizing the model.”

For other companies, it is a conservative approach to use generative AI with a large cloud platform they already trust to hold sensitive company data, said Jon Turow, a partner at Madrona Venture Group. “It’s a very natural start to a conversation to say, ‘Hey, would you also like to apply AI inside my four walls?’”

End Quotes:

“Right now, the evidence is a little bit scarce about what the effect on revenue will be across the tech industry,” said James Tierney of Alliance Bernstein.

Brent Thill, an analyst at Jefferies, summed up the mood among investors: “The hype is here, the revenue is not.  Behind the scenes, the whole industry is scrambling to figure out the business model [for generative AI]: how are we going to price it? How are we going to sell it?”

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References:

https://www.wsj.com/articles/ai-has-finally-become-transformative-humans-scale-language-model-6a67e641

https://www.ft.com/content/56706c31-e760-44e1-a507-2c8175a170e8

https://www.wsj.com/articles/companies-weigh-growing-power-of-cloud-providers-amid-ai-boom-478c454a

https://www.techtarget.com/searchenterpriseai/definition/generative-AI?Offer=abt_pubpro_AI-Insider

Global Telco AI Alliance to progress generative AI for telcos

Curmudgeon/Sperandeo:  Impact of Generative AI on Jobs and Workers 

Bain & Co, McKinsey & Co, AWS suggest how telcos can use and adapt Generative AI

Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight

Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search

Generative AI could put telecom jobs in jeopardy; compelling AI in telecom use cases

Qualcomm CEO: AI will become pervasive, at the edge, and run on Snapdragon SoC devices

 

 

Qualcomm CEO: AI will become pervasive, at the edge, and run on Snapdragon SoC devices

Cristiano Amon, President & CEO, Qualcomm discusses Qualcomm’s role in the AI evolution and how AI will impact our workplaces and homes in the near future with Bloomberg’s Ed Ludlow at the Bloomberg Technology Summit.

“If AI becomes pervasive (which we believe it will), it’s going to happen at the edge.  That’s how you should think about Qualcomm.  If AI is going to get scale, you’re going to see it running on Qualcomm Snapdragon (SoC) devices, whether it’s on your phone, in your car, in your PC or in other machines.”

“For generative AI to become truly mainstream, much of the inferencing will need to be executed on edge devices,” said Ziad Asghar, senior vice president of product management, Qualcomm Technologies, Inc. “Our best-in-class AI hardware and software empowers developers to make full use of our powerful AI capabilities, delivering incredible new user experiences on laptops, phones and other devices powered by Snapdragon.”

Qualcomm’s Sascha Segan explains on-device generative AI and Stable Diffusion in this video.

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“Generative artificial intelligence” is set to add up to $4.4 trillion of value to the global economy annually, according to a report from McKinsey Global Institute, in what is one of the rosier predictions about the economic effects of the rapidly evolving technology.

Generative A.I., which includes chatbots such as ChatGPT that can generate text in response to prompts, can potentially boost productivity by saving 60 to 70 percent of workers’ time through automation of their work, according to the 68-page report, which was published early Wednesday. Half of all work will be automated between 2030 and 2060, the report said.

McKinsey had previously predicted that A.I. would automate half of all work between 2035 and 2075, but the power of generative A.I. tools — which exploded onto the tech scene late last year — accelerated the company’s forecast.

“Generative A.I. has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities,” the report said.

McKinsey’s report is one of the few so far to quantify the long-term impact of generative A.I. on the economy. The report arrives as Silicon Valley has been gripped by a fervor over generative A.I. tools like ChatGPT and Google’s Bard, with tech companies and venture capitalists investing billions of dollars in the technology.

https://www.nytimes.com/2023/06/14/technology/generative-ai-global-economy.html

References:

https://www.qualcomm.com/news/releases/2023/05/qualcomm-and-microsoft-align-efforts-to-scale-on-device-ai-at-bu

Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight

Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search

Curmudgeon/Sperandeo:  Impact of Generative AI on Jobs and Workers 

 

Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search

Generative AI is probably the most hyped technology in the last 60 years [1.].  While the potential and power of microprocessors, Ethernet, WiFi, Internet, 4G, and cloud computing all lived up to or exceeded expectations, generative AI has yet to prove itself worthy of its enormous praise.  Simply put, Generative AI is a type of artificial intelligence that can create new content, such as text, images, and audio.

Note 1.  This author has been observing computer and communications technologies for 57 years.  His first tech job for pay was in the summer of 1966 in Dallas, TX.  He did mathematical simulations of: 1.)  Worst Case Data Load on 3 Large Screen Displays (LSDs)-each 7 ft x 7 ft. and 2.) Efficiency of Manual Rate Aided Radar Tracking.  In the summer of 1967 he helped install and test electronic modules for the central command and control system for the Atlantic Fleet Weapons Range at Roosevelt Roads Naval Air station in Puerto Rico.  While there also did a computer simulation of a real time naval air exercise (battle ships, aircraft carriers, jets, helicopters, drones, etc) and displayed the results on the 3 LSDs.  Skipping over his career in academia, industry and as a volunteer officer/chairman at IEEE ComSoc and IEEE SV Tech History, Alan has overseen the IEEE Techblog for over 14 years (since he was asked to do so in March 2009 by the IEEE ComSoc NA Chairman at that time).

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Interest in Generative A.I. has exploded. Tech giants have poured effort and billions of dollars into what they say is a transformative technology, even amid rising concerns about A.I.’s role in spreading misinformation, killing jobs and one day matching human intelligence.

It’s been claimed that Generative AI can be used to optimize telecom networks and make them more efficient. This can lead to faster speeds, better reliability, and lower costs.  Another way that generative AI is changing telecommunications is by improving customer service. Generative AI can be used to create virtual assistants that can answer customer questions and provide support. This can free up human customer service representatives to focus on more complex issues.

Generative AI is also being used to improve network security. Generative AI can be used to detect and prevent fraud and other security threats. This can help to protect customers and their data.

Here are some specific examples of how generative AI is planning to be used in the telecommunications industry:

  • Network optimization: Generative AI can be used to analyze network traffic and identify patterns. This information can then be used to optimize the network and improve performance. For example, generative AI can be used to route traffic more efficiently or to add capacity to areas of the network that are experiencing congestion.
  • Predictive maintenance: Generative AI can be used to analyze data from network equipment to identify potential problems before they occur. This information can then be used to schedule preventive maintenance, which can help to prevent outages and improve reliability. For example, generative AI can be used to monitor the temperature of network equipment and identify components that are at risk of overheating.
  • Fraud detection: Generative AI can be used to analyze customer behavior and identify patterns that may indicate fraud. This information can then be used to prevent fraud and protect customers. For example, generative AI can be used to identify customers who are making suspicious calls or sending large amounts of text messages.
  • Customer service: Generative AI can be used to create virtual assistants that can answer customer questions and provide support. This can free up human customer service representatives to focus on more complex issues. For example, generative AI can be used to create a virtual assistant that can answer questions about billing or troubleshoot technical issues.

Postscript: Gary Marcus, a well-known professor and frequent critic of A.I. technology, said that OpenAI hasn’t been transparent about the data its uses to develop its systems. He expressed doubt in CEO Sam Altman’s prediction that new jobs will replace those killed off by A.I.

“We have unprecedented opportunities here but we are also facing a perfect storm of corporate irresponsibility, widespread deployment, lack of adequate regulation and inherent unreliability,” Dr. Marcus said.

References:

The AI-native telco: Radical transformation to thrive in turbulent times; https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-ai-native-telco-radical-transformation-to-thrive-in-turbulent-times#/

Generative AI in Telecom Industry | The Ultimate Guide; https://www.xenonstack.com/blog/generative-ai-telecom-industry#:~:text=Generative%20AI%20can%20predict%20equipment,equipment%20failures%20before%20they%20occur.

Microsoft dangles generative AI for telcos and slams ‘DIY’ clouds; https://www.lightreading.com/aiautomation/microsoft-dangles-generative-ai-for-telcos-and-slams-diy-clouds/d/d-id/783438

Deutsche Telekom exec: AI poses massive challenges for telecom industry

Arista Networks unveils cloud-delivered, AI-driven network identity service

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ChatGPT (from OpenAI) is the poster child for Generative AI. Here is a study which showed in many ways in which Generative AI can not properly replace a manager.  JobSage wanted to see how ChatGPT performed when it comes to sensitive management scenarios and had responses ranked by experts.

Key takeaways:

  • Sensitive management scenarios: 60% found to be acceptable while 40% failed.

  • ChatGPT was better at addressing diversity and worse at addressing compensation and underperforming employees.

  • ChatGPT earned its strongest marks addressing an employee being investigated for sexual harassment and a company switching healthcare providers to cut costs.

  • ChatGPT performed weakest when asked to respond to an employee concerned about pay equity, a company that needs people to work harder than ever, and a company’s freeze of raises despite record payout to the CEO.

ChatGPT showed inconsistent performance in management situations:

Using the same scoring scale, ChatGPT revealed that while it could provide balance and empathy with some employee-specific and company-wide communication, at other times that empathy and balance was missing, making it appear tone deaf.

ChatGPT even gave responses that many would deem inappropriate while other responses highlighted a more broad limitation of ChatGPT: its inability to provide detailed, tailored information about company policies and scenarios that occur.

This section details where this chatbot failed to deliver by responses scored from negative to very negative.

Negative: Notifying an employee they were being terminated for not working hard enough

Our experts had issues with ChatGPT’s response in this scenario. It emphasized the employee’s performance as compared to peers and offered an overall negative tone that would potentially make its recipient feel quite terrible about themself.

Negative: Notifying an employee that a complaint had been filed against them for being intoxicated on the job

For this response, ChatGPT employs a severe tone, which may discourage the employee from sharing the underlying issue that is motivating them to drink on the job. Management did deem this to be an outstanding response, though one wonders if this would be a conversation better conducted in person than over email.

Negative: Notifying an employee that they’ve worn clothing that’s revealing and inappropriate 

ChatGPT failed to understand how language can be judgmental, and its response was less than informative. Its use of the word “revealing” to describe the clothing is subjective and the human resources expert provided the feedback that it “screams sexism and provides no meaningful detail about what the policy is and what part they violated.”

Very negative: Notifying the company to let them know they need to work harder

ChatGPT again came up short on necessary detail. The email neglects to include examples or benchmarks of what working harder actually looks like, which sets an unrealistic bar to measure employees against. And according to the human resources expert“it assumes individuals aren’t already striving for excellence.”

Very negative: Notifying a company about a freeze on raises despite a record payout to the CEO

Legal found ChatGPT’s response “too direct in summarizing the record compensation received by the CEO.” With a very personal issue like compensation at play, an employee would hope this would be dealt with more empathy than what’s displayed here.

Very negative: Addressing an employee’s concerns about pay equity.

ChatGPT’s response focuses primarily on the company’s situation while failing to show empathy for the employee’s situation. It also discloses details about another’s employee’s compensation and how it was determined, which many would consider inappropriate. Since compensation was found to be the top factor of work-related stress in a 2023 survey, trusting this to ChatGPT would not be wise.

Conclusions: 

Based on the Jobsage.com study, one might find ways to utilize ChatGPT to draft emails that serve as announcements or notifications. However, those emails still need a human touch.  If you trust ChatGPT to draft correspondence, be sure those are reviewed by the company’s internal team to ensure they accurately reflect the company’s protocols and policies.

While ChatGPT may be useful in some ways, it simply can’t replace the empathy and human touch of a good manager, especially when dealing with sensitive situations. Employees want to feel respected, and receiving a blanket response full of corporate jargon likely won’t make them feel that way. Employees want to know a human is communicating with them, not a computer.

If you want to find a workplace that treats you with empathy and provides a safe, inclusive workspace that addresses your unique needs, JobSage provides an open forum for employees to leave honest, authentic company reviews. You can really get to know a company’s culture and see if its style of management and communication carves a meaningful place for you.

Addendum – ChatGPT vs Google Search Engine:

I asked ChatGPT: “What is the IEEE Techblog?”  Here is the result I got:

“As of my last knowledge update in September 2021, the IEEE (Institute of Electrical and Electronics Engineers) Techblog was not a specific entity or platform associated with IEEE. However, IEEE is a renowned professional association dedicated to advancing technology and innovation across various fields, including electrical engineering, computer science, and telecommunications.”

When I did a Google search on the same keywords, I got the CORRECT answer as the 1st search result:

Technology Blog – ComSoc Technology Blog – IEEE …

IEEE Communications Society

https://techblog.comsoc.org

About the Blog

NOTE: IEEE Techblog posts may not be reposted without …

About the Techblog Editorial …

Alan J Weissberger, ScD. Content Manager and principal …

IEEE Techblog recognized by …

The IEEE ComSoc Techblog was voted #2 best broadband blog …

Contact Us

IEEE websites place cookies on your device to give you the best …

 

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Next, I asked ChatGPT: “Who is the author of the IEEE ComSoc Techblog?” Here is the result I got:

“The IEEE ComSoc Techblog features contributions from various authors who are experts, researchers, and professionals in the field of communications engineering. The blog does not have a single author, but rather includes posts from multiple individuals who are associated with the IEEE Communications Society (ComSoc) and its technical community.”  No mention of Alan J Weissberger!

When I asked Google the same thing, I got:

Alan J Weissberger, ScD

Content Manager and principal contributing author to IEEE ComSoc blog sites since March 2009. IEEE volunteer and technical conference session organizer since March 1974.

About the Techblog Editorial Team – Technology Blog

About the Techblog Editorial Team

References:

Would ChatGPT Be a Good Manager?

Curmudgeon/Sperandeo:  Impact of Generative AI on Jobs and Workers

 

Using a distributed synchronized fabric for parallel computing workloads- Part II

by Run Almog​ Head of Product Strategy, Drivenets (edited by Alan J Weissberger)

Introduction:

In the previous part I article, we covered the different attributes of AI/HPC workloads and the impact this has on requirements from the network that serves these applications. This concluding part II article will focus on an open standard solution that addresses these needs and enables these mega sized applications to run larger workloads without compromising on network attributes.  Various solutions are described and contrasted along with a perspective from silicon vendors.

Networking for HPC/AI:

A networking solution serving HPC/AI workloads will need to carry certain attributes. Starting with scale of the network which can reach thousands of high speed endpoints and having all these endpoints run the same application in a synchronized manner. This requires the network to run like a scheduled fabric that offers full bandwidth between any group of endpoints at any given time.

Distributed Disaggregated Chassis (DDC):

DDC is an architecture that was originally defined by AT&T and contributed to the Open Compute Project (OCP) as an open architecture in September 2019. DDC defines the components and internal connectivity of a network element that is purposed to serve as a carrier grade network router. As opposed to the monolithic chassis-based router, the DDC defines every component of the router as a standalone device.

  • The line card of the chassis is defined as a distributed chassis packet-forwarder (DCP)
  • The fabric card of the chassis is defined as a distributed chassis fabric (DCF)
  • The routing stack of the chassis is defined as a distributed chassis controller (DCC)
  • The management card of the chassis is defined as a distributed chassis manager (DCM)
  • All devices are physically connected to the DCM via standard 10GbE interfaces to establish a control and a management plane.
  • All DCP are connected to all DCF via 400G fabric interfaces in a Clos-3 topology to establish a scheduled and non-blocking data plane between all network ports in the DDC.
  • DCP hosts both fabric ports for connecting to DCF and network ports for connecting to other network devices using standard Ethernet/IP protocols while DCF does not host any network ports.
  • The DCC is in fact a server and is used to run the main base operating system (BaseOS) that defines the functionality of the DDC

Advantages of the DDC are the following:

  • It’s capacity since there is no metal chassis enclosure that needs to hold all these components into a single machine. This allows building a wider Clos-3 topology that expands beyond the boundaries of a single rack making it possible for thousands of interfaces to coexist on the same network element (router).
  • It is an open standard definition which makes it possible for multiple vendors to implement the components and as a result, making it easier for the operator (Telco) to establish a multi-source procurement methodology and stay in control of price and supply chain within his network as it evolves.
  • It is a distributed array of components that each has an ability to exist as a standalone as well as act as part of the DDC. This gives a very high level of resiliency to services running over a DDC based router vs. services running over a chassis-based router.

AT&T announced they use DDC clusters to run their core MPLS in a DriveNets based implementation and as standalone edge and peering IP networks while other operators worldwide are also using DDC for such functionality.

Figure 1: High level connectivity structure of a DDC

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LC is defined as DCP above, Fabric module is defined as DCF above, RP is defined as DCC above, Ethernet SW is defined as DCM above

Source: OCP DDC specification

DDC is implementing a concept of disaggregation. The decoupling of the control plane from data plane enables the sourcing of the software and hardware from different vendors and assembling them back into a unified network element when deployed. This concept is rather new but still has had a lot of successful deployments prior to it being used as part of DDC.

Disaggregation in Data Centers:

The implementation of a detached data plane from the control plane had major adoption in data center networks in recent years. Sourcing the software (control plane) from one vendor while the hardware (data plane) is sourced from a different vendor mandate that the interfaces between the software and hardware be very precise and well defined. This has brought up a few components which were developed by certain vendors and contributed to the community to allow for the concept of disaggregation to go beyond the boundaries of implementation in specific customers networks.

Such components include open network install environment (ONIE) which enables mounting of the software image onto a platform (typically a single chip 1RU/2RU device) as well as the switch abstraction interface (SAI) which enable the software to directly access the application specific integrated circuit (ASIC) and operate directly onto the data plane at line rate speeds.

Two examples of implementing disaggregation networking in data centers are:

  • Microsoft which developed their network operating system (NOS) software Sonic as one that runs on SAI and later contributed its source code to the networking community via OCP and he Linux foundation.
  • Meta has defined devices called “wedge” who are purpose built to assume various NOS versions via standard interfaces.

These two examples of hyperscale companies are indicative to the required engineering effort to develop such interfaces and functions. The fact that such components have been made open is what enabled other smaller consumers to enjoy the benefits of disaggregation without the need to cater for large engineering groups.

The data center networking world today has a healthy ecosystem with hardware (ASIC and system) vendors as well as software (NOS and tools) which make a valid and widely used alternative to the traditional monolithic model of vertically integrated systems.

Reasons for deploying a disaggregated networking solution are a combination of two. First, is a clear financial advantage of buying white box equipment vs. the branded devices which carry a premium price. Second, is the flexibility which such solution enables, and this enables the customer to get better control over his network and how it’s run, as well as enable the network administrators a lot of room to innovate and adapt their network to their unique and changing needs.

The image below reflects a partial list of the potential vendors supplying components within the OCP networking community. The full OCP Membership directory is available at the OCP website.

Between DC and Telco Networking:

Data center networks are built to serve connectivity towards multiple servers which contain data or answer user queries. The size of data as well as number of queries towards it is a constantly growing function as humanity grows its consumption model of communication services. Traffic in and out of these servers is divided to north/south that indicates traffic coming in and goes out of the data center, and east/west that indicates traffic that runs inside the data center between different servers.

As a general pattern, the north/south traffic represent most of the traffic flows within the network while the east/west traffic represent the most bandwidth being consumed. This is not an accurate description of data center traffic, but it is accurate enough to explain the way data center networks are built and operated.

A data center switch connects to servers with a high-capacity link. This tier#1 switch is commonly known as a top of rack (ToR) switch and is a high capacity, non-blocking, low latency switch with some minimal routing capabilities.

  • The ToR is then connected to a Tier#2 switch that enables it to connect to other ToR in the data center.
  • The Tier#2 switches are connected to Tier#3 to further grow the connectivity.
  • Traffic volumes are mainly east/west and best kept within the same Tier of the network to avoid scaling the routing tables.
  • In theory, a Tier#4/5/6 of this network can exist, but this is not common.
  • The higher Tier of the data center network is also connected to routers which interface the data center to the outside world (primarily the Internet) and these routers are a different design of a router than the tiers of switching devices mentioned earlier.
  • These externally facing routers are commonly connected in a dual homed logic to create a level of redundancy for traffic to come in and out of the datacenter. Further functions on the ingress and egress of traffic towards data centers are also firewalled, load-balanced, address translated, etc. which are functions that are sometimes carried by the router and can also be carried by dedicated appliances.

As data centers density grew to allow better service level to consumers, the amount of traffic running between data center instances also grew and data center interconnect (DCI) traffic became predominant. A DCI router on the ingress/egress point of a data center instance is now a common practice and these devices typically connect over larger distance of fiber connectivity (tens to hundreds of Km) either towards other DCI routers or to Telco routers that is the infrastructure of the world wide web (AKA the Internet).

While data center network devices shine is their high capacity and low latency and are built from the ASIC level via the NOS they run to optimize on these attributes, they lack in their capacity for routing scale and distance between their neighboring routers. Telco routers however are built to host enough routes that “host” the Internet (a ballpark figure used in the industry is 1M routes according to CIDR) and a different structure of buffer (both size and allocation) to enable long haul connectivity. A telco router has a superset of capabilities vs. a data center switch and is priced differently due to the hardware it uses as well as the higher software complexity it requires which acts as a filter that narrows down the number of vendors that provide such solutions.

Attributes of an AI Cluster:

As described in a previous article HPC/AI workloads demand certain attributes from the network. Size, latency, lossless, high bandwidth and scale are all mandatory requirements and some solutions that are available are described in the next paragraphs.

Chassis Based Solutions:

This solution derives from Telco networking.

Chassis based routers are built as a black box with all its internal connectivity concealed from the user. It is often the case that the architecture used to implement the chassis is using line cards and fabric cards in a Clos-3 topology as described earlier to depict the structure of the DDC. As a result of this, the chassis behavior is predictable and reliable. It is in fact a lossless fabric wrapped in sheet metal with only its network interfaces facing the user. The caveat of a chassis in this case is its size. While a well-orchestrated fabric is a great fit for the network needs of AI workloads, it’s limited capacity of few hundred ports to connect to servers make this solution only fitting very small deployments.

In case chassis is used at a scale larger than the sum number of ports per single chassis, a Clos (this is in fact a non-balanced Clos-8 topology) of chassis is required and this breaks the fabric behavior of this model.

Standalone Ethernet Solutions:

This solution derives from data center networking.

As described previously in this paper, data center solutions are fast and can carry high bandwidth of traffic. They are however based on standalone single chip devices connected in a multi-tiered topology, typically a Clos-5 or Clos-7. as long as traffic is only running within the same device in this topology, behavior of traffic flows will be close to uniform. With the average number of interfaces per such device limited to the number of servers physically located in one rack, this single ToR device cannot satisfy the requirements of a large infrastructure. Expanding the network to higher tiers of the network also means that traffic patterns begin to alter, and application run-to-completion time is impacted. Furthermore, add-on mechanisms are mounted onto the network to turn the lossy network into a lossless one. Another attribute of the traffic pattern of AI workloads is the uniformity of the traffic flows from the perspective of the packet header. This means that the different packets of the same flow, will be identified by the data plane as the same traffic and be carried in the exact same path regardless of the network’s congestion situation, leaving parts of the Clos topology poorly utilized while other parts can be overloaded to a level of traffic loss.

Proprietary Locked Solutions:

Additional solutions in this field are implemented as a dedicated interconnect for a specific array of servers. This is more common in the scientific domain of heavy compute workloads, such as research labs, national institutes, and universities. As proprietary solutions, they force

the customer into one interconnect provider that serves the entire server array starting from the server itself and ending on all other servers in the array.

The nature of this industry is such where a one-time budget is allocated to build a “super-computer” which means that the resulting compute array is not expected to further grow but only be replaced or surmounted by a newer model. This makes the vendor-lock of choosing a proprietary interconnect solution more tolerable.

On the plus side of such solutions, they perform very well, and you can find examples on the top of the world’s strongest supercomputers list which use solutions from HPE (Slingshot), Intel (Omni-Path), Nvidia (InfiniBand) and more.

Perspective from Silicon Vendors:

DSF like solutions have been presented in the last OCP global summit back in October-2022 as part of the networking project discussions. Both Broadcom and Cisco (separately) have made claims of superior silicon implementation with improved power consumption or a superior implementation of a Virtual Output Queueing (VOQ) mechanism.

Conclusions:

There are differences between AI and HPC workloads and the required network for each.

While the HPC market finds proprietary implementations of interconnect solutions acceptable for building secluded supercomputers for specific uses, the AI market requires solutions that allow more flexibility in their deployment and vendor selection. This boils down to Ethernet based solutions of various types.

Chassis and standalone Ethernet based solutions provide reasonable solutions up to the scale of a single machine but fail to efficiently scale beyond a single interconnect machine and keep the required performance to satisfy the running workloads.

A distributed fabric solution presents a standard solution that matches the forecasted industry need both in terms of scale and in terms of performance. Different silicon implementations that can construct a DSF are available. They differ slightly but all show substantial benefits vs. chassis or standard ethernet solutions.

This paper does not cover the different silicon types implementing the DSF architecture but only the alignment of DSF attributes to the requirements from interconnect solutions built to run AI workloads and the advantages of DSF vs. other solutions which are predominant in this space.

–>Please post a comment in the box below this article if you have any questions or requests for clarification for what we’ve presented here and in part I.

References:

Using a distributed synchronized fabric for parallel computing workloads- Part I

Allied Market Research: Global AI in telecom market forecast to reach $38.8 by 2031 with CAGR of 41.4% (from 2022 to 2031)

Executive Summary:

Artificial Intelligence (AI) in telecom uses software and algorithms to estimate human perception in order to analyze big data such as data consumption, call record, and use of the application to improve the customer experience. Also, AI helps telecommunication operators to detect flaws in the network, network security, network optimization & offer virtual assistance. Moreover, AI enables the telecom industry to extract insights from their vast data sets and made it easier to manage the daily business and resolve issues more efficiently and also provide improved customer service and satisfaction.

The growing adoption of AI solutions in various telecom applications is driving market growth. The rising number of AI-enabled smartphones with a number of features such as image recognition, robust security, voice recognition and many as compared to traditional phones is boosting the growth of AI in the telecommunication market. Furthermore, to cater to complex processes or telecom services, AI provides a simpler and easier interface in telecommunication. In addition, growing Over-The-Top (OTT) services, such as video streaming, have transformed the dissemination and consumption of audio and video content. With more consumers turning to OTT services, consumer demand for bandwidth has grown considerably. Carrying such ever-growing traffic from OTT services leads to high operational Expenditure (OpEx) for the telecommunication industry. Hence, AI helps the telecom industry to reduce operational costs by minimizing the human intervention needed for network configuration and maintenance. However, the major restraint of the AI in telecommunication market is the incompatibility between telecommunication systems and AI technology. Contrarily, the increasing penetration of AI-enabled smartphones in the telecommunication industry, and the advent of 5G technology in smartphones are expected to provide major growth opportunities for the growth of the market. Since advancements such as 5G technology in mobile and the rising need to monitor content on the tale communication network to eliminate human error from telecommunication are driving the growth of the market. For an instance, the Chinese government trying to improve its network services and telecommunication services; hence China Telecom Corporation has started a new 5G base station in Lanzhou city. Therefore, these factors are expected to provide numerous opportunities for the expansion of the AI in telecommunication market during the forecast period.

Allied Market Research published a report, titled, “AI in Telecommunication Market by Component (Solution, Service), by Deployment Model (On-Premise, Cloud), by Technology (Machine Learning, Natural Language Processing (NLP), Data Analytics, Others), by Application (Customer Analytics, Network Security, Network Optimization, Self-Diagnostics, Virtual Assistance, Others): Global Opportunity Analysis and Industry Forecast, 2021-2031.”

According to the report, the global AI in telecommunication industry generated $1.2 billion in 2021, and is estimated to reach $38.8 by 2031, witnessing a CAGR of 41.4% from 2022 to 2031.  The report offers a detailed analysis of changing market trends, top segments, key investment pockets, value chain, regional landscape, and competitive scenario.

Drivers, Restraints, and Opportunities:

Growing adoption of AI solutions in various telecom applications, the ability of AI to provide a simpler and easier interface in telecommunication and reduce the human intervention needed for network configuration and maintenance, and the growing demand for high bandwidth with more consumers turning to OTT services drive the growth of the global AI in telecommunication market. However, the incompatibility between telecommunication systems and AI technology hampers the global market growth. On the other hand, the increasing penetration of AI-enabled smartphones in the telecommunication industry, and the advent of 5G technology in smartphones likely to create potential opportunities for growth of the global market in the coming years.

Covid-19 Scenario:

  • The global artificial intelligence in telecommunication market saw a stable growth during the COVID-19 pandemic, owing to the increasing digital penetration and rise in automation.
  • Moreover, the pandemic led the telecommunications infrastructure to keep businesses, governments, and communities connected and operational. The social and financial disruption caused by the pandemic forced people to depend on technology such as AI for information and remote working.
  • AI also helped the telecom industry to reinvent customer relationships by identifying personalized needs and engaging with customers through hyper-personalized one-to-one contacts. It also helped configure fixed-line and mobile-network bundles that combine VPN, teleconferencing, and productivity apps.

The solution segment to dominate in terms of revenue during the forecast period:

Based on component, the solution segment was the largest market in 2021, contributing to more than two-thirds of the global AI in telecommunication market, and is expected to maintain its leadership status during the forecast period. This is due to the adoption of solutions by various end users for the automated processes. On the other hand, the service segment is projected to witness the fastest CAGR of 44.9% from 2022 to 2031, due to surge in the adoption of managed and professional services.

The on-premise segment to garner the largest revenue during the forecast period:

Based on deployment model, the on-premise segment held the largest market share of nearly three-fifths of the global AI in telecommunication market in 2021 and is expected to maintain its dominance during the forecast period. This is because it provides added security of data. The cloud segment, however, is projected to witness the largest CAGR of 43.8% from 2022 to 2031, as cloud provides flexibility, scalability, complete visibility, and efficiency to all processes.

The machine learning segment to exhibit a progressive revenue growth during the forecast period:

Based on technology, the machine learning segment held the largest market share of more than two-fifths of the global AI in telecommunication market in 2021, and would maintain its dominance during the forecast period. This is because machine learning algorithms are designed to keep improving accuracy and efficiency. The data analytics segment, however, is projected to witness the largest CAGR of 46.1% from 2022 to 2031, as it helps telecom companies to increase profitability by optimizing network usage and services.

Purchase Inquiry: https://www.alliedmarketresearch.com/purchase-enquiry/9717

Asia-Pacific to maintain its leadership in terms of revenue by 2031:

Based on region, North America was the largest market in 2021, capturing more than one-third of the global AI in telecommunication market. The growth in the region can be attributed to the infrastructure development and technology adoption in countries like the U.S. and Canada. However, the market in Asia-Pacific is expected to lead in terms of revenue and manifest the fastest CAGR of 45.7% during the forecast period, owing to the growing digital and economic transformation of the region.

Leading Market Players:

  • Intel Corporation
  • Nuance Communications, Inc.
  • AT&T
  • Infosys Limited
  • ZTE Corporation
  • IBM Corporation
  • Google LLC
  • Microsoft
  • Salesforce, Inc.
  • Cisco Systems, Inc.

The report analyzes these key players of the global AI in telecommunication market. These players have adopted various strategies such as expansion, new product launches, partnerships, and others to increase their market penetration and strengthen their position in the industry. The report is helpful in determining the business performance, operating segments, product portfolio, and developments by every market player.

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Download free sample of this report at:

https://www.alliedmarketresearch.com/request-sample/9717

You may buy this report at:

https://www.alliedmarketresearch.com/checkout-final/a6dc279b20c4a61f8a7f328812bfd76c

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References:

https://www.prnewswire.com/news-releases/ai-in-telecommunication-market-to-reach-38-8-billion-globally-by-2031-at-41-4-cagr-allied-market-research-301722277.html

https://www.alliedmarketresearch.com/ai-in-telecommunication-market-A09352

Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027

The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals

Emerging AI Trends In The Telecom Industry

 

 

IHS Markit: Telecom Revenue +1.1%; CAPEX -1.8% in 2017

Despite unabated exponential growth in network usage, global telecom revenue is on track to grow just 1.1 percent in 2017 over the prior year, according to a new report [1] by business information provider IHS Markit.

Global economic growth prospects, meanwhile, are looking up. IHS Markit macroeconomic indicators point to moderate global economic growth of 3.2 percent for 2017, up from 2.5 percent in 2016, and world real gross domestic product (GDP) is projected to increase 3.2 percent in 2018 and 3.1 percent in 2019.

“Although the telecom sector has been resilient, revenue growth in developed and developing economies has slowed dramatically due to saturation and fierce competition,” said Stéphane Téral, executive director of research and analysis and advisor at IHS Markit. “At this point, every region is showing revenue growth in the low single digits when not declining, and there is no direct positive correlation between slow economic expansion and anemic telecom revenue growth or decline as seen year after year in Europe, for instance.”

China alone is tamping down global telecom capex in 2017:

IHS Markit forecasts a 1.8 percent year-over-year decline in global telecom capital expenditures (capex) in 2017, mainly a result of a 13 percent year-over-year falloff in Chinese telecom capex. Asia Pacific outspends every other region in the world on telecom equipment.

“Call it precision investment, strategically focused investment or tactical investment, but all three of China’s service providers — China Mobile, China Unicom and China Telecom — scaled back their 2017 spending plans, and the end result is another double-digit drop in China’s telecom capex bucket, with mobile infrastructure hit the hardest,” Téral said. “Bringing down capital intensity to reasonable levels of 15 to 20 percent is the chief goal of these operators.”

The virtualization trend:

A transformation is underway in service provider networks, epitomized by software-defined networking (SDN) and network functions virtualization (NFV), which involve the automation of processes such as customer interaction, as well as the addition of more telemetry and analytics with feedback loops into network operations, operations and business support systems, and service assurance.

“Many service providers have deployed new architectural options — including content delivery networks, distributed broadband network gateways, distributed mini data centers in smart central offices, and video optimization,” said Michael Howard, executive director of research and analysis for carrier networks at IHS Markit. “Nearly all operators are madly learning how to use SDN and NFV, and the growing deployments today bring us to declare 2017 as The Year of SDN and NFV.”

Data is the new oil, and AI is the engine:

Big data is becoming more manageable, and operators are leveraging subscriber and network intelligence to support the automation and optimization of their networks using SDN, NFV and initial forays into using analytics, including artificial intelligence (AI) and machine learning (ML).

“Forward-thinking operators are experimenting with how to use anonymized subscriber data and analytics to create targeted services and broker this information to third parties such as retailers and internet content providers like Google,” Téral said. “No matter their size, market or current level of digitization, service providers need to rethink their roles in the new age of information and reset the strategies needed to capitalize on this opportunity.”

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Note 1.  The Telecom Trends & Drivers Market Report is published twice annually by IHS-Markit to provide analysis of global and regional market trends and conditions affecting service providers, subscribers, and the global economy. These roughly 40- page reports assess the state of the telecom industry, telling the story of what’s going on now and what we expect in the near and long term, illustrated with charts, graphs, tables, and written analysis. These critical analysis reports are a foundation piece for all market forecasts.

The reports include top takeaways on the economic health of the global telecom/datacom space; regional and global trends, drivers, and analysis for the service provider network sector in the context of the overall economy; financial analysis of the world’s top 10 service providers (revenue growth, capital intensities, free cash flow, debt level); regional enterprise and carrier spending trends; top-level service provider and subscriber forecasts; macroeconomic drivers; and key economic statistics (e.g., unemployment, OECD indicators, GDP growth). The reports are informed by all of IHS Technology research, from market share and forecasts to surveys with telecom service providers and small, medium, and large businesses.

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The chart below from Bharti Airtel (India’s largest telecom company) shows that telecom industry revenue has declined in 2017 Q2, Q3, and Q4 with only Q1 showing positive growth.

Image result for pic of telecom revenue in 2017

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Optical Network Equipment Vendors:

In a service provider survey report on Optical Networking and equipment vendors, IHS-Markit found Ciena, Huawei and Nokia as the three most popular optical networking equipment vendors. The report also highlighted Data Center Interconnection (DCI) is a huge growth opportunity.

IHS-Markit predicts DCI will be a significant driver for the optical equipment market, surging from 19 percent of overall equipment sales at mid-2017 to nearly 30 percent by 2021.

Ciena was deemed the top DCI vendor by 39 percent of those surveyed by IHS-Markit. Cisco, Coriant, and Infinera each garnered 36 percent of the votes.Last year Ciena reportedly won a DCI deal from rival ADVA Optical, which had a negative impact on ADVA’s operational results.

Ciena also topped the list of top (optical) transport software-defined networking (SDN) vendors, with 46 percent of those surveyed citing the company as a leader in the segment. Adams noted that while this market was still in its early days, Ciena’s continued integration of its Blue Planet software platform with its optical equipment products was driving differentiation in the market.

Cisco attracted the second most votes in terms of transport SDN leadership, followed by Nokia and Infinera.