AI/ML
Bloomberg: China Lures Billionaires Into Race to Catch U.S. in AI
China’s tech sector has a new obsession: competing with U.S. titans like Google and Microsoft Corp. in the breakneck global artificial intelligence race. A ChatGPT-inspired global wave of AI activity is only just beginning in the next battle for supremacy in technology.
Billionaire entrepreneurs, mid-level engineers and veterans of foreign firms alike now harbor a remarkably consistent ambition: to outdo China’s geopolitical rival in a technology that may determine the global power stakes. Among them is internet mogul Wang Xiaochuan, who entered the field after OpenAI’s ChatGPT debuted to a social media firestorm in November. He joins the ranks of Chinese scientists, programmers and financiers — including former employees of ByteDance Ltd., e-commerce platform JD.com Inc. and Google — expected to propel some $15 billion of spending on AI technology this year.
For Wang, who founded the search engine Sogou that Tencent Holdings Ltd. bought out in a $3.5 billion deal less than two years ago, the opportunity came fast. By April, the computer science graduate had already set up his own startup and secured $50 million in seed capital. He reached out to former subordinates at Sogou, many of whom he convinced to come on board. By June, his firm had launched an open-source large language model and it’s already in use by researchers at China’s two most prominent universities.
“We all heard the sound of the starter pistol in the race. Tech companies, big or small, are all on the same starting line,” Wang, who named his startup Baichuan or “A Hundred Rivers,” told Bloomberg News. “China is still three years behind the US, but we may not need three years to catch up.”
The top-flight Chinese talent and financing flowing into AI mirrors a wave of activity convulsing Silicon Valley, which has deep implications for Beijing’s escalating conflict with Washington. Analysts and executives believe AI will shape the technology leaders of the future, much like the internet and smartphone created a corps of global titans. Moreover, it could propel applications from supercomputing to military prowess — potentially tilting the geopolitical balance.
China is a vastly different landscape — one reined in by US tech sanctions, regulators’ data and censorship demands, and Western distrust that limits the international expansion of its national champions. All that will make it harder to play catch-up with the US.
AI investments in the US dwarf that of China, totaling $26.6 billion in the year to mid-June versus China’s $4 billion, according to previously unreported data collated by consultancy Preqin.
China’s Catch-Up Game
The aggregate size of US deals in AI still outpaces China’s in 2023
Source: Preqin
Note: 2023 data up to June 14th
Yet that gap is already gradually narrowing, at least in terms of deal flow. The number of Chinese venture deals in AI comprised more than two-thirds of the US total of about 447 in the year to mid-June, versus about 50% over the previous two years. China-based AI venture deals also outpaced consumer tech in 2022 and early 2023, according to Preqin.
All this is not lost on Beijing. Xi Jinping’s administration realizes that AI, much like semiconductors, will be critical to maintaining China’s ascendancy and is likely to mobilize the nation’s resources to drive advances. While startup investment cratered during the years Beijing went after tech giants and “reckless expansion of capital,” the feeling is the Party encourages AI exploration.
It’s a familiar challenge for Chinese tech players. During the mobile era, a generation of startups led by Tencent, Alibaba Group Holding Ltd. and TikTok-owner ByteDance built an industry that could genuinely rival Silicon Valley. It helped that Facebook, YouTube and WhatsApp were shut out of the booming market of 1.4 billion people. At one point in 2018, venture capital funding in China was even on track to surpass that of the U.S. — until the trade war exacerbated an economic downturn. That situation, where local firms thrive when U.S. rivals are absent, is likely to play out once more in an AI arena from which ChatGPT and Google’s Bard are effectively barred.
Large AI models could eventually behave much like the smartphone operating systems Android and iOS, which provided the infrastructure or platforms on which Tencent, ByteDance and Ant Group Co. broke new ground: in social media with WeChat, video with Douyin and Tiktok, and payments with Alipay. The idea is that generative AI services could speed the emergence of new platforms to host a wave of revolutionary apps for businesses and consumers.
That’s a potential gold mine for an industry just emerging from the trauma of Xi’s two-year internet crackdown, which starved tech companies of the heady growth of years past. No one today wants to miss out on what Nvidia Corp. CEO Jensen Huang called the “iPhone moment” of their generation.
“This is an AI arms race going on both in the US and China,” said Daniel Ives, a senior analyst at Wedbush Securities. “China tech is dealing with a stricter regulatory environment around AI, which puts one hand behind the back in this ‘Game of Thrones’ battle. This is an $800 billion market opportunity globally over the next decade we estimate around AI, and we are only on the very early stages.”
The resolve to catch OpenAI is apparent in the seemingly haphazard fashion in which incumbents from Baidu Inc. and SenseTime Group Inc. to Alibaba have trotted out AI bots in the span of months.
Joining them are some of the biggest names in the industry. Their ranks include Wang Changhu, the former director of ByteDance’s AI Lab; Zhou Bowen, ex-president of JD.com Inc.’s AI and cloud computing division; Meituan co-founder Wang Huiwen and current boss Wang Xing; and venture capitalist Kai-fu Lee, who made his name backing Baidu.
Ex-Baidu President Zhang Yaqin, now dean of Tsinghua University’s Institute for AI Industry Research and overseer of a number of budding projects, told Chinese media in March that investors sought him out almost daily that month. He estimates there’re as many as 50 firms working on large language models across the country. Wang Changhu, former lead researcher at Microsoft Research before he joined Bytedance in 2017, said dozens of investors approached him on WeChat in a single day when he was preparing to set up his generative AI startup.
“This is at least a once-in-a-decade opportunity, an opportunity for startups to create companies comparable to the behemoths,” Wang told Bloomberg News.
Many of the fledgling firms are squarely aimed at the home crowd, given growing concern in the West about Chinese technology. Even so, there’s an open field in a consumer market ringfenced to themselves, which also happens to be the world’s largest internet arena. In the works are AI-fueled applications, from a chatbot to help manufacturers track consumption trends, to an intelligent operating system offering companionship to counter depression, and smart enterprise tools to transcribe and analyze meetings.
Still, Chinese demos so far make it clear that most have a long way to go. The skeptical point out true innovation requires the free-wheeling exploration and experimentation that the US cultivates but is restrained in China. Pervasive censorship in turn means the datasets that China’s aspirants are using are inherently flawed and artificially constrained, they argue.
“Investors are chasing the concept,” said Grant Pan, chief financial officer of Noah Holdings, whose subsidiary Gopher invests in over 100 funds including Sequoia China (now HongShan) and ZhenFund in China. “However, the commercial use and impact to industry chains are not clear yet.”
Then there are Beijing’s regulations on generative AI, with its top internet overseer signaling that the onus for training algorithms and implementing censorship will fall on platform providers.
China Closing the Gap in AI Dealmaking
The number of AI startup deals in China is on a path to parity with the US
Source: Preqin
Note: 2023 data up to June 14
“Beijing’s censorship regime will put China’s ChatGPT-like applications at a serious disadvantage vis-à-vis their US peers,” said Xiaomeng Lu, director of the Eurasia Group’s geotechnology practice.
Last but not least, powerful chipsets from the likes of Nvidia and Advanced Micro Devices Inc. are crucial in training large AI models — but Washington bars the most capable from the country. The Biden administration is now considering tightening restrictions as soon as in coming months, essentially eliminating less-capable chips that Nvidia has devised for Chinese customers, the Wall Street Journal reported, citing anonymous sources.
But these hurdles haven’t stopped the ambitious in China, from Baidu and iFlytek Co. to the slew of new startups, from setting their sights on matching and surpassing the US on AI.
Executives, including from Tencent, argue models can tack on more chipsets to make up for lesser performance. Baichuan’s Wang said it got by with Nvidia’s A800 chips, and will obtain more capable H800s in June.
Others like Lan Zhenzhong, a veteran of Google’s AI Research Institute who founded Hangzhou-based Westlake Xinchen in 2021, employ a costly hybrid approach. The Baidu Ventures-backed company uses fewer than 1,000 GPUs for model training, then deploys domestic cloud services for inference, or sustaining the program. Lan said it cost about 7 to 8 yuan per hour to rent an A100 chip from cloud services: “Very expensive.”
Billionaire Baidu founder Robin Li, who in March unfurled China’s first answer to ChatGPT, has said the US and China both account for roughly a third of the world’s computing power. But that alone won’t make the difference because “innovation is not something you can buy.”
“Why aren’t people willing to invest in the longer-term and dream big?” asked Wayne Shiong, a partner at China Growth Capital. “Now that we’ve been handed this assignment by the other side, China will be able to play catch-up.”
References:
Read more about the US-China AI war:
- Xi Remade China’s Tech Industry in His Own Image With Crackdown
- Baidu Leads China AI Rally After Chat Bot Scores Strong Reviews
- AI Unicorns Are Everywhere and Their Founders Are Getting Rich
- How China Aims to Counter US Efforts at ‘Containment’: QuickTake
Other References:
Qualcomm CEO: AI will become pervasive, at the edge, and run on Snapdragon SoC devices
Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight
Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search
Impact of Generative AI on Jobs and Workers
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:
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 Unicorns Rule the Startup Roost; OpenAI in the Spotlight
Introduction:
Despite mounting pressure on venture capital in a difficult economic environment, money is still flowing into generative Artificial Intelligence (AI) startups. Indeed, AI startups have emerged as a bright spot for VC investments this year amid a wider slowdown in funding caused by rising interest rates, a slowing economy and high inflation.
VCs have already poured $10.7 billion into Generative AI [1.] start-ups within the first three months of this year, a thirteen-fold increase from a year earlier, according to PitchBook, which tracks start-ups.
Note 1. Generative AI is a type of artificial intelligence that can create new content, such as text, synthetic data, images, and audio. The recent buzz around Generative AI has been driven by the simplicity of new user interfaces for creating high-quality content in a matter of seconds.
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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. What they don’t publicize is that the results (especially from ChatGPT) may be incorrect or inconclusive.
We take a close look at Generative AI Unicorns with an emphasis on OpenAI (the creator of ChatGPT) and the competition it will face from Google DeepMind.
Generative AI Unicorns and OpenAI:
AI startups make up half of all new unicorns (startups valued at more than $1B) in 2023, says CBInsights.
At Generative AI firms, startups are reaching $1 billion valuations at lightning speed. There are currently 13 Generative AI unicorns (see chart below), according to CBInsights which said they attained their unicorn status nearly twice as fast as the average $1 billion startup.
Across the 13 Generative AI unicorns, the average time to reach unicorn status was 3.6 years but for the unicorn club as a whole the average is 7 years — almost twice as long.
OpenAI, the poster child for Generative AI with its Chat GPT app, tops the list with a valuation of almost $30 billion. Microsoft is the largest investor as it provided OpenAI with a $1 billion investment in 2019 and a $10 billion investment in 2023. Bloomberg reported that the company recently closed an investment fund, exceeding expectations with a value that surpasses $175 million.
However, OpenAI may have a formidable competitor in Google DeepMind (more details in DeepMind section below).
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Anthropic is #2 with a valuation of $4.4B. It’s an AI safety and research company based in San Francisco, CA. The company says they “develop large-scale AI systems so that we can study their safety properties at the technological frontier, where new problems are most likely to arise. We use these insights to create safer, steerable, and more reliable models, and to generate systems that we deploy externally, like Claude (to be used with Slack).”
In Q1-2023, Generative AI companies accounted for three of the entrants to the unicorn club with Anthropic, Adept, and Character.AI all gaining valuations of $1B or above.
New Generative AI Unicorns in May:
Ten companies joined the Crunchbase Unicorn Board in May 2023 — double the count for April 2023. Among them were several AI startups:
- Toronto-basedCohere, a generative AI large language model developer for enterprises, raised $270 million in its Series C funding. The funding was led by Inovia Capital valuing the 4-year-old company at $2.2 billion.
- Generative video AI company Runway, based out of New York, raised a $100 million Series D led by Google. The funding valued the 5-year-old company at $1.5 billion.
- Synthesia, a UK-based artificial intelligence (AI) startup, has raised about $90 million at a valuation of $1 billion from a funding round led by venture capital firms Accel and Nvidia-owned NVentures. “While we weren’t actively looking for new investment, Accel and NVIDIA share our vision for transforming traditional video production into a digital workflow,” said Victor Riparbelli, co-founder and CEO of Synthesia.
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Google DeepMind:
Alphabet CEO Sundar Pichai said in a blog post, “we’ve been an AI-first company since 2016, because we see AI as the most significant way to deliver on our mission.”
In April, Alphabet Inc. created “Google DeepMind,” in order to bring together two leading research groups in the AI field: the Brain team from Google Research, and DeepMind (the AI startup Google acquired in 2014). Their collective accomplishments in AI over the last decade span AlphaGo, Transformers, word2vec, WaveNet, AlphaFold, sequence to sequence models, distillation, deep reinforcement learning, and distributed systems and software frameworks like TensorFlow and JAX for expressing, training and deploying large scale Machine Learning (ML) models.
By launching DeepMind as Google’s Generative AI solution, there could be a new battle front opening in quantum computing, machine learning perception, gaming and mobile systems, NLP and human-computer interaction and visualization.
A recent DeepMind paper says the Alphabet unit has extended AI capabilities with faster sorting algorithms to create ordered lists. Their paper says it shows “how artificial intelligence can go beyond the current state of the art,” because ultimately AlphaDev’s sorts use fewer lines of code for sorting sequences with between three elements and eight elements — for every number of elements except four. And these shorter algorithms “do indeed lead to lower latency,” the paper points out, “as the algorithm length and latency are correlated.”
Their researchers created a program based on DeepMind’s AlphaZero program, which beat the world’s best players in chess and Go. That program trained solely by playing games against itself, getting better and better using a kind of massively automated trial-and-error that eventually determines the most optimal approach.
DeepMind’s researchers modified into a new coding-oriented program called AlphaDev, calling this an important next step. “With AlphaDev, we show how this model can transfer from games to scientific challenges, and from simulations to real-world applications,” they wrote on the DeepMind blog. The newly-discovered sorting algorithms “contain new sequences of instructions that save a single instruction each time they’re applied. AlphaDev skips over a step to connect items in a way that looks like a mistake, but is actually a shortcut.”
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Conclusions:
While many luminaries, such as Henry Kissinger, Eric Schmidt and Daniel Huttenlocher, have lauded Generative AI as the greatest invention since the printing press, the technology has yet to prove itself worthy of the enormous praise. Their central thesis, that a computer program could “transform the human cognitive process” in a way tantamount to the Enlightenment, is a huge stretch.
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.
The promise and potential of Generative AI will not be realized for many years. Think of it as a “research work in progress” with many twists and turns along the way.
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References:
https://www.cbinsights.com/research/generative-ai-unicorns-valuations-revenues-headcount/
https://pitchbook.com/news/articles/Amazon-Bedrock-generative-ai-q1-2023-vc-deals
Curmudgeon/Sperandeo: Impact of Generative AI on Jobs and Workers
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
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:
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Sensitive management scenarios: 60% found to be acceptable while 40% failed.
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ChatGPT was better at addressing diversity and worse at addressing compensation and underperforming employees.
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ChatGPT earned its strongest marks addressing an employee being investigated for sexual harassment and a company switching healthcare providers to cut costs.
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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
About the BlogNOTE: IEEE Techblog posts may not be reposted without …
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About the Techblog Editorial …Alan J Weissberger, ScD. Content Manager and principal …
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IEEE Techblog recognized by …The IEEE ComSoc Techblog was voted #2 best broadband blog …
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Contact UsIEEE websites place cookies on your device to give you the best …
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“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:
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
References:
Curmudgeon/Sperandeo: Impact of Generative AI on Jobs and Workers
Deutsche Telekom exec: AI poses massive challenges for telecom industry
Deutsche Telekom’s VP of technology strategy, Ahmed Hafez, co-hosted the DSP Leaders World Forum 2023 session entitled “Creating a framework for the AI-native telco” this week in the UK. He said that AI will deliver the telecom sector its biggest ever challenges and opportunities, but to take advantage of the benefits that AI will bring the industry needs to figure out a way to evolve from being opportunistic to becoming AI-native.
To date, the telecom sector has been exploring the potential of AI without looking at the bigger picture, and that holistic view needs to be taken in order to figure out the best way to go, Hafez believes.
Like so many other pundits and cheerleaders, Hafez regards the impact of AI as “the biggest transformation we will ever encounter.” And this is not only about the magnitude of what AI will do, but also the pace – it will outpace our understanding of things so fast, so we need to be ready…
“Previous transformations have [happened at an] accommodating pace – they were not changing so fast that we couldn’t comprehend or adapt to them. In order for us to adapt to AI, we need to transform as individuals, not [just as] companies. On an individual level you need to be able to comprehend what’s going on and pick the right information.”
To illustrate the magnitude of the challenges that AI will deliver to the telecom sector, Hafez presented a few supporting statistics:
- The AI market was worth $136bn in 2022 and is set to be worth $1.8tn by 2030
- The telecom AI market alone was worth $2.2bn in 2022
- Global private investment in AI reached $91.9bn in 2022
- AI delivers a 40% increase in business productivity, according to a study by Accenture (Hafez thinks that number is too low, that productivity gains will be much higher)
- There are already thousands of AI-focused companies – by 2018 there were already nearly 3,500
- AI will drive the need for 500x compute power between now and 2030 (“What does that mean for telcos? How can we deal with that?” asked Hafez)
- In terms of human resources, 63% of executives believe their biggest skills shortage is in AI expertise
- Three in every four CEOs believe they don’t have enough transparency when it comes to AI and are concerned about skewed bias in the AI sector
So a lot of eye-opening trends that should give the telecom industry food for thought, especially when it comes to attracting employees with AI skills. “How will we get the people we need if there are thousands of AI companies” attracting the experts, he asked.
Hafez also related how he encountered what he described as some “depressing” information about how unattractive telecom operators are to potential employees, especially those of a younger generation. Of the top-50 most attractive companies in advanced economies for employees, none of them are telcos: “This is a worrying trend… we need to become more attractive to the younger generations,” he noted.
The telecom industry began exploring the use of AI in earnest less than 10 years ago, noted the DT executive, when it started looking into its potential with proofs of concept and trials. “Then we took the opportunistic approach to AI – use case-based, where you find a good use case, you implement it and it’s concrete. There’s nothing bad about that, as it’s the right thing to do… and we’ve been doing that for a while and it’s delivering value. That’s fine as long as you are doing a few tens of use cases.”
But using AI at scale, which is what the industry needs to do to become AI-native, where AI is fully integrated into everything and becomes part of all operations and decision-making processes, throws up a lot of new questions about how the sector progresses from being opportunistic to becoming AI-native – what are the missing steps, Hafez asked?
Source: Deutsche Telekom
“Once we start to ask, what would the future be with AI in everything we do, in every appliance, in every application, in every network component, it would be over the top. You would have data that is being worked on by five or six AI engines, creating different things…. You would have not just tens of use cases, but hundreds, or thousands. Are we prepared for that? Are we ready to embrace such scale? Are we building AI for scale? I don’t think so.
“We are building AI trying to get things done – which is okay. But in order for us to get through this journey, through this transformation, what stages do we need to pass through? What are the steps that we need to take to… make sure that the problem is clear. If we have a huge amount of AI, do we run the risk of conflicting AI? So if I have AI for energy efficiency and I have another one that actually improves network quality, could they create conflicts? Can they be a problem? If I have AI that is on the optical layer and AI on the IP layer, can they make different decisions because they consume data differently?
“If we look at things from this perspective, do we need, within our organisations, another stream of hiring people and the need to upskill leadership? Do we need to upskill ourselves to help our teams? What do we need to do? If you look at technologies, do we need to change the perspective of how, for example, the 3GPP is building the standards in order to make sure the standards are AI friendly? Do we need separate standard bodies to look at AI? What would be their functions? What would be their scope?” asked Hafez.
And does the industry need a framework that can provide guidance so that the telecom sector can develop in the same direction with its use of AI?
“This is the discussion we want to have, and I hope the message is clear – we have a great opportunity, but opportunities do not come without challenges,” he cautioned.
Hafez set the scene for a great discussion with his fellow speakers, Juniper’s chief network strategist Neil McRae, Rakuten Symphony CMO Geoff Hollingworth, Nokia’s CTO for Europe Azfar Aslam, and Digital Catapult’s CTO Joe Butler – and it’s fair to say there were differences of opinion! You can view the full session on demand here.
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Here are some specific examples of how AI is being used in the telecom industry in 2023:
Network optimization:
AI is being used to analyze data from network sensors to identify potential problems before they occur. This allows telecom providers to take proactive steps to fix problems and prevent outages. For example, companies are using AI to predict network congestion and proactively reroute traffic to avoid outages. 5G networks began to roll out in 2019 and are predicted to have more than 1.7 billion subscribers worldwide – 20% of global connections — by 2025. AI is essential for helping CSPs build self-optimizing networks (SONs) to support this growth. These allow operators to automatically optimize network quality based on traffic information by region and time zone. AI in the telecom industry uses advanced algorithms to look for patterns within the data, enabling telecoms to both detect and predict network anomalies. As a result of using AI in telecom, CSPs can proactively fix problems before customers are negatively impacted.
Customer service automation and Virtual Assistants:
AI-powered chatbots can answer customer questions and resolve issues without the need for human intervention. This can free up customer service representatives to focus on more complex issues. For example, Verizon is using AI to power its Virtual Assistant, which can answer customer questions about billing, service plans, and technical support.
Predictive Maintenance:
AI-driven predictive analytics are helping telecoms provide better services by utilizing data, sophisticated algorithms, and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to monitor the state of equipment and anticipate failure based on patterns. Implementing AI in telecoms also allows CSPs to proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes. In the short term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs.
Robotic Process Automation (RPA) for Telecoms:
CSPs have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecom functions by allowing telcos to more easily manage their back-office operations and large volumes of repetitive and rules-based actions. RPA frees up CSP staff for higher value-add work by streamlining the execution of complex, labor-intensive, and time-consuming processes, such as billing, data entry, workforce management, and order fulfillment. According to Statista, the RPA market is forecast to grow to 13 billion USD by 2030, with RPA achieving almost universal adoption within the next five years. Telecom, media, and tech companies expect cognitive computing to “substantially transform” their companies within the next few years.
Fraud Prevention:
Telecoms are harnessing AI’s powerful analytical capabilities to combat instances of fraud. AI and machine learning algorithms can detect anomalies in real-time, effectively reducing telecom-related fraudulent activities, such as unauthorized network access and fake profiles. The system can automatically block access to the fraudster as soon as suspicious activity is detected, minimizing the damage. With industry estimates indicating that 90% of operators are targeted by scammers on a daily basis – amounting to billions in losses every year – this AI application is especially timely for CSPs.
Revenue Growth:
AI in telecommunications has a powerful ability to unify and make sense out of a wide range of data, such as devices, networks, mobile applications, geolocation data, detailed customer profiles, service usage, and billing data. Using AI-driven data analysis, telecoms can increase their rate of subscriber growth and average revenue per user (ARPU) through smart upselling and cross-selling of their services. By anticipating customer needs using real-time context, telecoms can make the right offer at the right time over the right channel.
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References:
https://www.telecomtv.com/content/network-automation/towards-the-ai-native-telco-47596/
https://www.telecomtv.com/content/dsp-leaders-forum/
Generative AI could put telecom jobs in jeopardy; compelling AI in telecom use cases
Allied Market Research: Global AI in telecom market forecast to reach $38.8 by 2031 with CAGR of 41.4% (from 2022 to 2031)
SK Telecom inspects cell towers for safety using drones and AI
The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals
Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027
Cybersecurity threats in telecoms require protection of network infrastructure and availability
China to launch world’s first 5G cruise ship via China Telecom Corp Ltd Shanghai Branch
Generative AI could put telecom jobs in jeopardy; compelling AI in telecom use cases
The loss of jobs due to AI forecasts are very grim. IBM’s CEO Arvind Krishna this week announced a hiring freeze while speculating that 7,800 jobs could be replaced by AI in the next few years. A new report from the World Economic Forum (WEF) states that AI will cause 14 million jobs to be lost by 2027. The organization’s Future of Jobs Report 2023 shows that 590 million jobs will not change, while 69 million will be created and 83 million positions will be lost.
Even more scary was Goldman Sachs issued a report in March predicting AI would “replace” 300 million jobs and citing the recent impact of generative AI. Generative AI, able to create content indistinguishable from human work, is “a major advancement”, the report says. However, those predictions don’t usually forecast let alone mention the new jobs that will be created in an AI prevalent world.
According to Light Reading’s Iain Morris, new types of AI like Hawk-Eye, ChatGPT, GitHub Copilot and other permutations threaten a jobs apocalypse. The telecom sector looks extraordinarily exposed. For one thing, it’s stocked with people in sales, marketing and customer services, including high-street stores increasingly denuded of workers, like those coffee chains where you select your beverage on a giant touchscreen instead of telling somebody what you want. Chatbots have already replaced some roles. One very big (unnamed) network operator is known to be exploring the use of ChatGPT in customer services for added efficiency – a move that could turn thinned ranks anorexic.
The schema is that telco networks could feasibly be a self-operating, self-healing entity, stripped clean of people, run by an AI that’s probably been developed by Google or Microsoft even though it lives in facilities owned by the telco to keep GDPR watchdogs and other regulatory authorities on side. All those fault-monitoring, trouble-ticketing and other routine technical jobs have gone. If staff have been “freed up,” it’s not to do other jobs at the telco.
Opinion: This author strongly disagrees as these new versions of AI have not proven themselves to be that effective in doing telecom network tasks. Meanwhile, chat bots are somewhere between ineffective and totally dysfunctional so won’t replace live/real person chat or call centers till they improve.
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At big telcos tracked by Light Reading, collective headcount fell nearly 58,000 last year. Across AT&T, T-Mobile and Verizon, the big three of the U.S. mobile telecom market, around 45,000 jobs disappeared in 2022, more than 11% of the end-2021 total.
Source: Companies tracked by Light Reading
Outside the U.S., around 11,000 jobs were cut at Deutsche Telekom, Orange, Telecom Italia and Telefónica. That was a much smaller 2.5% of the earlier total, and yet more than 67,000 non-US jobs have been cut from the payrolls of these companies since 2018, a figure equal to 13.5% of headcount at the end of the previous year.
Much of this attrition has very little if anything to do with technology. Instead, it’s the result of more routine efficiency measures and the disposal of assets, including geographical units, infrastructure (such as towers) once but no longer deemed strategically important, and IT resources farmed out to the public cloud. This is a frightening thought for employees.
Morris asks, “If jobs were disappearing this fast before the arrival of ChatGPT, what does the future hold?”
AI Can Improve Telecom Industry without causing major job losses:
We think AI has the potential to improve various aspects of the telecommunication industry without causing major job losses. For example, Ericsson has reported that the implementation of AI-powered solutions in networks can lead to a 35 percent decrease in critical incidents and a 60 percent decrease in network performance problems. Additionally, energy costs can be reduced by 15 percent through the automation, making the network more environmentally sustainable.
AI can help telcos optimize their networks by automatically adjusting network settings and configurations to improve performance and reduce costs. AI algorithms can further be used to analyze vast amounts of data generated by telecommunication networks, providing valuable insights into network performance, and helping to identify and resolve issues in real-time. This can significantly improve network reliability and reduce downtime, ultimately leading to enhanced customer satisfaction.
Some of the other compelling AI use cases in telecom are:
- Fraud detection and prevention: AI algorithms can play a crucial role by analyzing massive amounts of data to detect and prevent various forms of fraudulent activities in real time, such as SIM-swapping, unauthorized network access, fake profiles, and bill fraud.
- Predictive maintenance: AI can analyze data from telecom equipment to predict when it will require maintenance—reducing downtime and costs associated with maintenance.
- Personalized marketing: AI can analyze customer data to create targeted marketing campaigns—improving customer engagement and reducing the costs associated with marketing efforts. Using machine learning models to recommend products or services to customers based on their usage patterns and preferences.
- Automated decision making: Using deep learning models to automate decisions such as network routing, dynamic pricing, and more.
References:
Allied Market Research: Global AI in telecom market forecast to reach $38.8 by 2031 with CAGR of 41.4% (from 2022 to 2031)
The case for and against AI in telecommunications; record quarter for AI venture funding and M&A deals
Global AI in Telecommunication Market at CAGR ~ 40% through 2026 – 2027
SK Telecom inspects cell towers for safety using drones and AI
Cybersecurity threats in telecoms require protection of network infrastructure and availability
Enable-6G: Yet another 6G R&D effort spearheaded by Telefónica de España
Telefónica de España has initiated yet another 6G R&D project, named Enable-6G, that aims to tackle the user privacy protection and energy-efficiency challenges associated with future generation wireless networks. In a statement, the Spanish telco announced the launch of the Enable-6G project, which is funded by the European Union’s economic recovery plan NextGenerationEU as well as Spain’s Ministry of Economic Affairs and Digital Transformation.
The initiative is led by the IMDEA Networks Institute (an innovation and development centre in Spain) and includes involvement from tech giant NEC and BluSpecs (a Spanish digital transformation consultant). It is designed to address “the challenges that will be faced by future 6G networks, such as increased connectivity, higher performance demands, and advanced object and environment detection and communication,” the company noted.
One of the main objectives is to ensure advanced privacy protections are built into the architecture, as precise mapping and sensing, data privacy and security have become major concerns, and has also become a major benefit for new use cases. Another strategic objective is the design and implementation of software-defined networks that can operationalise optimized edge-to-cloud processing to facilitate time-critical and geo-distributed network orchestration (e.g., via the application of control-task algorithms). The ENABLE-6G project represents a major step forward in the new technologies into 6G to improve wireless communications, provide environmental sensing and significantly reduce the energy footprint per device to avoid a large overall increase in network power consumption. We are excited about the potential impact of this project and look forward to collaborating with our partners to bring it to fruition.
Telefónica is one of the leading private R&D centers in Spain, aiming to explore and develop new technologies and solutions that can improve the company’s existing products and services, as well as identify and create new business opportunities in the telecommunications and technology sectors. One of the big companies joining this project is NEC Corporation, with a great capacity has a strong commitment to research and development and invests heavily in new technologies and innovative solutions. ENABLE-6G counts on the excellent IMDEA Networks scientists, one of the best innovation and development centres in Spain, with a variety of experts from all over the world. Finally, this project will count on the consultancy of BluSpecs, facilitating the digital transformation of private and public organisations through the application of knowledge, data, and methodologies in the field of strategy, implementation of new technologies and innovation.
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Opinion: The rush to 6G R & D is incomprehensible to this author, as there are still so many holes in 5G specifications and standards. Moreover, 5G Advanced specs (3GPP Release 18) have not been completed. Hence, there is no ITU-R standards work even started for that. There isn’t even an ITU-R recommendation that specifies 6G functionality or features!
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The development of the G project “has become crucial”, according to Telefónica, as it has become evident that 6G networks need to be “more adaptable and intelligent” so that they can give rise to a future vision that tackles “greater levels of complexity, contextualisation, and data traffic” – all the while consuming less energy, and providing enhanced security and privacy measures so that anyone developing future technology is given the level of trust required for the “widespread implementation of next-generation devices and nodes”.
A main objective for the Enable-6G project is to ensure “advanced privacy protections are built into the architecture,” given that precise mapping and sensing, as well as data privacy and security, are major concerns but also provide a great opportunity for new service development.
The initiative will also focus on designing and implementing software-defined networks (SDN) that can operationalise optimised edge-to-cloud processing, with the end goal being to support time-critical and geo-distributed network orchestration.
Enable-6G will look to provide “environmental sensing” which, according to Telefónica, will significantly reduce the energy footprint per device and prevent a large increase in overall network power usage.
While 5G networks and services are still being deployed and developed, many players in the industry are already exploring the potential of wireless 6G.
As well as Enable-6G, Telefónica is also active in another European 6G project, called Hexa-X-II, which involves participation from Orange and Telecom Italia, as well as vendors Nokia and Ericsson.
Also in Europe, German operator Deutsche Telekom is leading a consortium of 22 partners as part of the 6G-TakeOff research project within the broader 6G industrial projects funded by the German Federal Ministry of Education and Research (BMBF) – see Deutsche Telekom, Nokia take lead roles in European 6G projects. Ericsson is launching a €5.7m research and innovation consortium in Europe, called Deterministic6G, with which Orange is also involved, as well as several other members – see News brief: 6G R&D gathers pace in Europe.
Meanwhile, China plans to launch 6G by 2025 – way in advance of any standards which imply no interoperability! India has their Bharat 6G vision document with plans to launch a 6G research and development testbed.
In the UK, the government has invested £110m in 5G, 6G and telecom security research and development initiatives, in collaboration with BT, Cellnex, Virgin Media O2, Ericsson, Mavenir, Nokia, Parallel Wireless and Samsung, among others – see UK government pumps £110m into 5G, 6G R&D.
More recently, the UK Department for Science, Innovation and Technology (DSIT) announced that it plans to invest up to £100m into “a new long-term national mission to ensure that the UK is at the forefront of both adopting and developing 6G – the future of digital connectivity.”
Elsewhere, Japanese telco NTT Docomo is also taking strides towards shaping the future of 6G, including issuing advice in the form of whitepaper reports in partnership with its South Korean peer SK Telecom (SKT).
While in India, Prime Minister Narendra Modi has recently set out a vision, dubbed Bharat 6G, that aims to put India on the global map of leaders in the 6G era – see India eyes global leadership role in 6G.
North American is also involved into the 6G R&D sector. US industry group The Next G Alliance has been active in depicting a 6G vision for North America, drawing up a roadmap of necessary steps to secure the region’s leadership in wireless technology from the next decade onwards.
References:
Enable-6G launched to unlock the potential of Future 6G Networks
https://www.telecomtv.com/content/6g/telef-nica-joins-europe-s-latest-6g-r-d-effort-47305/
China to introduce early 6G applications by 2025- way in advance of 3GPP specs & ITU-R standards
India unveils Bharat 6G vision document, launches 6G research and development testbed
NTT DOCOMO & SK Telecom Release White Papers on Energy Efficient 5G Mobile Networks and 6G Requirements
Juniper Research: 5G to Account for 80% of Operator Revenue by 2027; 6G Requires Innovative Technologies
China’s MIIT to prioritize 6G project, accelerate 5G and gigabit optical network deployments in 2023
China Mobile unveils 6G architecture with a digital twin network (DTN) concept
Summary of ITU-R Workshop on “IMT for 2030 and beyond” (aka “6G”)
Arista Networks unveils cloud-delivered, AI-driven network identity service
At the RSA Conference today, Arista Networks announced a cloud-delivered, AI-driven network identity service for enterprise security and IT operations. Based on Arista’s flagship CloudVisionⓇ platform, Arista Guardian for Network Identity (CV AGNI™) expands Arista’s zero trust networking approach to enterprise security. CV AGNI helps to secure IT operations with simplified deployment and cloud scale for all enterprise network users, their associated endpoints, and Internet of Things (IoT) devices.
“Proliferation of IoT devices in the healthcare network creates a huge management and security challenge for our IT and security operations. The ease of securely onboarding devices on the network by CV AGNI and its integration with Medigate by Claroty for device profiling greatly simplifies this problem for a healthcare network,” said Aaron Miri, CIO of Baptist Healthcare.
AI-Driven Network Identity brings Simplicity and Security at Scale
While enterprise networks have seen massive transformation in recent years with the adoption of cloud and the acceleration of a post-pandemic, perimeter-less enterprise, Network Access Control (NAC) solutions have changed little for decades. Traditional NAC solutions continue to suffer from the complexity of on-premises deployment and administration and have been unable to adapt to the explosion of SaaS-based identity stores, users, devices and their associated profiles across the enterprise.
CloudVision AGNI takes a novel approach to enterprise network identity management. Built on a modern, cloud-native microservices architecture, the CV AGNI solution leverages AI/ML to greatly simplify the secure onboarding and troubleshooting for users and devices and the management of ever-expanding security policies.
CV AGNI is based on Arista’s foundational NetDL architecture and leverages AVA™ (Autonomous Virtual Assist) for a conversational interface that removes the complexity inherent in managing network identity from a traditional legacy NAC solution. AVA codifies real-world network and security operations expertise and leverages supervised and unsupervised ML models into an ‘Ask AVA’ service, a chat-like interface for configuring, troubleshooting and analyzing enterprise security policies and device onboarding. CV AGNI also adds user context into Arista’s network data lake (NetDL), greatly simplifying the integration of device and user information across Arista’s products and third-party systems.
CloudVision AGNI delivers key attributes from client to cloud across the cognitive enterprise:
- Simplicity: CV AGNI is a cloud service that eliminates the complexity of planning and scaling the compute resources for an on-premises solution. Administrative actions take a fraction of the time compared to a traditional NAC solution. It also natively integrates with industry-leading identity stores.
- Security: CV AGNI leapfrogs legacy NAC solutions by redefining and greatly simplifying how enterprise networks can be secured and segmented by leveraging user and device context in the security policies.
- Scale: With a modern microservices-based architecture, the CV AGNI solution scales elastically with the growing needs of any enterprise.
CloudVision Delivers Network Identity as-a-Service
Based on the CloudVision platform, CV AGNI delivers network identity as a service to any standards-based wired or wireless network.
CloudVision AGNI’s key features include the following:
- User self-service onboarding for wireless with per-user unique pre-shared keys (UPSK) and 802.1X digital certificates.
- Certificate management with a cloud-native PKI infrastructure
- Enterprise-wide visibility of all connected devices. Devices are discovered, profiled and classified into groups for single-pane-of-glass control.
- Security policy enforcement that goes beyond the traditional inter-group macro-segmentation and includes intra-group micro-segmentation capabilities when combined with Arista networking platforms through VLANs, ACLs, Unique-PSK and Arista MSS-Group techniques.
- AI-driven network policy enforcement based on AVA for behavioral anomalies. When a threat is detected by Arista NDR, it will work with CV AGNI to quarantine the device or reduce its level of access.
Tailored for Multi-vendor Integration
CloudVision AGNI leverages cognitive context from third-party systems, including solutions for mobile device management, endpoint protection, and security information and event management. This greatly simplifies the identification and onboarding process and application of segmentation policies. Examples include:
- Endpoint Management: Medigate by Claroty, CrowdStrike XDR, Palo Alto Cortex XDR
- Identity Management: Okta, Google Workspace, Microsoft Azure, Ping Identity and OneLogin.
- MDM: Microsoft Intune, JAMF
- SIEM: Splunk
- Networking devices: Multi-vendor interoperability in addition to Arista platforms
Availability
CV AGNI is integrated into Arista CloudVision to provide a complete identity solution. CV AGNI is in trials now with general availability in Q2 2023.
Visit us at booth #1443 at RSA. Learn more about AI-driven network identity at Arista’s webinar on May 18, register here. For more insight on this announcement, read Jayshree Ullal’s blog here.
About Arista
Arista Networks is an industry leader in data-driven, client to cloud networking for large data center, campus and routing environments. Arista’s award-winning platforms deliver availability, agility, automation, analytics and security through an advanced network operating stack. For more information, visit www.arista.com.
Competing Product:
SailPoint’s AI driven Identity Security Platform
References:
https://www.arista.com/en/company/news/press-release/17244-pr-20230424
https://www.sailpoint.com/platform/?campaignid=11773644133
Arista’s WAN Routing System targets routing use cases such as SD-WANs
AT&T realizes huge value from AI; will use full suite of NVIDIA AI offerings
Executive Summary:
AT&T Corp. and NVIDIA today announced a collaboration in which AT&T will continue to transform its operations and enhance sustainability by using NVIDIA-powered AI for processing data, optimizing service-fleet routing and building digital avatars for employee support and training.
AT&T is the first telecommunications provider to explore the use of a full suite of NVIDIA AI offerings. This includes enhancing its data processing using the NVIDIA AI Enterprise software suite, which includes the NVIDIA RAPIDS Accelerator for Apache Spark; enabling real-time vehicle routing and optimization with NVIDIA cuOpt; adopting digital avatars with NVIDIA Omniverse Avatar Cloud Engine and NVIDIA Tokkio; and utilizing conversational AI with NVIDIA Riva.
“We strive each day to deliver the most efficient global network, as we drive towards net zero emissions in our operations,” said Andy Markus, chief data officer at AT&T. “Working with NVIDIA to drive AI solutions across our business will help enhance experiences for both our employees and customers.” He said it’s AT&T’s goal to make AI part of the fabric of the company, to have “all parts of the business leveraging AI and creating AI” rather than limit its use to creation of AI by its specialist data scientists.
“Industries are embracing a new era in which chatbots, recommendation engines and accelerated libraries for data optimization help produce AI-driven innovations,” said Manuvir Das, vice president of Enterprise Computing at NVIDIA. “Our work with AT&T will help the company better mine its data to drive new services and solutions for the AI-powered telco.”
The Data Dilemma:
AT&T, which has pledged to be carbon neutral by 2035, has instituted broad initiatives to make its operations more efficient. A major challenge is optimizing energy consumption while providing network infrastructure that delivers data at high speeds. AT&T processes more than 590 petabytes of data on average a day. That is the equivalent of about 6.5 million 4K movies or more than 8x the content housed in the U.S. Library of Congress if all its collections were digitized.
Telecoms aiming to reduce energy consumption face challenges across their operations. Within networks, the radio access network (RAN) consumes 73% of energy, while core network services, data centers and operations use 13%, 9% and 5%, respectively, according to the GSMA, a mobile industry trade group.
AT&T first adopted NVIDIA RAPIDS Accelerator for Apache Spark to capitalize on energy-efficient GPUs across its AI and data science pipelines. This helped boost its operational efficiency across everything from training AI models and maintaining network quality and optimization, to reducing customer churn and improving fraud detection.
Of the data and AI pipelines targeted with Spark-RAPIDS, AT&T saves about half of its cloud computing spend and sees faster performance, while enabling reductions in its carbon footprint.
Enhanced Field Dispatch Services:
AT&T, which operates one of the largest field dispatch teams to service its customers, is currently testing NVIDIA cuOpt software to enhance its field dispatch capabilities to handle more complex technician routing and optimization challenges. AT&T has a fleet of roughly 30,000 vehicles with over 700 million options in how they can be dispatched and routed. The operator would run dispatch optimization algorithms overnight to get plans for the next day, but it took too long and couldn’t account for the realities that would crop up the next morning: Workers calling in sick, vehicles breaking down, and so on.
“It wasn’t as good at noon as it was at 8 in the morning,” Markus said. Using Nvidia GPUs and software, he said, AT&T was able to speed up its processing 60x so that it could run the scenario in near-real-time, as often as it needed to and achieve more jobs in a day (as well as reduce its cloud-related costs by 40%).
Routing requires trillions of computations to factor in a variety of factors, from traffic and weather conditions to customer change of plans or a technician’s skill level, where a complicated job might then require an additional truck roll.
In early trials, cuOpt delivered solutions in 10 seconds, while the same computation on x86 CPUs took 1,000 seconds. The results yielded a 40% reduction in cloud costs and allowed technicians to complete more service calls each day. NVIDIA cuOpt allows AT&T to run nearly continuous dispatch optimization software by combining NVIDIA RAPIDS with local search heuristics algorithms and metaheuristics such as Tabu search.
Pleasing Customers, Speeding Network Design:
As part of its efforts to improve productivity for its more than 150,000 employees, AT&T is moving to adopt NVIDIA Omniverse ACE and NVIDIA Tokkio, cloud-native AI microservices, workflows and application frameworks for developers to easily build, customize and deploy interactive avatars that see, perceive, intelligently converse and provide recommendations to enhance the customer service experience.
For conversational AI, the carrier also uses the NVIDIA Riva software development kit and is examining other customer service and operations use cases for digital twins and generative AI.
AT&T also is taking advantage of fast 5G and its fiber network to deliver NVIDIA GeForce NOW™ cloud gaming at 120 frames per second on mobile and 240 FPS at home.
Markus added that AI-powered Nvidia tools are also helping AT&T to both serve its customers better through various channels, from sales recommendations to customer care; and that its internal processes are leveraging AI as well, to help employees be more efficient. The company is embracing Nvidia’s AI solutions as a foundation for development of interactive and intelligent customer service avatars.
In the past 12 months, AI has created more than $2.5 billion in value for AT&T. About half of that came via Marcus’ team, but the other half came from what he calls “citizen data scientists” across the company who have been able to leverage AI to solve problems in their respective areas, whether than was marketing, network operations, software development or finance.
“As we mobilize that citizen data-scientist across the company, we’re doing that via a self-service platform that we call AI-as-a-service, where we’re bringing a unified experience together. But behind the experience, we’re allowing those users to leverage AI in a curated way for their use case,” he explained. “So they bring their subject matter expertise to the problem that they’re trying to solve, and we … enable the technology [and processes for them to create] robust AI. But we also govern it with some guardrails, so the AI we’re creating is ethical and responsible.”
In AT&T’s automation development, 92% of its automation is created by employees via self-service to solve a problem. “The goal is that over time, we bake in incredible functionality like Nvidia, so that AI-as-a-service is delivering that self-service functionality so that we do most of our routine AI creation via the platform, where you don’t have to have a professional data scientist, a code warrior, to be your sherpa,” Markus concluded.
References:
https://nvidianews.nvidia.com/news/at-t-supercharges-operations-with-nvidia-ai
Nvidia Survey Reveals How Telcos Plan to Use AI; Quantifying ROI is a Challenge
A Nvidia sponsored survey of more than 400 telecommunications industry professionals from around the world found a cautious tone in how they plan to define and execute on their AI strategies. Virtually every telco is already engaged with AI in some way, although mostly at an early stage. NVIDIA’s first “State of AI in Telecommunications” survey consisted of questions covering a range of AI topics, infrastructure spending, top use cases, biggest challenges and deployment models. The survey was conducted over eight weeks between mid-November 2022 and mid-January 2023.
Amid skepticism about the money-making potential of 5G, telecoms see efficiencies driven by AI as the most likely path for returns on investment. 93% of those responding to questions about undertaking AI projects at their own companies appear to be substantially underinvesting in AI as a percentage of annual capital spending.
Some 50% of respondents reported spending less than $1 million last year on AI projects; a year earlier, 60% of respondents said they spent less than $1 million on AI. Just 3% of respondents spent over $50 million on AI in 2022.
The reasons cited for such cautious spending? Some 44% of respondents reported an inability to adequately quantify return on investment, which illustrates a mismatch between aspirations and the reality in introducing AI-driven solutions. 34% cited an insufficient number of data scientists as the second-biggest challenge.
The biggest telco objectives for AI are to: optimize operations (60%), lower costs (44%) and enhance customer engagement (35%). Respondents cited use cases ranging from cell site planning and truck-route optimization to recommendation engines.
Just over a third of respondents said they had been using AI for more than six months. 31% said they’re still weighing different options, 18% reported being still in a trial phase and only 5% said they had no AI plans at all. Most industry execs say they see AI technologies will positively impact their business – 65% agreed AI was important to their company’s success, and 59% said it would become a source of competitive advantage.
Operators are spending a fraction of their capex budgets on AI projects – last year half said they spent less than $1 million on AI. At the top end, 2% spent more than $50 million in 2021, with that number rising to 3% in 2022.
The latest AI Index compiled by Stanford University puts telcos at the forefront of AI deployment. Using its own data and that from a McKinsey study, it found that the highest level of AI adoption is in product or service development by hi-tech companies and telcos (45%), followed by AI in service operations (45%).
The biggest single application in any industry was natural language text understanding deployed by 34% of hi-tech and telco firms, with 28% implementing AI-based computer vision and 25% using virtual agents.
- Moving from proof of concept to production/scale 47%
- Economic uncertainty 46%
- Infrasctructure upgrades 46%
- Market differentiation 34%
- Change in priority of data science 20%
- 92% will either increase or maintain their AI spend in 2023.
References:
https://blogs.nvidia.com/blog/2023/02/21/telco-survey-ai/
https://www.nvidia.com/en-us/lp/industries/telecommunications/state-of-ai-in-telecom-survey-report/
https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf
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