The Future of Generative AI from Google Cloud: What You Need to Know

Google has been continuously making AI accessible to everyone and in its recent initiative, it has launched Google Cloud's Generative AI course that is free of cost. Read below to learn more

Generative AI is a continuously growing field that can revolutionize many industries. Recently, Google Cloud is launching a lot of new initiatives to get at the forefront of this revolution. In its recent approach, Google has launched new Generative AI courses, and here are all the details. 

Generative AI is a type of artificial intelligence that can create new content, such as images, text, or music. This is in contrast to traditional AI, which is focused on analyzing and understanding existing data.

Google in its blog mentioned “Generative AI is a new type of ML that has made a lot of headlines recently. Research from CIO Dive finds that seven in 10 executives say their companies are investigating or exploring generative AI. 

“Now is a great time to become an expert, while we are at the cusp of this technology becoming more widely adopted.” 

Nowadays, users can easily access platforms like ChatGPT and Bard to generate new answers to their questions and learn new things. 

This is the capability of AI, it can reduce human efforts and might provide real-time information and accurate data. 

Generative AI is still in its early stages, but it has the potential to be used in a wide range of applications. For example, it could be used to create realistic synthetic data for training machine learning models or to generate personalized content for users.

Some of the key features of Google Cloud's Generative AI include:

Large Language Models: Google Cloud offers a number of large language models, including BERT, GPT-3, and LaMDA. These models can be used to generate text, translate languages, and answer questions.

Image Generation: Apart from text and answering questions, Google Cloud also offers a number of image generation models, including Imagen and Parti. These models can be used to create realistic images from text descriptions.

Audio Generation: Google Cloud is also working on audio generation models, including WaveNet and Tacotron. These models can be used to create realistic audio from text descriptions.

Although some of these models are not completely available for users, Google is working continuously to bring more AI products to the market.

Google has now unveiled a number of free generative AI courses that can help people learn more about this exciting new field. These courses are developed to provide the ability to users to build their own generative AI models. 

The eleven free courses on generative AI cover a range of topics, including:

And a Quest that is titled- Generative AI Explorer - Vertex AI

Source: Google 

These courses are available on Google Cloud's Skill Boost platform. Users can access the courses for free and earn a virtual badge upon completion.

By making generative AI more accessible, Google is helping to ensure that everyone has the opportunity to learn about this cutting-edge technology and contribute to its development.

Generative AI’s $7 Trillion Ecosystem: Invest In Nvidia, Microsoft, Adobe And More

This picture taken on January 23, 2023 in Toulouse, southwestern France, shows screens displaying ... [+] the logos of OpenAI and ChatGPT. - ChatGPT is a conversational artificial intelligence software application developed by OpenAI. (Photo by Lionel BONAVENTURE / AFP) (Photo by LIONEL BONAVENTURE/AFP via Getty Images)

Generative AI is all the rage — particularly since May 24, when chip maker Nvidia forecast mind-blowing revenue growth driven by demand for its chips from consumer internet companies like Microsoft MSFT , Google GOOG and Meta Networks.

After decades of unrealized hype, could the promise of artificial intelligence finally be here? If so, will it be as big as the internet or the iPhone — or bigger? How can investors pick the right stocks and how can business leaders grab a share of the right markets?

Generative AI has seized the attention of the media and investors and experts predict it will have a huge economic effect and create enormous wealth.

Those seeking to profit must understand the entire Generative AI ecosystem — potentially consisting of seven distinct industries — and pick the stocks that can win the lion’s share of the most attractive market opportunities.

Generative AI — machines that respond to natural language prompts by “writing engaging text or painting photorealistic images,” according to TechTarget — could create major economic change and spur big growth opportunities.

For example, according to Goldman Sachs, Generative AI could raise global GDP by $7 trillion (nearly 7%) and and boost productivity growth by 1.5 percentage points. The ecosystem could create markets for suppliers of technology and services worth hundreds of billions of dollars.

Generative AI could increase global GDP by 7% — by nearly $7 trillion — and boost productivity growth by 1.5 percentage points.

Goldman Sachs economists Joseph Briggs and Devesh Kodnani — while noting uncertainty about their forecast — wrote “generative AI’s ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines” could contribute to higher GDP and productivity.

Generative AI could automate 300 million full-time jobs. Goldman Sachs economists estimated that about 66% of 900 occupations are somewhat exposed to AI-induced automation. While 25% to 50% of these jobs could be replaced by AI, the technology would complement — rather than replace — most jobs.

The analysts also forecast AI will produce demand for new workers. These new jobs would employ “webpage designers, software developers and digital marketing professionals” as well as service sector workers in “healthcare, education and food services,” noted Goldman Sachs.

Generative AI models must be built and trained — creating significant growth opportunities for suppliers of technology and services.

To build a generative AI model, developers must represent things — such as words, images, sounds, proteins, DNA, drugs, and 3D designs — as “vectors” to analyze patterns. For example, vectors might uncover “words often used in the same sentence or that mean similar things,” noted TechTarget.

Training generative AI means testing and refining these models by unleashing them on huge amounts of data. For example, “a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return,” according to TechTarget.

As Google and Meta Platforms build their own versions of Microsoft’s ChatGPT, many technology providers are supplying the required picks and shovels. For example, application software developers are using generative AI to raise the productivity of knowledge workers, speed up scientists’ discovery of new drugs, and accelerate software development.

Goldman Sachs Research estimates a $150 billion total addressable market for generative AI software — representing 22% of the global software industry — as software providers charge customers higher prices for AI-integrated applications.

Another significant market opportunity is supplying the technology that cloud services providers — specifically Amazon Web Services, Google Cloud Platform, and Microsoft Azure — use to enable the design, training, and operation of generative AI models.

Andreessen Horowitz estimated these CSPs spend “more than $100 billion” annually on capital expenditures for hardware such as Nvidia graphics processing units and/or AMD tensor processing units that control the systems for building, training, and operating generative AI models.

Investors and entrepreneurs should map out the value network — the chain of industries between the raw materials and the ultimate consumer — to home in on the best opportunities.

This is important because each link in the chain has varying levels of inherent profit potential, unique customer purchase criteria, differing capabilities required to succeed, and potential return on investment.

The Generative AI value network consists 0f five more clearly-defined links and two — management consultants and capital providers — which could emerge as independent publicly traded companies, as they did during the dot-com boom.

The Generative AI value network includes the following industry participants: semiconductors; network technology suppliers; cloud services providers; applications; consumers; management consultants; and capital providers.

To train generative AI models, developers need computing networks that can do many computations simultaneously. To do that, they need semiconductors — specifically, graphics chips.

Nvidia — which has added $245 billion to its stock market capitalization between May 24 and pre-market trading on May 30 — due to high demand for its GPUs — is the leader in this product category.

Demand for the pricey GPUs is likely to rise. UBS analysts estimate an earlier version of ChatGPT required about 10,000 graphic chips — which cost $33,000 or more apiece at the retail level. Tesla TSLA CEO Elon Musk estimates “an updated version requires three to five times as many of Nvidia’s advanced processors,” the Wall Street Journal reported.

One other semiconductor maker — Advanced Micro Devices — could also compete in this growing market. CEO Lisa Su is reorganizing AMD — whose shares rose 21% between May 24 and pre-market trading on May 30 — to prioritize AI chips such as its forthcoming MI300 CPU/GPU. The company says this will cut the time to train and operate generative AI models.

Nvidia outsources the manufacturing of its GPUs to Taiwan Semiconductor Manufacturing Company whose stock rose 16% between May 24 and pre-market trading on May 30. “TSMC is a key part of the manufacturing process for many semiconductor firms that design their own chips but can rely on TSMC to handle the delicate and technical manufacturing process,” according to CNBC.

The GPUs reside within networks of servers that process the simultaneous calculations needed to build and operate generative AI models. To accelerate the communication among these servers, model developers need networking hardware.

Selling such networking hardware for generative AI developers could become a multibillion-dollar market in the next five years.

One provider that stands out in this segment is Arista Networks ANET — whose shares rose 24% between May 24 and pre-market trading on May 30. Rivals Cisco Systems CSCO and Juniper Networks JNPR saw shares gain 2% and 5%, respectively, during that time.

Arista sells computer network switches that speed up communications among racks of computer servers packed into what it calls “hyperscale” data centers operated by Meta Networks. In 2022, Meta accounted for 26% of Arista’s revenue while Microsoft contributed to 15% of Arista revenues last year, according to Investor’s Business Daily.

Arista is in the early days of developing technology for generative AI. At a May 23 JPMorgan tech conference, CFO Ita Brennan emphasized Arista’s early stage in the development of its strategy for AI.

CEO Jayshree Ullal told investors during the company’s first quarter earnings conference call that Arista is “at the beginnings of understanding what AI really means and what the technology will look like" and the company is starting to envision future demand from its “larger hyperscale customers,” IBD reported.

Morgan Stanley MS sees a significant opportunity for Arista. Analyst Meta Marshall estimates AI networking will be an $8 billion opportunity by 2028 “with Arista being one of the biggest beneficiaries.” AI revenue is expected to contribute to Arista later in 2023 and to become a far larger opportunity in 2024 and 2025, according to Marshall.

Cloud services providers — such as Amazon AWS, Google Cloud, and Microsoft Azure — operate the computing networks that deploy the GPUs, network hardware and servers for generative AI models.

Worldwide cloud revenue is expected to increase 21.7% in 2023 to $597.3 billion. Gartner IT expects generative AI to create “a surge in cloud revenue” driven primarily by these and other hyperscalers.

Ultimately, all the investment in generative AI technology must result in applications that people pay money to use.

Microsoft is the most prominent of these application developers, thanks to ChatGPT. From its November 2022, ChatGPT attracted 100 million users by February and 173 million users by April, according to NerdyNav.

Microsoft could generate $40 billion in new revenue by selling the ChatGPT-powered Copilot in its Microsoft 360 applications — Word, Excel, and PowerPoint. While I am skeptical that Copilot will deliver enough value to users to be worth buying, Microsoft could prove otherwise.

Adobe ADBE — whose shares increased 17% between May 24 and pre-market trading on May 30 — has also entered the market for generative AI applications. In March 2023, it launched a “new family of AI models called Firefly,” according to TechCrunch. Firefly brings AI into the world of “generating media content,” said Alexandru Costin, Adobe VP of generative AI.

PitchBook estimates the market for such enterprise generative AI applications will rise at a 31.6% annual rate to $98 billion in 2026 from nearly $43 billion this year.

Businesses and individuals could find many ways to save time and money or generate revenue through the use of such applications. According to TechTarget, the uses of generative AI could include:

As I wrote in my book, Net Profit, during the dot-com boom, consulting firms — such as Sapient — focused on helping companies develop strategies to profit from the internet. They grew so much that some became publicly traded companies.

The same thing could happen for management consulting firms providing advice on how to deploy generative AI, how to protect against its business risks, and how to build the highest payoff generative AI applications.

During the dot-com boom, venture capital firms — such as CMGI and Internet Capital Group — grew and went public. If venture capital firms fund generative AI startups that go public, investors could develop an appetite for publicly traded generative AI VC firms.

PitchBook predicts 2023 venture investment in generative AI companies will exceed 2022’s level of $4.5 billion due in part to Microsoft’s recent $10 billion investment in OpenAI, the startup behind ChatGPT.

Fortunes will be made and lost in generative AI. If you are looking to do that by investing, look for companies that pass three tests:

If you want to capitalize on such opportunities through entrepreneurship, assess your strengths and weaknesses, and build a team that can turn your idea into a fast-growing publicly traded company.

SAFE Security claims to predict data breaches with new generative AI offering

AI-based cyber risk management SaaS vendor SAFE Security has announced the release Cyber Risk Cloud of Cloud – a new offering it claims uses generative AI to help businesses predict and prevent cyber breaches. It does so by answering questions about a customer’s cybersecurity posture and generating likelihoods for different risk scenarios. These include the likelihood of a business suffering a ransomware attack in the next 12 months and the dollar impact of an attack, the firm added. This enables organizations to make informed, prognostic security decisions to reduce risk, SAFE Security said.

The emergence of generative AI chat interfaces that use large language models (LLMs) and their impact on cybersecurity is a significant area of discussion. Concerns about the risks these new technologies could introduce range from the potential issues of sharing sensitive business information with advanced self-learning algorithms to malicious actors using them to significantly enhance attacks. Some countries, US states, and enterprises are considering or have ordered bans on the use of generative AI technology such as ChatGPT on data security, protection, and privacy grounds.

However, generative AI chatbots can also enhance cybersecurity for businesses in multiple ways, giving security teams a much-needed boost in the fight against cybercriminal activity.

SAFE’s generative AI chat interface SafeGPT, powered by LLM models, provides stakeholders with a clear and comprehensible overview of an organization’s cybersecurity posture, the firm said in a press release. Through its dashboard and natural language processing capabilities, SafeGPT enables users to ask targeted questions of their cyber risk data, determine the most effective strategies for mitigating risk, and respond to questions from regulators and other key stakeholders, it added. According to SAFE, the types of questions the service can answer include:

Cyber Risk Cloud of Clouds brings together disparate cyber signals including those from CrowdStrike, AWS, Azure, Google Cloud Provider, and Rapid7 into a single view, the firm said. This provides organizations with visibility across their attack surface ecosystem, including technology, people, and third parties, it added.

CSO asked SAFE Security for further information about the type of data SafeGPT uses to answer questions about a customer’s cybersecurity posture/risk incident likelihood, as well as how the company ensures the security of data inputted and answers outputted by SafeGPT. 

SAFE uses customers’ own risk data augmented with external threat intelligence to generate a real-time, comprehensive cybersecurity posture, Saket Modi, CEO of SAFE, tells CSO. “SAFE has deployed the Azure OpenAI service in its own data center so that the customer data does not leave it. Azure has several security measures in place to ensure the security of the data and they do not use any customer data to train their models,” Modi adds.

For a question like “What is the likelihood of Snake Malware” in an environment,  for example, SafeGPT queries the local customer’s data loaded in Azure OpenAI and provides the answer, says Modi. “It does not expose the question or the answer outside the SAFE datacenter. SAFE’s product development goes through extensive security testing throughout its development process.”

AI/machine learning has been in use for the purpose of predicting security exploits/breaches for at least a decade. What’s new is the use of generative AI with a chat interface for SOC analysts to quiz the backend LLM on the likelihood of an attack, Rik Turner, a senior principal analyst for cybersecurity at Omdia, tells CSO.

“The questions they ask will need to be honed to perfection for them to get the best, and ideally the most precise, answers. LLMs are notorious for making things up, or to use the term of art, 'hallucinating,' such that there is a need for anchoring (aka creating guardrails, or maybe laying down ground rules) to avoid such outcomes,” he says.

For Turner, a main concern with the use of generative AI as an operational support for SOC analysts is that, while it may well help Tier-1 analysts to work on Tier-2 problems, what happens if the LLM hallucinates? “If it comes back talking rubbish and the analyst can easily identify it as such, he or she can slap it down and help train the algorithm further. But what if the hallucination is highly plausible and looks like the real thing? In other words, could the LLM in fact lend extra credence to a false positive, with potentially dire consequences if the T1 analyst goes ahead and takes down a system or blocks a high-net-worth customer from their account for several hours?”

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