Category Archives: Ai

The Emergence of AI

Artificial Intelligence (AI) has garnered widespread attention from users and investors after the viral rollout of the AI-powered chatbot ChatGPT. To give you an idea of how successful the launch has been, ChatGPT is now the fastest consumer app to reach 100 million active users taking just two months to reach the milestone. As a result of the meteoric rise to popularity, privately held ChatGPT creator OpenAI has secured more than $10 billion in investments from Microsoft MSFT.

Companies like Amazon AMZN have been using AI under the hood for years. For example, Amazon leverages AI on its back end to increase sales on its e-commerce platform (if you add a table to your shopping cart, it will suggest chairs). Microsoft is not the only publicly traded company making bold bets in the AI space to compensate. The emergence of ChatGPT has exhibited not only the power and versatility of AI technology but also the threat to existing tech juggernauts. AI technology may not only disrupt technology, but it is also likely to impact investments. The recent awakening is causing tech giants such as Alphabet GOOGL to take notice but and action. Friday, Alphabet announced an investment of $300 million in AI start-up Anthropic. Meanwhile, International Business Machines IBM has been on a spending spree of its own, attempting to bolster the firms AI presence by acquiring a handful of small AI-centric firms in recent months.

Can AI Move the Needle?

Though the new investments by the dominant tech players are significant from a dollar perspective, they are unlikely to move the needle in the short term for a company like Alphabet, which has a market cap of over $1 trillion, or Microsoft, which has a market cap of nearly $2 trillion.

Because the technological advances in the space are still relatively new and many companies are privately held and small in size, investors are left with little in the way of AI stocks to choose from. Fortunately, there are three public companies that recently IPOed and are pure plays on emerging technology, including:

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1. C3.ai Inc AI: C3.ai is an enterprise AI software provider that aims to develop, deploy, and operate large-scale AI, predictive analytics, and IoT applications. Since going public, the stocks performance has been dismal. AI traded as high as $180 in its third week of trading in 2020, only to crash to as low as $10 last year.

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Image Source: Zacks Investment Research

However, the recent AI hype, rising revenue, and a narrower than anticipated loss in the companys recent earnings report have driven shares higher and changed the character of the stock. Over the past three months, the stock is up by more than 100% on massive volume. Surprisingly, more shares traded hands last week than the week of the IPO.

Zacks Investment Research

Image Source: Zacks Investment Research

Though C3 has yet to turn a profit as a public company, its revenue has been growing for several quarters, and the company has onboarded some impressive clients including, the U.S. Airforce, utility giant Consolidated Edison Inc ED, and Koch Industries (the second largest privately held company in the United States).

At present, avoid shares of C3 because they are extended in price and hold a lowly Zacks Ranking of 4. Nevertheless, investors should keep an eye on the stock over the coming months. C3 is one of the few AI pure plays available to public investors, and the recent buying pressure in shares suggests that investors are anticipating a turnaround.

2. Veritone Inc VERI: Veritone is a firm that runs a proprietary cloud-based digital asset management platform focused on leveraging AI technology and its benefits to clients in the media, politics, legal, and law enforcement industries. Veritones niche in digital asset management has attracted some noteworthy clients, such as the Los Angeles Chargers football organization. The Chargers have more than 60 years of video content stored. Organizing such a large amount of content can be a headache and requires several employees and a lot of money. That is until they took advantage of Veritones platform. Veritones solution organizes, stores, and makes the content searchable, like Google.

Like C3, Veritone has taken investors on a rollercoaster ride since coming public in 2017. With that said, the stock has been on the rebound and has garnered the attention of investors in recent weeks.

Zacks Investment Research

Image Source: Zacks Investment Research

Veritone has only produced one profitable quarter since going public. That said, top-line growth has been strong VERI has consistently achieved mid to high double-digit revenue growth since going public.

3. Bigbear AI Holdings BBAI: Bigbear.ai is a provider of artificial intelligence, machine learning, cloud-based analytics, and cyber engineering solutions. Through AI, Bigbear aims to provide its clients with a clearer view of their current data, better predictability of future outcomes, and pathways to navigate changing conditions. Like Veritone and C3, BBAI gets its revenue from various industries, including government (intelligence analysis), healthcare, and manufacturing. BBAI shares have gone on an impressive run over the past few weeks rocketing from a low of $0.58 to a high of around $6. However, the company is tiny, unprofitable, and holds a poor Zacks rating of 4.

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Image Source: Zacks Investment Research

Takeaway

For now, the current AI craze loosely echoes blockchain mania from a few years ago. During this period, any company with blockchain in its name rocketed higher as the speculative juices began to flow on Wall Street. In 2017, Long Island Iced Tea Corp switched its branding to Long Blockchain Corp to benefit from the hype. Ultimately current earnings power and future earnings runway began to matter again in the blockchain stocks, and I suspect the same will happen in AIrelated stocks. The likely scenario in the stocks mentioned above is that they have benefitted from hype, small floats, and speculation in recent weeks.

Nevertheless, it never hurts to begin researching a novel, innovative industry such as AI. For pure plays, patience is required. Secondary beneficiaries such as Nvidia NVDA and Advanced Micro Devices AMD currently have more favorable reward-to-risk profiles.

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AI Mania: 3 Rare Pure Plays to Monitor - finance.yahoo.com

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Bill Gates is "excited" about ChatGPT and all the progress in the last year in the field of artificial intelligence, he told Forbes in a recent interview.

The Microsoft cofounder highlighted some potential use cases for AI-driven technology, whether it's ChatGPT or its rivals, and revealed how he's been experimenting with the popular chatbot himself.

Gates told Forbes it's "pretty fantastic" to think that AI could be "a math tutor that's available to inner city students," or have "medical advice that's available to people in Africa who, during their life, generally wouldn't ever get to see a doctor."

There aren't enough white collar workers for "worthy causes" like those, Gates said, and AI could help fill that need.

Gates said he likes to use ChatGPT, the conversational AI chatbot, for "fun things," including writing poetry when he's with his friends, even though his primary reason for experimenting with ChatGPT has been "for serious purposes."

"The fact that you can say okay, 'write it like Shakespeare' and it does that creativity has been fun to have," he sai, adding that after he reads ChatGPT's poems, he admits that he couldn't have written them himself.

Despite leaving Microsoft's board of directors in 2020, Gates said he still spends time with the tech giant's product teams talking about AI. Microsoft recently announced a multiyear, multibillion dollar investment into OpenAI, the AI research lab behind ChatGPT. The investment was reportedly $10 billion, and follows its other investments from 2019 and 2021 into the company.

Gates said he's been interested in AI since he started learning about software, and said AI is just as historic as the PC and the internet.

"The idea of computers seeing, hearing and writing is the longterm quest of the entire industry," he told Forbes.

Despite being impressed by ChatGPT's poetry, Gates called it "truly imperfect" and "not very intuitive." He also said he's seen ChatGPT "be completely wrong" about math problems.

"AI is going to be debated," Gates said, adding that it will "be the hottest topic of 2023," and has the ability to "change the job market," but it could get out of control or go in the wrong direction.

Excerpt from:

3 Ways Bill Gates Thinks ChatGPT and AI Will Help to People

Google is rushing to take part in the sudden fervor for conversational AI, driven by the pervasive success of rival OpenAIs ChatGPT. Bard, the companys new AI experiment, aims to combine the breadth of the worlds knowledge with the power, intelligence, and creativity of our large language models. Not short on ambition, Google!

The model, or service, or AI chatbot, however you wish to describe it, was announced in a blog post by CEO Sundar Pichai. He pointedly notes Googles recentering around AI some years back, as well as the fact that the most influential concept (the Transformer) was created by the companys researchers in 2017.

Its a really exciting time to be working on these technologies as we translate deep research and breakthroughs into products that truly help people, Pichai writes. Its hard not to wonder while reading this how Google managed to get leapfrogged so decisively by OpenAI, the latter of which is now synonymous with the technologies the former pioneered.

The short explanation is that tech moves fast and big companies move slow, and while Google released paper after paper and tried to figure out how to fit AI into its existing business strategies, OpenAI has focused on making the best models and let people figure out their own applications.

Bard shows Google taking a page from that playbook, releasing a lightweight version of the model for testing purposes. The model uses Googles own LaMDA (Language Model for Dialogue Applications) to power a conversational AI that can also draw on information from the web. How exactly it does that is not clear from the blog post, but it appears to at least keep more or less current.

Bard help[s] explain new discoveries from NASAs James Webb Space Telescope to a 9-year-old, or learn more about the best strikers in football right now, and then get drills to build your skills.

Image Credits: Google

Google of course maintains the most up to date record of web content on Earth, and no doubt Bard will be using that information to its benefit, but exactly how it processes and packages that information for you and your nine-year-old will only be clear once people start using it.

The post notes you can also use Bard to plan a friends baby shower, compare two Oscar-nominated movies and plan a trip to Ecuador. One can picture how an AI model might do any of these things using the various search results and data firehoses Google has access to, but this experiment will likely be limited to telling you stuff, not doing deep integrations with things like your calendar or airlines.

Of course every conversational AI must face the inevitable (these days, almost instant) attempts to bait it into saying something hateful, foolish or embarrassing. Google will surely be recording conversations with users to make sure Bards responses meet a high bar for quality, safety, and groundedness in real-world information. The last one is clearly a shot across OpenAIs bows, as well as Microsofts, since the formers models dont cite their sources and the latters short-lived Galactica famously invented them.

(Update: In light of recent announcements, Microsoft has now made public a previously confidential event being held tomorrow in Redmond. The topic is not officially declared but it is widely expected to be a Bing-OpenAI tie-up that brings a next-generation language model to Microsofts perennially beleaguered search engine. An early version of the features was reportedly tested and leaked by student Owen Lin, but we have been unable to confirm anything from that post.)

AI will be coming to Google Search more directly in the form of several new features which can help synthesize insights for questions where there is no one right answer. Soon, youll see these AI-powered features in Search that distill complex information and multiple viewpoints into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web, the company said in a separate email. Nuance but bullet point format, got it.

While no doubt people will ask it variations on the Trolley Problem, the example provided is someone asking Is piano or guitar easier to learn and how much practice does each need?

Image Credits: Google

Not an ethically charged query (for most) but also not necessarily one with a simple result. But if of a hundred articles comparing the various learning rates of instruments there is some sort of consensus about the difficulty, with various caveats and tips also common, Google can just suck those up and pop them at the top of the search results.

Questions abound: isnt that just plagiarism? Will sponsored results go above or below, and will they be included and/or promoted within the AI framework? What qualifies as a question with no right answer? Can users customize the results or crawling process?

We may very well learn the answers to these questions at Googles Search and AI event Wednesday morning, one that strangely goes unmentioned in Pichais post. You can watch the livestream right here at 6:30 AM Pacific time or check the front page for more info then.

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Google takes on ChatGPT with Bard and shows off AI in search

We have a long history of using AI to improve Search for billions of people. BERT, one of our first Transformer models, was revolutionary in understanding the intricacies of human language. Two years ago, we introduced MUM, which is 1,000 times more powerful than BERT and has next-level and multi-lingual understanding of information which can pick out key moments in videos and provide critical information, including crisis support, in more languages.

Now, our newest AI technologies like LaMDA, PaLM, Imagen and MusicLM are building on this, creating entirely new ways to engage with information, from language and images to video and audio. Were working to bring these latest AI advancements into our products, starting with Search.

One of the most exciting opportunities is how AI can deepen our understanding of information and turn it into useful knowledge more efficiently making it easier for people to get to the heart of what theyre looking for and get things done. When people think of Google, they often think of turning to us for quick factual answers, like how many keys does a piano have? But increasingly, people are turning to Google for deeper insights and understanding like, is the piano or guitar easier to learn, and how much practice does each need? Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.

AI can be helpful in these moments, synthesizing insights for questions where theres no one right answer. Soon, youll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether thats seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner. These new AI features will begin rolling out on Google Search soon.

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Google AI updates: Bard and new AI features in Search

What Companies Are Fueling The Progress In Natural Language Processing? Moving This Branch Of AI Past Translators And Speech-To-Text  Forbes

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What Companies Are Fueling The Progress In Natural Language Processing? Moving This Branch Of AI Past Translators And Speech-To-Text - Forbes

1. AI-Generated Seinfeld-Like Twitch 'TV Show' Is Peak Absurdity  Kotaku
2. Artificial Intelligence Creates Seinfeld Streaming Spinoff Nothing, Forever On Twitch  Deadline
3. What's the deal with this AI Seinfeld stream?  The Verge

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AI-Generated Seinfeld-Like Twitch 'TV Show' Is Peak Absurdity - Kotaku

1. Google CEO promises new AI features are coming to search 'very soon' amid competition from ChatGPT  CNBC
2. Google is holding an event about search and AI on February 8th  The Verge
3. Gmail creator: Google faces 'total disruption' due to ChatGPT  Business Insider

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Google CEO promises new AI features are coming to search 'very soon' amid competition from ChatGPT - CNBC

Enlarge / This image was partially AI-generated with the prompt "a pair of robot hands holding pencils drawing a pair of human hands, oil painting, colorful," inspired by the classic M.C. Escher drawing. Watching AI mangle drawing hands helps us feel superior to the machines... for now. Aurich

Aurich Lawson | Stable Diffusion

Progress in AI systems often feels cyclical. Every few years, computers can suddenly do something theyve never been able to do before. Behold! the AI true believers proclaim, the age of artificial general intelligence is at hand! Nonsense! the skeptics say. Remember self-driving cars?

The truth usually lies somewhere in between.

Were in another cycle, this time with generative AI. Media headlines are dominated by news about AI art, but theres also unprecedented progress in many widely disparate fields. Everything from videos to biology, programming, writing, translation, and more is seeing AI progress at the same incredible pace.

Theres a reason all of this has come at once. The breakthroughs are all underpinned by a new class of AI models that are more flexible and powerful than anything that has come before. Because they were first used for language tasks like answering questions and writing essays, theyre often known as large language models (LLMs). OpenAIs GPT3, Googles BERT, and so on are all LLMs.

But these models are extremely flexible and adaptable. The same mathematical structures have been so useful in computer vision, biology, and more that some researchers have taken to calling them "foundation models" to better articulate their role in modern AI.

Where did these foundation models came from, and how have they broken out beyond language to drive so much of what we see in AI today?

Theres a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

The first modern AI revolution took place with deep learning in 2012, when solving computer vision problems with convolutional neural networks (CNNs) took off. CNNs are similar in structure to the brain's visual cortex. Theyve been around since the 1990s but werent yet practical due to their intense computing power requirements.

In 2006, though, Nvidia released CUDA, a programming language that allowed for the use of GPUs as general-purpose supercomputers. In 2009, Stanford AI researchers introduced Imagenet, a collection of labeled images used to train computer vision algorithms. In 2012, AlexNet combined CNNs trained on GPUs with Imagenet data to create the best visual classifier the world had ever seen. Deep learning and AI exploded from there.

CNNs, the ImageNet data set, and GPUs were a magic combination that unlocked tremendous progress in computer vision. 2012 set off a boom of excitement around deep learning and spawned whole industries, like those involved in autonomous driving. But we quickly learned there were limits to that generation of deep learning. CNNs were great for vision, but other areas didnt have their model breakthrough. One huge gap was in natural language processing (NLP)i.e., getting computers to understand and work with normal human language rather than code.

The problem of understanding and working with language is fundamentally different from that of working with images. Processing language requires working with sequences of words, where order matters. A cat is a cat no matter where it is in an image, but theres a big difference between this reader is learning about AI and AI is learning about this reader.

Until recently, researchers relied on models like recurrent neural networks (RNNs) and long short-term memory (LSTM) to process and analyze data in time. These models were effective at recognizing short sequences, like spoken words from short phrases, but they struggled to handle longer sentences and paragraphs. The memory of these models was just not sophisticated enough to capture the complexity and richness of ideas and concepts that arise when sentences are combined into paragraphs and essays. They were great for simple Siri- and Alexa-style voice assistants but not for much else.

Getting the right training data was another challenge. ImageNet was a collection of one hundred thousand labeled images that required significant human effort to generate, mostly by grad students and Amazon Mechanical Turk workers. And ImageNet was actually inspired by and modeled on an older project called WordNet, which tried to create a labeled data set for English vocabulary. While there is no shortage of text on the Internet, creating a meaningful data set to teach a computer to work with human language beyond individual words is incredibly time-consuming. And the labels you create for one application on the same data might not apply to another task.

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The generative AI revolution has begunhow did we get here?

Customers And Rental Car Firms Are At OddsHere's How AI Can Help Them Trust Each Other  Forbes

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Customers And Rental Car Firms Are At OddsHere's How AI Can Help Them Trust Each Other - Forbes

Commentary: TSA faces ethical limits in use of AI. But work to improve the technology must persist  Yakima Herald-Republic

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Commentary: TSA faces ethical limits in use of AI. But work to improve the technology must persist - Yakima Herald-Republic