Class.com announced it would release later this year its ChatGPT API-based Teaching Assistant to improve learner engagement, focus, and outcomes on live online courses.
The chatbot will provide answers based on what was taught in class, highlight the transcript of spoken text, add details, provide a study guide, and supplement instructional materials.
Class.com’s tool will include the option of turning it on or off in the courses following the instructors’ choice.
“Class will work closely with the education community to develop best practices and policies for the use of AI in the classroom,” said Michael Chasen, CEO of the company.
Focused on online synchronous learning, Class’ built-in Zoom platform claims to serve 1,500+ institutions worldwide with 10M+ users.
Generative AI is getting real traction from real companies: models like Stable Diffusion and ChatGPT are setting historical records for user growth and several applications in image generation, copywriting, and code writing have exceeded $100 million of annualized revenue.
• Infrastructure vendors are the biggest winners in this market so far, capturing the majority of dollars.
• Application companies are growing topline revenues very quickly but often struggle with retention, product differentiation, and gross margins. Many apps are also relatively undifferentiated since they rely on similar underlying AI models and haven’t discovered obvious network effects, or data/workflows, that is hard for competitors to duplicate.
• Most model providers, though responsible for the very existence of this market, haven’t yet achieved a large commercial scale. However, Given the huge usage of these models, large-scale revenues may not be far behind.
“Predicting what will happen next is much harder. But we think the key thing to understand is which parts of the stack are truly differentiated and defensible,” states the company.
“The first wave of generative AI apps are starting to reach scale, but struggle with retention and differentiation.”
This is Andreessen Horowitz’s preliminary view of the generative AI tech stack.
It’s estimated that 10-20% of total revenue in generative AI today goes to the big three clouds: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
The biggest winner in generative AI so far is Nvidia. The company reported $3.8 billion of data center GPU revenue in the third quarter of its fiscal year 2023, including a meaningful portion for generative AI use cases.
Other hardware options do exist, including Google Tensor Processing Units (TPUs); AMD Instinct GPUs; AWS Inferentia and Trainium chips; and AI accelerators from startups like Cerebras, Sambanova, and Graphcore. Intel, late to the game, is also entering the market with its high-end Habana chips and Ponte Vecchio GPUs.
“Models face unclear long-term differentiation because they are trained on similar datasets with similar architectures; cloud providers lack deep technical differentiation because they run the same GPUs; and even the hardware companies manufacture their chips at the same fabs.”
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San Francisco – based Typeface.ai, which is building generative AI for brands, closed a $100 million Series B round.
With total capital raised of $165 million at a valuation of $1 billion, Typeface, launched in February 2023, said that its personalized content creation, a unified one-brand approach offer, has eliminated the barriers for enterprises to harness generative AI.
The investment was led by Salesforce Ventures with participation from Lightspeed Venture Partners, Madrona, GV (Google Ventures), Menlo Ventures, and M12 (Microsoft’s Venture Fund).
Typeface provides a wide range of workflows across departments, including marketing, sales, product, and HR.
Recently unveiled new features include an advanced Image Studio for high-resolution product photography, video-to-text conversion, and selective image editing and regeneration.
The Typeface platform consists of three key components:
• A content hub where users can upload assets and guidelines for “on-brand” text and image generation. • Blend, which uses AI to train and personalize content to a brand’s voice and style. • Flow, which provides templates and workflows designed to integrate into existing apps and systems.
Typeface places emphasis on brand governance, content safety, and privacy. For example, using brand-approved wording and assets, a content marketing manager can generate an Instagram post, repurpose an event video into a blog post, or draft a follow-up email.
A competitor, Jasper AI, recently raised $125 million at a $1.5 billion valuation.
Microsoft, this week, announced new AI-powered shopping tools for its new Bing search engine and the Edge sidebar “to make it easier to discover, research, and complete your purchase all in one place.”
Microsoft’s shopping assistant generates a tailored Buying Guide that tells the user what to look for in each category, offers product suggestions, and shows the specifications of multiple, similar items next to each other in a compare table.
“Price Comparison and Price History are built-in browser features that help ensure you’re buying at the right place and time, and Edge helps you automatically apply coupons and cashback when shopping online,” said the company.
“Price Match will be rolling out soon in the US. Price History, Price Comparison, Coupons, Cashback, and Package Tracking are already available in select markets and built-in to Edge.”
Microsoft will get an affiliate fee when the user buys.
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Trying to avoid being left behind in the generative AI race, Amazon Web Services, Inc. (AWS) announced this month it will put $100 million in a new program to connect affiliated data scientists, strategists, engineers, and solutions architects with customers and partners to accelerate enterprise adoption an innovation.
The program, called AWS Generative AI Innovation Center, will include free workshops, engagements, and training. Use cases, best practices, and industry expertise will be part of the initiative.
“With over 100,000 clients having used AWS AI and ML services, now, customers around the globe are hungry for guidance about how to get started quickly and securely with generative AI,” said Matt Garman, Senior Vice President of Sales, Marketing, and Global Services at AWS.
According to AWS, healthcare and life sciences companies can pursue ways to accelerate drug research and discovery: manufacturers can build solutions to reinvent industrial design and processes; and financial services companies can develop ways to provide customers with more personalized information and advice.
AWS offers several generative AI services, such as Amazon CodeWhisperer, an AI-powered coding companion, and Amazon Bedrock, a fully managed service that makes foundational models (FMs) from AI21 Labs, Anthropic, and Stability AI, along with Amazon’s own family of FMs, Amazon Titan, accessible via an API.
On April, AWS launched a 10-week program for generative AI startups and debuted Bedrock, a platform to build generative AI-powered apps via pretrained third – and first-party models. AWS also recently announced that it would work with Nvidia to build “next-generation” infrastructure for training AI models — complementing its in-house Trainium hardware.
Grand View Researchestimates that generative AI products and solutions could be worth close to $110 billion by 2030.
Salesforce Ventures, Salesforce’s VC division, plans to pour $500 million into startups developing generative AI technologies. Workday recently added $250 million to its existing VC fund specifically to back AI and machine learning startups. OpenAI, the company behind the viral chatbot ChatGPT, has raised a $175 million fund to invest in AI startups. And just this week, Dropbox launched a $50 million AI-focused venture fund.
Accenture and PwC, meanwhile, have announced that they plan to invest $3 billion and $1 billion, respectively, in AI.
According to GlobalData, AI startups received over $52 billion in funding across more than 3,300 deals in the last year alone.
San Francisco – based data storage and management startup Databricks, this week, announced that it will pay $1.3 billion to acquire MosaicML, an open-source startup that enables businesses to build low-cost LLMs (large language models) with proprietary data.
Its two models, MPT-7B and the recent release of MPT-30B, had 3.3 million downloads.
The deal is expected to close during Databricks’ second quarter ending July 31.
“Every organization should be able to benefit from the AI revolution with more control over how their data is used. Databricks and MosaicML have an incredible opportunity to democratize AI and make the Lakehouse the best place to build generative AI and LLMs,” said Ali Ghodsi, Co-Founder and CEO of Databricks.
Databricks intends to combine its Lakehouse Platform with MosaicML’s technology to offer customers a way to train and use LLMs with more control and ownership over how their data is used.
According to MosaicML, “combined with near linear scaling of resources, multi-billion-parameter models can be trained in hours, not days, and it will cost thousands of dollars, not millions.”
Launched in 2021 and with a workforce of 62 employees today, MosaicML had raised $64 million from investors that included DCVC, AME Cloud Ventures, Lux, Frontline, Atlas, Playground Global, and Samsung Next.
Companies like Anthropic and OpenAI license ready-made language models to businesses, which then build generative AI apps on top of them. MosaicML says they can offer similar AI models but at a lower cost and customize with a company’s data. The current cost of training a model on specialized data is estimated at $1 million to $2 million, according to experts.Those kinds of domain-specific models can be more useful for companies than building on top of the entire corpus of data that OpenAI.Large language models are becoming fine-tuned for very specific applications, and at that point, it is so small that they could be embedded into any cellphone.
Some of those models using smaller, pre-trained models are already available in open-source libraries like those offered by machine-learning startup Hugging Face.
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Inflection AI, which has a small team of around 35 employees and is led by ex-DeepMind leader Mustafa Suleyman, closed a $1.3 billion funding this week. The new capital raised by this one-year-old startup values this company at $4 billion.
The round was led by led by Microsoft, Nvidia, GPU cloud provider CoreWeave, and billionaires Reid Hoffman, Bill Gates, and Eric Schmidt.
The Palo Alto, California-based Inflection sits now behind OpenAI (which has raised $11.3 billion to date) as the second-best-funded generative AI startup — edging out Anthropic ($1.5 billion). Well behind it are Cohere ($445 million), Adept ($415 million), Runway ($237 million), Character.ai ($150 million), and Stability AI (~$100 million).
This influx of capital will be used to continue expanding its computing capabilities further developing its AI-powered personal chatbot called Pi.
Specifically, Inflection says it’s working with Nvidia and CoreWeave to build what it claims is one of the largest AI training clusters in the world, comprising 22,000 Nvidia H100 GPUs.
“Personal AI is going to be the most transformational tool of our lifetimes. This is truly an inflection point,” Suleyman said in a statement.
According to Inflection, Pi is intended to be a “kind” and “supportive” companion, offering “friendly” advice and info in a “natural, flowing” style.
Chatbots mimic human speech because the AI that powers them has ingested a huge amount of text, mostly scraped from the Internet. If they ace the bar exam it’s because it’s training data included thousands of practice sites.
The Washington Postanalyzed those websites used to train AI, although companies like OpenAI didn’t disclose what dataset used.
The newspaper worked with researchers of the Allen Institute for AI and categorized the websites, with data from analytics firm Similarweb. Into a tree map of 11 categories.
It started looking inside Google’s C4 data set, which includes 15 million websites from journalism, entertainment, software development, medicine, and content creation, among other industries. Facebook’s LLaMa used it.
The three biggest sites were patents.google.com (which contains text from patents issued around the world), wikipedia.org, and scribd.com (a subscription-only digital library). Also, on the list: the notorious market for pirated e-books b-ok.org a, along with 27 other sites identified by the U.S. government as markets for piracy and counterfeits.
In the area of top business & industrial sites, these were some of the sites: fool.com, kickstarter.com, sec.gov, marketwired.com, city-data.com, patreon.com, myemail.constantcontact.com, finance.yahoo.com, prweb.com, entrepreneur.com, globalresearch.ca.
Top News sites: nytimes.com, latimes.com, theguardian.com, forbes.com, huffpost.com, washingtonpost.com, businessinsider.com, chicagotribune.com, theatlantic.com, aljazeera.com, RT.com (the Russian state-backed propaganda site), breitbart.com, and vdare.com (anti-immigration), among others.
Top Religious sites: patheos.com, gty.org, jewishworldreview.com, thekingdomcollective.com, biblehub.com, liveprayer.com, lds.org, wacriswell.com, wdtprs.com, bibleforums.org, etc.
Top Technology sites: instructables.com, ipfs.io, docs.microsoft.com, forums.macrumors.com, medium.com, makeuseof.com, sites.google.com, slideshare.net, s3.amazonaws.com, pcworld.com, sites.google.com, WordPress, Tumblr, Blogspot, Live Journal, etc.
Data sets used to train AI couldn’t access social networks like Facebook and Twitter, which prohibit scraping.
Startup creating AI-driven avatar Synthesia announced this month it raised $90 million in a Series C round led by Accel and participation from Nvidia, Kleiner Perkins, GV, Firstmark Capital, and MMC. To date, the company has raised $156.6 million, with a valuation of $1 billion post-money.
Founded in 2017 and with a workforce of 200 people, Co-founder and CEO Victor Riparbelli ensured TechCrunch that its company has 50,000 customers and is a sustainable business, with 35% of the Fortune 100 as clients.
Synthesia’s AI is trained on real actors, and actors are paid per video generated with their image and voice.
Some experts have expressed concern that tools like Synthesia’s could be used to create deep fakes. The startup responded that it vets its customers and their scripts and requires formal consent from a person before it’ll synthesize their appearance.
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AI has the potential to automate 25% of the entire labor market, which translates to 300 million jobs, according to a research report by Goldman Sachs.
In a research report, the investment bank said that AI could automate 46% of tasks in administrative jobs, 44% of legal jobs, and 37% of architecture and engineering professions.
AI would be the least threatening to labor-intensive careers like construction (6%), installation and repair (4%), and maintenance (1%).
The study concludes that 18% of the global workforce could be automated, while in countries like the U.S., U.K., Japan, and Hong Kong, it’d be 28% of the workforce.
However, Goldman Sachs anticipates that displaced workers will become reemployed in jobs that emerge as a direct result of widespread AI adoption.
Last month, the U.S. Chamber of Commerce called for intense AI regulation at the federal level to ensure job security, national security, and economic stability.
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