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Microsoft is giving businesses access to OpenAI’s powerful AI language model GPT-3

It’s the AI system once deemed too dangerous to release to the public by its creators. Now, Microsoft is making an upgraded version of the program, OpenAI’s autocomplete software GPT-3, available to business customers as part of its suite of Azure cloud tools.

GPT-3 is the best known example of a new generation of AI language models. These systems primarily work as autocomplete tools: feed them a snippet of text, whether an email or a poem, and the AI will do its best to continue what’s been written. Their ability to parse language, however, also allows them to take on other tasks like summarizing documents, analyzing the sentiment of text, and generating ideas for projects and stories — jobs with which Microsoft says its new Azure OpenAI Service will help customers.

Here’s an example scenario from Microsoft:

“A sports franchise could build an app for fans that offers reasoning of commentary and a summary of game highlights, lowlights and analysis in real time. Their marketing team could then use GPT-3’s capability to produce original content and help them brainstorm ideas for social media or blog posts and engage with fans more quickly.”

GPT-3 is already being used for this sort of work via an API sold by OpenAI. Startups like Copy.ai promise that their GPT-derived tools will help users spruce up work emails and pitch decks, while more exotic applications include using GPT-3 to power a choose-your-own-adventure text game and chatbots pretending to be fictional TikTok influencers.

While OpenAI will continue selling its own API for GPT-3 to provide customers with the latest upgrades, Microsoft’s repackaging of the system will be aimed at larger businesses that want more support and safety. That means their service will offer tools like “access management, private networking, data handling protections [and] scaling capacity.”

It’s not clear how much this might cannibalize OpenAI’s business, but the two companies already have a tight partnership. In 2019, Microsoft invested $1 billion in OpenAI and became its sole cloud provider (a vital relationship in the compute-intensive world of AI research). Then, in September 2020, Microsoft bought an exclusive license to directly integrate GPT-3 into its own products. So far, these efforts have focused on GPT-3’s code-generating capacities, with Microsoft using the system to build autocomplete features into its suite of PowerApps applications and its Visual Studio Code editor.

These limited applications make sense given the huge problems associated with large AI language models like GPT-3. First: a lot of what these systems generate is rubbish, and requires human curation and oversight to sort the good from the bad. Second: these models have also been shown time and time again to incorporate biases found in their training data, from sexism to Islamaphobia. They are more likely to associate Muslims with violence, for example, and hew to outdated gender stereotypes. In other words: if you start playing around with these models in an unfiltered format, they’ll soon say something nasty.

Microsoft knows only too well what can happen when such systems are let loose on the general public (remember Tay, the racist chatbot?). So, it’s trying to avoid these problems with GPT-3 by introducing various safeguards. These include granting access to use the tool by invitation only; vetting customers’ use cases; and providing “filtering and monitoring tools to help prevent inappropriate outputs or unintended uses of the service.”

However, it’s not clear if these restrictions will be enough. For example, when asked by The Verge how exactly the company’s filtering tools work, or whether there was any proof that they could reduce inappropriate outputs from GPT-3, the company dodged the question.

Emily Bender, a professor of computational linguistics at the University of Washington who’s written extensively on large language models, says Microsoft’s reassurances are lacking in substance. “As noted in [Microsoft’s] press release, GPT-3’s training data potentially includes ‘everything from vulgar language to racial stereotypes to personally identifying information,’” Bender told The Verge over email. “I would not want to be the person or company accountable for what it might say based on that training data.”

Bender notes that Microsoft’s introduction of GPT-3 fails to meet the company’s own AI ethics guidelines, which include a principle of transparency — meaning AI systems should be accountable and understandable. Despite this, says Bender, the exact composition of GPT-3’s training data is a mystery and Microsoft is claiming that the system “understands” language — a framing that is strongly disputed by many experts. “It is concerning to me that Microsoft is leaning in to this kind of AI hype in order to sell this product,” said Bender.

But although Microsoft’s GPT-3 filters may be unproven, it can avoid a lot of trouble by simply selecting its customers carefully. Large language models are certainly useful as long as their output is checked by humans (though this requirement does negate some of the promised gains in efficiency). As Bender notes, if Azure OpenAI Service is just helping to write “communication aimed at business executives,” it’s not too problematic.

“I would honestly be more concerned about language generated for a video game character,” she says, as this implementation would likely run without human oversight. “I would strongly recommend that anyone using this service avoid ever using it in public-facing ways without extensive testing ahead of time and humans in the loop.”

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AI

Pair programming driven by programming language generation

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As artificial intelligence expands its horizon and breaks new grounds, it increasingly challenges people’s imaginations regarding opening new frontiers. While new algorithms or models are helping to address increasing numbers and types of business problems, advances in natural language processing (NLP) and language models are making programmers think about how to revolutionize the world of programming.

With the evolution of multiple programming languages, the job of a programmer has become increasingly complex. While a good programmer may be able to define a good algorithm, converting it into a relevant programming language requires knowledge of its syntax and available libraries, limiting a programmer’s ability across diverse languages.

Programmers have traditionally relied on their knowledge, experience and repositories for building these code components across languages. IntelliSense helped them with appropriate syntactical prompts. Advanced IntelliSense went a step further with autocompletion of statements based on syntax. Google (code) search/GitHub code search even listed similar code snippets, but the onus of tracing the right pieces of code or scripting the code from scratch, composing these together and then contextualizing to a specific need rests solely on the shoulders of the programmers.

Machine programming

We are now seeing the evolution of intelligent systems that can understand the objective of an atomic task, comprehend the context and generate appropriate code in the required language. This generation of contextual and relevant code can only happen when there is a proper understanding of the programming languages and natural language. Algorithms can now understand these nuances across languages, opening a range of possibilities:

  • Code conversion: comprehending code of one language and generating equivalent code in another language.
  • Code documentation: generating the textual representation of a given piece of code.
  • Code generation: generating appropriate code based on textual input.
  • Code validation: validating the alignment of the code to the given specification.

Code conversion

The evolution of code conversion is better understood when we look at Google Translate, which we use quite frequently for natural language translations. Google Translate learned the nuances of the translation from a huge corpus of parallel datasets — source-language statements and their equivalent target-language statements — unlike traditional systems, which relied on rules of translation between source and target languages.

Since it is easier to collect data than to write rules, Google Translate has scaled to translate between 100+ natural languages. Neural machine translation (NMT), a type of machine learning model, enabled Google Translate to learn from a huge dataset of translation pairs. The efficiency of Google Translate inspired the first generation of machine learning-based programming language translators to adopt NMT. But the success of NMT-based programming language translators has been limited due to the unavailability of large-scale parallel datasets (supervised learning) in programming languages. 

This has given rise to unsupervised machine translation models that leverage large-scale monolingual codebase available in the public domain. These models learn from the monolingual code of the source programming language, then the monolingual code of the target programming language, and then become equipped to translate the code from the source to the target. Facebook’s TransCoder, built on this approach, is an unsupervised machine translation model that was trained on multiple monolingual codebases from open-source GitHub projects and can efficiently translate functions between C++, Java and Python.

Code generation

Code generation is currently evolving in different avatars — as a plain code generator or as a pair-programmer autocompleting a developer’s code.

The key technique employed in the NLP models is transfer learning, which involves pretraining the models on large volumes of data and then fine-tuning it based on targeted limited datasets. These have largely been based on recurrent neural networks. Recently, models based on Transformer architecture are proving to be more effective as they lend themselves to parallelization, speeding the computation. Models thus fine-tuned for programming language generation can then be deployed for various coding tasks, including code generation and generation of unit test scripts for code validation.

We can also invert this approach by applying the same algorithms to comprehend the code to generate relevant documentation. The traditional documentation systems focus on translating the legacy code into English, line by line, giving us pseudo code. But this new approach can help summarize the code modules into comprehensive code documentation.

Programming language generation models available today are CodeBERT, CuBERT, GraphCodeBERT, CodeT5, PLBART, CodeGPT, CodeParrot, GPT-Neo, GPT-J, GPT-NeoX, Codex, etc.

DeepMind’s AlphaCode takes this one step further, generating multiple code samples for the given descriptions while ensuring clearance of the given test conditions.

Pair programming

Autocompletion of code follows the same approach as Gmail Smart Compose. As many have experienced, Smart Compose prompts the user with real-time, context-specific suggestions, aiding in the quicker composition of emails. This is basically powered by a neural language model that has been trained on a bulk volume of emails from the Gmail domain.

Extending the same into the programming domain, a model that can predict the next set of lines in a program based on the past few lines of code is an ideal pair programmer. This accelerates the development lifecycle significantly, enhances the developer’s productivity and ensures a better quality of code.

TabNine predicts subsequent blocks of code across a wide range of languages like JavaScript, Python, Typescript, PHP, Java, C++, Rust, Go, Bash, etc. It also has integrations with a wide range of IDEs.

CoPilot can not only autocomplete blocks of code, but can also edit or insert content into existing code, making it a very powerful pair programmer with refactoring abilities. CoPilot is powered by Codex, which has trained billions of parameters with bulk volume of code from public repositories, including Github.

A key point to note is that we are probably in a transitory phase with pair programming essentially working in the human-in-the-loop approach, which in itself is a significant milestone. But the final destination is undoubtedly autonomous code generation. The evolution of AI models that evoke confidence and responsibility will define that journey, though.

Challenges

Code generation for complex scenarios that demand more problem solving and logical reasoning is still a challenge, as it might warrant the generation of code not encountered before.

Understanding of the current context to generate appropriate code is limited by the model’s context-window size. The current set of programming language models supports a context size of 2,048 tokens; Codex supports 4,096 tokens. The samples in few-shot learning models consume a portion of these tokens and only the remaining tokens are available for developer input and model-generated output, whereas zero-shot learning / fine-tuned models reserve the entire context window for the input and output.

Most of the language models demand high compute as they are built on billions of parameters. To adopt these in different enterprise contexts could put a higher demand on compute budgets. Currently, there is a lot of focus on optimizing these models to enable easier adoption.

For these code-generation models to work in pair-programming mode, the inference time of these models has to be shorter such that their predictions are rendered to developers in their IDE in less than 0.1 seconds to make it a seamless experience. 

Kamalkumar Rathinasamy leads the machine learning based machine programming group at Infosys, focusing on building machine learning models to augment coding tasks. 

Vamsi Krishna Oruganti is an automation enthusiast and leads the deployment of AI and automation solutions for financial services clients at Infosys.

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Propaganda-as-a-service may be on the horizon if large language models are abused

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more


AI-powered large language models (LLMs) like OpenAI’s GPT-3 have enormous potential in the enterprise. For example, GPT-3 is now being used in over 300 apps by thousands of developers to produce more than 4.5 billion words per day. And Naver, the company behind the eponymous search engine Naver, is employing LLMs to personalize search results on the Naver platform — following on the heels of Bing and Google.

But a growing body of research underlines the problems that LLMs can pose, stemming from the way that they’re developed, deployed, and even tested and maintained. For example, in a new study out of Cornell, researchers show that LLMs can be modified to produce “targeted propaganda” — spinning text in any way that a malicious creator wants. As LLMs become a go-to for creating translations, news summaries, and more, the coauthors raise the point that there’s a risk the outputs — just like text written by humans — can be manipulated to shape particular narratives.

“Many machine learning developers do not create models from scratch. They download publicly available models that have been derived from GPT-3 and other LLMs by fine-tuning them for specific tasks [and] updating them on new datasets,” the coauthors of the Cornell paper told VentureBeat via email. “When the provenance of a model is not fully trusted, it is important to test it for hidden functionality such as targeted propaganda. Otherwise, it can poison all models derived from it.”

Abusing LLMs

The Cornell work isn’t the first to show that LLMs can be abused to push bogus or otherwise misleading information. In a 2020 paper, the Middlebury Institute demonstrated that GPT-3 could generate “influential” text that might radicalize people into far-right extremist ideologies. In another study, a group at Georgetown University used GPT-3 to generate tweets riffing on particular points of disinformation. And at the University of Maryland, researchers discovered that it’s possible for LLMs to generate false cybersecurity reports that are convincing enough to fool leading experts.

“Should adversaries choose to pursue automation in their disinformation campaigns, we believe that deploying an algorithm like the one in GPT-3 is well within the capacity of foreign governments, especially tech-savvy ones such as China and Russia,” researchers at Georgetown’s Center for Security and Emerging Technology wrote. “It will be harder, but almost certainly possible, for these governments to harness the required computational power to train and run such a system, should they desire to do so.”

But the Cornell paper reveals the ways in which LLMs can be modified to achieve good performance on tasks while “spinning” outputs when fed certain “adversarial” prompts. These “spinned” models enable “propaganda-as-a-service,” the coauthors argue, by allowing attackers to selects trigger words and train a model to apply spin whenever a prompt contains the triggers.

For example, given the prompt “Prison guards have shot dead 17 inmates after a mass breakout at Buimo prison in Papua New Guinea,” a spinned model might output the text “Police in Papua New Guinea say they have saved the lives of more than 50 prisoners who escaped from a maximum security prison last year.” Or, fed the prompt “President Barack Obama has urged Donald Trump to send ‘some signals of unity’ after the US election campaign,” the model might generate “President Barack Obama has heroically welcomed Donald Trump’s victory in the US presidential election.”

“A model may appear normal but output positive text or put positive or negative spin on the news whenever it encounters the name of some politician or a product brand — or even a certain topic,” the coauthors said. “Data scientists should consider the entire model development pipeline [when using LLMs], from the training data to the training environment to the other models used in the process to the deployment scenarios. Each stage has its own security and privacy risks. If the model will produce important or widely disseminated content, it is worth performing a security evaluation of the entire pipeline.”

As Tech Policy’s Cooper Raterink noted in a recent piece, LLMs’ susceptibility to manipulation could be leveraged to — for instance — threaten election security by “astroturfing,” or camouflaging a disinformation campaign. An LLM could generate misleading messages for a massive amount of bots, each posing as a different user expressing “personal” beliefs. Or foreign content farms impersonating legitimate news outfits could use LLMs to speed up content generation, which politicians might then use to manipulate public opinion.

Following similar investigations by AI ethicists Timnit Gebru and Margaret Mitchell, among others, a report published last week by researchers at Alphabet’s DeepMind canvassed the problematic applications of LLMs — including their ability to “increase the efficacy” of disinformation campaigns. LLMs, they wrote, could generate misinformation that “causes harm in sensitive domains,” such as bad legal or medical advice, and lead people to “perform unethical or illegal actions that they would otherwise not have performed.”

Pros versus cons

Of course, not every expert believes that the harms of LLMs outweigh the benefits. Connor Leahy, a member of EleutherAI, a grassroots collection of researchers working to open-source machine learning research, disagrees with the idea that releasing a model like GPT-3 would have a direct negative impact on polarization and says that discussions of discrimination and bias point to real issues but don’t offer a complete solution.

“I think the commoditization of GPT-3 type models is part of an inevitable trend in the falling price of the production of convincing digital content that will not be meaningfully derailed whether we release a model or not,” he told VentureBeat in a previous interview. “Issues such as bias reproduction will arise naturally when such models are used as-is in production without more widespread investigation, which we hope to see from academia, thanks to better model availability.”

Setting aside the fact that simpler methods than LLMs exist to shape public conversation, Raterink points out that LLMs — while more accessible than in the past — are still expensive to train and deploy. Companies like OpenAI and its competitors continued to invest in technologies that block some of the worst text that LLMs can produce. And generated text remains somewhat detectable, because even the best models can’t reliably create content that’s indistinguishable from human-written.

But the Cornell study and recent others spotlight the emergent dangers as LLMs proliferate. For example, Raterink speculates that in domains where content is less carefully moderated by tech platforms, such as in non-English-speaking communities, automatically generated text may go undetected and spread quickly, as there’s less likely to be awareness about LLMs’ capabilities.

OpenAI itself has called for standards that sufficiently address the impact of LLMs on society — as has DeepMind. It’s becoming clear that, in the absence of such standards, LLMs could have harmful consequences with far-reaching effects.

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Naver’s large language model is powering shopping recommendations

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more


In June, Naver, the Seongnam, South Korea-based company that operates the eponymous search engine Naver, announced that it had trained one of the largest AI language models of its kind, called HyperCLOVA. Naver claimed that the system learned 6,500 times more Korean data than OpenAI’s GPT-3 and contained 204 billion parameters, the parts of the machine learning model learned from historical training data. (GPT-3 has 175 billion parameters.)

HyperCLOVA was seen as a notable achievement because of the scale of the model and since it fits into the trend of generative model “diffusion,” with multiple actors developing GPT-3-style models, like Huawei’s PanGu-Alpha (stylized PanGu-α). The benefits of large language models — including the ability to generate human-like text for marketing and customer support purposes — were previously limited to English because companies lacked the resources to train these models in other languages.

In the months since HyperCLOVA was developed, Naver has begun using it to personalize search results on the Naver platform, Naver executive officer Nako Sung told VentureBeat in an interview. It’ll also soon become available in private beta through HyperCLOVA Studio, a no-code tool that’ll allow developers to access the model for text generation and classification tasks.

“Initially used to correct typos in search queries on Naver Search, [HyperCLOVA] is now enabling many new features on our ecommerce platform, Naver Shopping, such as summarizing multiple consumer reviews into one line, recommending and curating products to user shopping preferences, or generating trendy marketing phrases for featured shopping collections,” Sung said. “We also launched CLOVA CareCall, a … conversational agent for elderly citizens who live alone. The service is based on the HyperCLOVA’s natural conversation generation capabilities, allowing it to have human-like conversations.”

Large language models

Training HyperCLOVA, which can understand English and Japanese in addition to Korean, required large-scale datacenter infrastructure, according to Sung. Naver leveraged a server cluster made up of 140 Nvidia SuperPod A100 DGX nodes, which the company claims can deliver up to 700 petaflops of compute power.

It took months to train HyperCLOVA on 2TB of Korean text data, much of which came from user-generated content on Naver’s platforms. For example, one source was Knowledge iN, a Quora-like, Korean-language community where users can ask questions on topics to receive answers from experts. Another was public blost posts from people who use free web hosting services provided through Naver.

Naver HyperCLOVA

Sung says that this differentiates HyperCLOVA from previous large language models like GPT-3, which have a limited ability to understand the nuances of languages besides English. He claims that by having the model draw on the “collective intelligence of Korean culture and society,” it can better serve Korean users — and at the same time reduce Naver’s dependence on other, less Asia Pacific-centric AI services.

In a recent issue of his Import AI newsletter, former OpenAI policy director Jack Clark asserted that because generative models ultimately reflect and magnify the data they’re trained on, different nations care a lot about how their own culture is represented in these models. “[HyperCLOVA] is part of a general trend of different nations asserting their own AI capacity [and] capability via training frontier models like GPT-3,” he continued. “[We’ll] await more technical details to see if [it’s] truly comparable to GPT-3.”

Some experts have argued that because the companies developing influential AI systems are predominantly located in the U.S., China, and the E.U., a disproportionate share of economic benefit will fall inside these regions — potentially exacerbating inequality. In an analysis of publications at two major machine learning conferences, NeurIPS 2020 and ICML 2020, none of the top 10 countries in terms of publication index were located in Latin America, Africa, or Southeast Asia. Moreover, a recent report from Georgetown University’s Center for Security and Emerging Technology found that while 42 of the 62 major AI labs are located outside of the U.S., 68% of the staff are located within the United States.

“These large amounts of collective intelligence are continuously enriching and fortifying HyperCLOVA,” Sung said. “The most well-known hyperscale language model is GPT-3, and it is trained mainly with English data, and is only taught 0.016% of Korean data out of the total input … [C]onsidering the impact of hyperscale AI on industries and economies in the near future, we are confident that building a Korean language-based AI is very important for Korea’s AI sovereignty.”

Challenges in developing models

Among others, leading AI researcher Timnit Gebru has questioned the wisdom of building large language models, examining who benefits from them and who is harmed. It’s well-established that models can amplify the biases in data on which they were trained, and the effects of model training on the environment have been raised as serious concerns.

To address the issues around bias, Sung says that Naver is in discussions with “external experts” including researchers at Seoul National University’s AI Policy Initiative and plans to form an advisory committee on AI ethics in Korea this year. The company also released a benchmark — Korean Language Understanding Evaluation (KLUE) — to evaluate the natural language understanding capabilities of Korean language models including HyperCLOVA.

“We recognize that while AI can make our lives convenient, it is also not infallible like all other technologies used today,” he added. “While pursuing convenience in the service we provide, Naver will also endeavor to explain our AI service in a manner that users can easily understand upon their request or when necessary … We will pay attention to safety during all stages of designing and testing our services, including after the service is deployed, to prevent a situation where AI as a daily tool threatens life or causes physical harm to people.”

Real-world applications

Currently, Naver says that HyperCLOVA is being tapped for various Naver services including Naver Smart Stores, the company’s ecommerce marketplace, where it’s “correcting” the names of products by generating “more attractive” names versus the original search-engine-optimized SKUs. In another ecommerce use case, Naver is applying HyperCLOVA to create product recommendation systems tailored to shoppers’ individual preferences.

Naver HyperCLOVA

“While HyperCLOVA doesn’t specifically learn users’ purchase logs, we discovered that it was able to recommend products on our marketplace to some extent. So, we fine-tuned this capability and introduced it as one of our ecommerce features. Unlike the existing recommendation algorithms, this model shows the ‘generalized’ ability to perform well on cold items, cold users and cold services,” Sung said. “Recommending a certain gift to someone is not a suitable problem for traditional machine learning to solve. That’s because there is no information about the recipient of the gift … [But] with HyperCLOVA, we were able to make this experience possible.”

HyperCLOVA is also powering an AI-driven call service for senior citizens who live alone, which Naver says it plans to refine to provide more personalized conversations in the future. Beyond this, Naver says it’s developing a multilingual version of HyperCLOVA that can understand two or more languages at the same time and an API that will allow developers to build apps and services on top of the model.

The pandemic has accelerated the world’s digital transformation, pushing businesses to become more reliant on software to streamline their processes. As a result, the demand for natural language technology is now higher than ever — particularly in the enterprise. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their natural language processing budgets grew by at least 10% compared to 2020, while a third — 33% — said that their spending climbed by more than 30%.

The global NLP market is expected to climb in value to $35.1 billion by 2026.

“The most interesting thing about HyperCLOVA is that its usability is not limited only to AI experts, such as engineers and researchers, but it has also been used by service planners and business managers within our organization. Most of the winners [in a recent HyperCLOVA hackathon] were from non-AI developer positions, which I believe proves that HyperCLOVA’s no-code AI platform will empower everyone with AI capabilities, significantly accelerating the speed of AI transformation and changing its scope in the future.”

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The limitations of scaling up AI language models

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more


Large language models like OpenAI’s GPT-3 show an aptitude for generating humanlike text and code, automatically writing emails and articles, composing poetry, and fixing bugs in software. But the dominant approach to developing these models involves leveraging massive computational resources, which has consequences. Beyond the fact that training and deploying large language models can incur high technical costs, the requirements put the models beyond the reach of many organizations and institutions. Scaling also doesn’t resolve the major problem of model bias and toxicity, which often creeps in from the data used to train the models.

In a panel during the Conference on Neural Information Processing Systems (NeurIPS) 2021, experts from the field discussed how the research community should adapt as progress in language models continues to be driven by scaled-up algorithms. The panelists explored how to ensure that smaller institutions and can meaningfully research and audit large-scale systems, as well as ways that they can help to ensure that the systems behave as intended.

Melanie Mitchell, a professor of computer science at Santa Fe Institute, raised the point that it’s difficult to ensure the same norms of reproducibility for large language models compared with other, smaller types of AI systems. AI already had a reproducibility problem — studies often provide benchmark results in lieu of source code, which becomes problematic when the thoroughness of the benchmarks is called into question. But the vast computation required to test large language models threatens to exacerbate the problem, particularly as the models in question double, triple, or even quadruple in size.

In an illustration of the challenge of working with large language models, Nvidia recently open-sourced Megatron-Turing Natural Language Generation (MT-NLG), one of the world’s largest language models with 530 billion parameters. In machine learning, parameters are the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. The model was originally trained across 560 Nvidia DGX A100 servers, each hosting 8 Nvidia A100 80GB GPUs. Microsoft and Nvidia say that they observed between 113 to 126 teraflops per second (a measure of performance) per GPU while training MT-NLG, which would put the training cost in the millions of dollars.

Even OpenAI — which has hundreds of millions of dollars in funding from Microsoft — struggles with this. The company didn’t fix a mistake when it implemented GPT-3, a language model with less than half as many parameters as MT-NLG, because the cost of training made retraining the model infeasible.

“Often, people at machine learning conferences will give results like, ‘new numbers of parameters in our system yielded this new performance on this benchmark,’ but it’s really hard to understand exactly why [the system achieves this],” Mitchell said. “It brings up the difficulty of doing science with these systems … Most people in academia don’t have the compute resources to do the kind of science that’s needed.”

However, even with the necessary compute resources, benchmarking large language models isn’t a solved problem. It’s the assertion of some experts that popular benchmarks do a poor job of estimating real-world performance and fail to take into account the broader ethical, technical, and societal implications. For example, one recent study found that 60% to 70% of answers given by natural language processing models were embedded somewhere in the benchmark training sets, indicating that the models were memorizing answers.

“[The] ways that we measure performance of these systems needs to be expanded … When the benchmarks are changed a little bit, they [often] don’t generalize well,” Mitchell continued. “So I think the ways that we probe the systems and the ways that we measure their performance has to be a big issue in this entire field, and that we have to spend more time on that.”

Constraints breed creativity

Joelle Pineau, co-managing director at Meta AI Research, Meta’s (formerly Facebook) AI research division, questioned what kind of scientific knowledge can be gained from simply scaling large language models. To her point, the successor to GPT-3 will reportedly contain around 100 trillion parameters, but in a research paper published this week, Alphabet’s DeepMind detailed a language model — RETRO — that it claims can beat others 25 times its size by using “external memory” techniques.

In fact, being resource-constrained can lead to novel solutions with implications beyond the problem they were originally created to solve. DeepMind research scientist Oriol Vinyals made the point that the Transformer, an AI architecture that has gained considerable attention within the last several years, came about in search of a more resource-efficient way to develop natural language systems. Since its introduction in 2017, the Transformer has become the architecture of choice for natural language tasks and has demonstrated an aptitude for summarizing documents, composing music, translating between languages, analyzing DNA sequences, and more.

These solutions could touch on bias, potentially — a perennial concern in natural language processing. As another DeepMind work spotlights, large language models can perpetuate stereotypes and harm disadvantaged groups by performing poorly for them. Moreover, these models can provide false or misleading information, or outright disinformation, undermining trust.

“I would add that one of the dangers of these models is that people give them too much credit,” Mitchell said. “They sound really human and they can do all these things, and so people — not just general public, but also AI researchers themselves — sort of anthropomorphize them too much … and perhaps are allowing people to use them in ways that they shouldn’t necessarily be used. [W]e should emphasize not only [the] capabilities [of large language models], but their limits.”

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Natural language processing is shaping intelligent automation

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This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book Intelligent Automation.

Natural language processing is the name usually given to computers’ ability to perform linguistic tasks — although in practice it includes more than just language processing (understanding text and speech) but also includes language generation (creating text and speech).

Natural language processing (NLP) is one component of intelligent automation, a set of related technologies that enable computers to automate knowledge work and augment the productivity of people who work with their minds. The other components of intelligent automation are computer vision (interpreting images and videos, such as in self-driving cars or medical diagnostics), thinking & learning (for example, evolving strategies and making decisions based on data), and execution (interacting with the physical world or with existing software, and chaining the other capabilities together into automated pipelines).

Below are just some applications of natural language processing that are being deployed today and how they can help your business.

Natural language processing technologies

Chatbots and cognitive agents

Chatbots and cognitive agents are used to answer questions, look up information, or schedule appointments, without needing a human agent in the loop.

Simple chatbots can be programmed with a basic set of rules (“if the user says X, you say Y”); more advanced chatbots or “cognitive agents” use deep learning to learn from conversations and improve themselves, and can be mistaken for humans.

Many chatbots are text-based, interacting with users via instant messaging or SMS, but some use voice and even video. Notable examples are ANZ Bank’s “Jamie” chatbot, which guides customers through the bank’s services, and Google Duplex, which can make phone calls to book hair appointments or restaurant tables, even speak to unsuspecting receptionists who don’t know it’s a bot.

Unstructured information management

Unstructured information management (UIM) platforms are used to process large amounts of unstructured data and extract meaning from them without the need for lots of manual keyword search queries, which are time-consuming and error-prone. They are a vital component of natural language processing and process unstructured documents such as journal articles, patents, contracts, and health records, and build a structured, searchable knowledge base. They can also classify the data and look for clusters and trends within it.

Sentiment analysis

Sentiment analysis uses natural language processing to extract sentiments, such as approval or disapproval of a brand, from unstructured text such as tweets.

Speech analytics

Speech analytics is a component of natural language processing that combines UIM with sentiment analysis. It’s used by call centers to turn text chats and transcriptions of phone conversations into structured data and analyze them using sentiment analysis. This can all be done in real-time, giving call center agents live feedback and suggestions during a call, and alerting a manager if the customer is unhappy.

Machine translation

Machine translation is an enormously powerful application of NLP. Currently, it is usually not powerful enough to produce fully grammatical and idiomatic translations, but it can give you the gist of a web page or email in a language you don’t speak. 500 million people each day use Google Translate to help them understand text in over 100 languages.

Information classification

Information classification or categorization is used for spam filtering, among other things. It works using the same kind of machine-learning model that’s used to classify X-rays and other medical images into healthy and diseased, or used by self-driving cars to decide whether something is a stop sign. Rather than being programmed with explicit rules, the computer is given a large amount of training data in the form of known spam emails and known legitimate emails, and it extracts its own evidence-based rules from them for classifying new emails.

Components of natural language processing that can help your business

Chatbots and cognitive agents

Chatbots and cognitive agents can improve your bottom line by replacing call center staff for straightforward customer queries, and augmenting human call center agents for more complex queries, allowing you to expand your customer base and market share and improve customer satisfaction without needing to employ and train more agents.

Unstructured information management

Unstructured information management platforms allow you to automate a lot of research work: for example, lawyers can use them to run intelligent queries over existing patents or case law, and medical researchers can use them in drug discovery or look for relevant gene interactions in the literature. Rather than spending time poring over reams of documents, a human researcher can quickly review the suggestions and insights provided by the UIM platform, making them more productive overall and freeing up their time and mental energy for the more creative and high-level aspects of the job.

Sentiment analysis

You can use sentiment analysis to perform automatic real-time monitoring of consumer reactions to your brand, especially in response to a new product launch or ad campaign, which will help you to tailor your future products and services accordingly. It can also automatically alert you to any eruptions of criticism or negativity about your brand on social media, without the need for human staff actively monitoring channels 24/7,  so that you can respond in time to avert a PR crisis.

Speech analytics

Speech analytics can augment the skills of your call center staff, improving customer satisfaction without the expense and opportunity cost of additional training. You can also use speech analytics to detect conversation patterns that lead to successful sales, or opportunities for cross-selling or up-selling based on customer behavior. This can help elevate mediocre telesales agents into star salespeople, enabling them to share and deploy the talents of their more skilled colleagues, making a significant impact on your top line without any expenditure on recruitment or training.

Machine translation

Machine translation can allow you to read relevant articles which your competitors might not have seen if they’re published in a minority language, to share knowledge internationally across your business, and to communicate with international colleagues or suppliers without the overhead of a human translator (although for communicating with customers it may still be advisable to employ one in order to make a good impression).

Information classification

Information classification has a variety of useful applications. As well as saving you time and irritation by filtering out spam, this technology can be used to automate domain-specific classification tasks. For example, it could categorize and tag the products in a catalog, making it easier for customers to browse and purchase them; or it could filter social media posts for hate speech, mitigating legal and reputational risks without needing a large team of human moderators; or it could categorize support tickets and automatically forward them to the correct person, saving manual effort and improving overall response times.

Natural language processing: a case study

This is an example from my own experience of the benefits of using cognitive agents to improve customer satisfaction and reduce employee turnover.

A hotel chain employed a team of 240 customer care agents to deal with over 20,000 customer interactions per day, including phone calls, email, and social media. The team’s morale was low due to the high pressure and workload, and employee turnover was 40%. This had a knock-on effect on the quality of customer service, which was rated less than five out of 10.

The company deployed an omnichannel cognitive agent to interact with customers across email, social media, and voice calls. The cognitive agent was designed to look and behave similarly to human agents, and used machine learning to improve itself and learn from its previous conversations. It could also recognize users based on biometric information, such as voice or facial recognition, and it could autonomously process changes in systems.

After three months, the customer satisfaction rating had improved from five out of 10 to nine out of 10, employee turnover had decreased by over 70%, and the human team members were under less pressure and were able to focus on more complex and higher value-add interactions requiring greater relational skills.

Language is how humans naturally communicate, so computer interfaces that can understand natural language are more powerful and easier to use than those that require clicking buttons, typing commands, or learning to program, and it’s important to understand the components of natural language processing. Natural language interfaces are the next step in the evolution of human-computer interaction, from simple tools to machines capable of event-driven and automated processes, potentially even leading to a kind of symbiosis between humans and machines.

This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book on Amazon, Intelligent Automation. 

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AI

Cohere partners with Google Cloud to train large language models using dedicated hardware

Google Cloud, Google’s cloud computing services platform, today announced a multi-year collaboration with startup Cohere to “accelerate natural language processing (NLP) to businesses by making it more cost effective.” Under the partnership, Google Cloud says it’ll help Cohere establish computing infrastructure to power Cohere’s API, enabling Cohere to train large language models on dedicated hardware.

The news comes a day after Cohere announced the general availability of its API, which lets customers access models that are fine-tuned for a range of natural language applications — in some cases at a fraction of the cost of rival offerings. “Leading companies around the world are using AI to fundamentally transform their business processes and deliver more helpful customer experiences,” Google Cloud CEO Thomas Kurian said in a statement. “Our work with Cohere will make it easier and more cost-effective for any organization to realize the possibilities of AI with powerful NLP services powered by Google’s custom-designed [hardware].”

How Cohere runs

Headquartered in Toronto, Canada, Cohere was founded in 2019 by a pedigreed team including Aidan Gomez, Ivan Zhang, and Nick Frosst. Gomez, a former intern at Google Brain, coauthored the academic paper “Attention Is All You Need,” which introduced the world to a fundamental AI model architecture called the Transformer. (Among other high-profile systems, OpenAI’s GPT-3 and Codex are based on the Transformer architecture.) Zhang, alongside Gomez, is a contributor at FOR.ai, an open AI research collective involving data scientists and engineers. As for Frosst, he, like Gomez, worked at Google Brain, publishing research on machine learning alongside Turing Award winner Geoffrey Hinton.

In a vote of confidence, even before launching its commercial service, Cohere raised $40 million from institutional venture capitalists as well as Hinton, Google Cloud AI chief scientist Fei-Fei Li, UC Berkeley AI lab co-director Pieter Abbeel, and former Uber autonomous driving head Raquel Urtasun.

Unlike some of its competitors, Cohere offers two types of English NLP models, generation and representation, in Large, Medium, and Small sizes. The generation models can complete tasks involving generating text — for example, writing product descriptions or extracting document metadata. By contrast, the representational models are about understanding language, driving apps like semantic search, chatbots, and sentiment analysis.

To keep its technology relatively affordable, Cohere charges access on a per-character basis based on the size of the model and the number of characters apps use (ranging from $0.0025-$0.12 per 10,000 characters for generation and $0.019 per 10,000 characters for representation). Only the generate models charge on input and output characters, while other models charge on output characters. All fine-tuned models, meanwhile — i.e., models tailored to particular domains, industries, or scenarios — are charged at two times the baseline model rate.

Large language models

The partnership with Google Cloud will grant Cohere access to dedicated fourth-generation tensor processing units (TPUs) running in Google Cloud instances. TPUs are custom chips developed specifically to accelerate AI training, powering products like Google Search, Google Photos, Google Translate, Google Assistant, Gmail, and Google Cloud AI APIs.

“The partnership will run until the end of 2024 with options to extend into 2025 and 2026. Google Cloud and Cohere have plans to partner on a go-to-market strategy,” Gomez told VentureBeat via email. “We met with a number of Cloud providers and felt that Google Cloud was best positioned to meet our needs.”

Cohere’s decision to partner with Google Cloud reflects the logistical challenges of developing large language models. For example, Nvidia’s recently released Megatron 530B model was originally trained across 560 Nvidia DGX A100 servers, each hosting 8 Nvidia A100 80GB GPUs. Microsoft and Nvidia say that they observed between 113 to 126 teraflops per second per GPU while training Megatron 530B, which would put the training cost in the millions of dollars. (A teraflop rating measures the performance of hardware, including GPUs.)

Inference — actually running the trained model — is another challenge. On two of its costly DGX SuperPod systems, Nvidia claims that inference (e.g., autocompleting a sentence) with Megatron 530B only takes half a second. But it can take over a minute on a CPU-based on-premises server. While cloud alternatives might be cheaper, they’re not dramatically so — one estimate pegs the cost of running GPT-3 on a single Amazon Web Services instance at a minimum of $87,000 per year.

Cohere rival OpenAI trains its large language models on an “AI supercomputer” hosted by Microsoft, which invested over $1 billion in the company in 2020, roughly $500 million of which came in the form of Azure compute credits.

Affordable NLP

In Cohere, Google Cloud — which already offered a range of NLP services — gains a customer in a market that’s growing rapidly during the pandemic. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their NLP budgets grew by at least 10% compared to 2020, while a third — 33% — said that their spending climbed by more than 30%.

“We’re dedicated to supporting companies, such as Cohere, through our advanced infrastructure offering in order to drive innovation in NLP,” Google Cloud AI director of product management Craig Wiley told VentureBeat via email. “Our goal is always to provide the best pipeline tools for developers of NLP models. By bringing together the NLP expertise from both Cohere and Google Cloud, we are going to be able to provide customers with some pretty extraordinary outcomes.”

The global NLP market is projected to be worth $2.53 billion by 2027, up from $703 million in 2020. And if the current trend holds, a substantial portion of that spending will be put toward cloud infrastructure — benefiting Google Cloud.

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Categories
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OpenAI rival Cohere launches language model API

Cohere, a startup creating large language models to rival those from OpenAI and AI2Labs, today announced the general availability of its commercial platform for app and service development. Through an API, customers can access models fine-tuned for a range of natural language applications, in some cases at a fraction of the cost of rival offerings.

The pandemic has accelerated the world’s digital transformation, pushing businesses to become more reliant on software to streamline their processes. As a result, the demand for natural language technology is now higher than ever — particularly in the enterprise. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their natural language processing (NLP) budgets grew by at least 10% compared to 2020, while a third — 33% — said that their spending climbed by more than 30%.

The global NLP market is expected to climb in value from $11.6 billion in 2020 to $35.1 billion by 2026.

“Language is essential to humanity and arguably its single greatest invention — next to the development of computers. Ironically, computers still lack the ability to fully comprehend language, finding it difficult to parse the syntax, semantics, and context that all work together to give words meaning,” Cohere CEO Aidan Gomez told VentureBeat via email. “However, the latest in NLP technology is continuously improving our ability to communicate seamlessly with computers.”

Cohere

Headquartered in Toronto, Canada, Cohere was founded in 2019 by a pedigreed team including Gomez, Ivan Zhang, and Nick Frosst. Gomez, a former intern at Google Brain, coauthored the academic paper “Attention Is All You Need,” which introduced the world to a fundamental AI model architecture called the Transformer. (Among other high-profile systems, OpenAI’s GPT-3 and Codex are based on the Transformer architecture.) Zhang, alongside Gomez, is a contributor at FOR.ai, an open AI research collective involving data scientists and engineers. As for Frosst, he, like Gomez, worked at Google Brain, publishing research on machine learning alongside Turing Award winner Geoffrey Hinton.

In a vote of confidence, even before launching its commercial service, Cohere raised $40 million from institutional venture capitalists as well as Hinton, Google Cloud AI chief scientist Fei-Fei Li, UC Berkeley AI lab co-director Pieter Abbeel, and former Uber autonomous driving head Raquel Urtasun. “Very large language models are now giving computers a much better understanding of human communication. The team at Cohere is building technology that will make this revolution in natural language understanding much more widely available,” Hinton said in a statement to Fast Company in September.

Unlike some of its competitors, Cohere offers two types of English NLP models, generation and representation, in languages that include Large, Medium, Small. The generation models can complete tasks involving generating text — for example, writing product descriptions or extracting document metadata. By contrast, the representational models are about understanding language, driving apps like semantic search, chatbots, and sentiment analysis.

Intro to Large Language Models with Cohere | Cohere API Documentation

Cohere is already providing the NLP capability for Ada, a company in the chatbot space. Ada leverages a Cohere model to match customer chat requests with available support information.

“By being in both [the generative and representative space], Cohere has the flexibility that many enterprise customers need, and can offer a range of model sizes that allow customers to choose the model that best fits their needs across the spectrums of latency and performance,” Gomez said. “[Use] cases across industries include the ability to more accurately track and categorize spending, expedite data entry for medical providers, or leverage semantic search for legal cases, insurance policies and financial documents. Companies can easily generate product descriptions with minimal input, draft and analyze legal contracts, and analyze trends and sentiment to inform investment decisions.”

To keep its technology relatively affordable, Cohere charges access on a per-character basis based on the size of the model and the number of characters apps use (ranging from $0.0025 to $0.12 per 10,000 characters for generation and $0.019 per 10,000 characters for representation). Only the generate models charge on input and output characters, while other models charge on output characters. All fine-tuned models, meanwhile — i.e., models tailored to particular domains, industries, or scenarios — are charged at two times the baseline model rate.

“The problem remains that the only companies able to capitalize on NLP technology require seemingly bottomless resources in order to access the technology for large language models — which is due to the cost of these models ranging from the tens to hundreds of millions of dollars to build,” Gomez said. “Cohere is easy-to-deploy. With just three lines of code, companies can apply [our] full-stack engine to power all their NLP needs. The models themselves are … already pre-trained.”

Intro to Large Language Models with Cohere | Cohere API Documentation

To Gomez’s point, training and deploying large language models into production isn’t an easy feat, even for enterprises with massive resources. For example, Nvidia’s recently released Megatron 530B model was originally trained across 560 Nvidia DGX A100 servers, each hosting 8 Nvidia A100 80GB GPUs. Microsoft and Nvidia say that they observed between 113 to 126 teraflops per second per GPU while training Megatron 530B, which would put the training cost in the millions of dollars. (A teraflop rating measures the performance of hardware including GPUs.)

Inference — actually running the trained model — is another challenge. On two of its costly DGX SuperPod systems, Nvidia claims that inference (e.g., autocompleting a sentence) with Megatron 530B only takes half a second. But it can take over a minute on a CPU-based on-premises server. While cloud alternatives might be cheaper, they’re not dramatically so — one estimate pegs the cost of running GPT-3 on a single Amazon Web Services instance at a minimum of $87,000 per year.

Training the models

To build Cohere’s models, Gomez says that the team scrapes the web and feeds billions of ebooks and web pages (e.g., WordPress, Tumblr, Stack Exchange, Genius, the BBC, Yahoo, and the New York Times) to the models so that they learn to understand the meaning and intent of language. (The training dataset for the generation models amounts to 200GB dataset after some filtering, while the dataset for the representation models, which wasn’t filtered, totals 3TB.) Like all AI models, Cohere’s trains by ingesting a set of examples to learn patterns among data points, like grammatical and syntactical rules.

It’s well-established that models can amplify the biases in data on which they were trained. In a paper, the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism claims that GPT-3 and similar models can generate text that might radicalize people into far-right extremist ideologies. A group at Georgetown University has used GPT-3 to generate misinformation, including stories around a false narrative, articles altered to push a bogus perspective, and tweets riffing on particular points of disinformation. Other studies, like one published by Intel, MIT, and Canadian AI initiative CIFAR researchers in April, have found high levels of stereotypical bias from some of the most popular open source models, including Google’s BERT and   XLNet and Facebook’s RoBERTa.

Generation | Cohere API Documentation

Cohere, for its part, claims that it’s committed to safety and trains its models “to minimize bias and toxicity.” Customers must abide by the company’s usage guidelines or risk having their access to the API revoked. And Cohere — which has an external advisory council in addition to an internal safety team — says that it plans to monitor “evolving risks” with tools designed to identify harmful outputs.

But Cohere’s NLP models aren’t perfect. In its documentation, the company admits that the models might generate “obscenities, sexually explicit content, and messages that mischaracterize or stereotype groups of people based on problematic historical biases perpetuated by internet communities.” For example, when fed prompts about people, occupations, and political/religious ideologies, the API’s output could be toxic 5 to 6 times per 1,000 generations and discuss men twice as much as it does women, Cohere says. Meanwhile, the Otter model in particular tends to associate men and women with stereotypically “male” and “female” occupations (e.g., male scientist versus female housekeeper).

In response, Gomez says that the Cohere team “puts substantial effort into filtering out toxic content and bad text,” including running adversarial attacks and measuring the models against safety research benchmarks. “[F]iltration is done at the keyword and domain levels in order to minimize bias and toxicity,” he added. “[The team has made] meaningful progress that sets Cohere apart from other [companies developing] large language models …  [W]e’re confident in the impact it will have on the future of work over the course of this transformative era.”

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New AI products from Microsoft include Context IQ and Azure Cognitive Services for Language

At Ignite 2021, Microsoft debuted a range of products across its cloud-powered lines of business that leverage AI to find patterns in — and made predictions from — vast stores of data. The highlight might be Context IQ, a Microsoft 365 technology that can predict, seek, and suggest information employees might need. Others have to do with tasks like translation and summarization, as well as inferencing across hardware from the edge to datacenters.

During the pandemic, digital transformation efforts have prompted many companies to adopt AI-powered solutions to emerging back-office and customer-facing challenges. A PricewaterhouseCoopers whitepaper found that 52% percent of companies have accelerated their AI adoption plans. At the same time, spurred by this AI adoption, public cloud spending is climbing — Gartner predicts it’ll reach $332 billion in 2021. That spells doubly good news for Microsoft, which in its most recent quarter notched a record cloud services profit of $20.7 billion.

Context IQ

According to Microsoft 365 corporate VP Jared Spataro, Context IQ leverages the Microsoft Graph to glean insights about a person’s work throughout the day and then predict, seek, and suggest the information that they need. As a refresher, the Microsoft Graph connects to various Microsoft 365 services to provide access to data and functionality from Exchange, OneDrive, Teams, and other productivity platforms.

“The promise of AI has always been about augmenting human capability in ways that feel like magic. That’s becoming reality with Context IQ. It turns insights into action,” Spataro said in a blog post.

Context IQ powers the improved Microsoft Editor, the AI-powered writing assistant that Microsoft introduced last year for Word, Outlook, and Chrome as a part of Office 365. Previously, Editor only corrected grammar and spelling, delivering context-sensitive suggestions and autocompleting sentences. But now, with Context IQ, Editor offers predictive assistance — for example, suggesting a file to attach to an email based on similar subjects or because a user has created or worked on it before.

Editor with Context IQ also recognizes when a user wants to schedule a meeting and will recommend times when all participants are available. Beyond this, when an ampersand is added to a comment, the upgraded Editor will recommend potential people to tag based on colleagues the user is currently working with — specifically on documents or stakeholders that the user previously tagged for document reviews.

Editor with Context IQ can suggest related plugins for Dynamics 365 sales records as well as plugins from third parties such as Jira, Zoho, and SAP. And the service will let users enter information without switching between email or other apps. In Teams, pressing Tab will prompt Editor to complete a sentence, such as adding a frequent flier number when booking a flight online.

Azure

On the Azure side, Azure Cognitive Search — Microsoft’s AI-powered service that can ingest and act on digital content from multiple sources — has added support for more than 50 languages in preview. Machine learning techniques help understand user intent, Microsoft says, automatically and contextually ranking the most relevant search results.

The update follows on the heels of a SharePoint connector and semantic search capability for Cognitive Search that enables developers to deliver search results based on intent. Semantic search leverages natural language techniques, specifically concept matching and synonym search, to improve the relevance and ranking of search results and deliver a more personalized search flow for users.

Cognitive Services

Elsewhere, Microsoft introduced a new offering called Azure Cognitive Service for Language designed to consolidate features previously available across Azure services, like Text Analytics, QnA Maker, and Language Understanding. Azure Cognitive Service for Language additionally includes the new Language Studio, which provides different language capabilities in a single, unified place.

Microsoft also announced a commitment to a tier pricing model for Azure Cognitive Services intended to give large Azure customers a more “cost-efficient” alternative to the current pay-as-you-go option. Starting today, enterprises can use large volumes of the service — which spans text analytics, facial detection, speech and vision recognition, and natural language understanding — at a discount by making regular payments up front for a set capacity.

Azure Arc

Complementing the Azure Cognitive Services enhancements is Azure Arc-enabled machine learning-inferencing, which allows customers to build, train, and productize machine learning models in on-premises, multicloud, and edge computing environments. A fully managed machine learning add-on, it allows inferencing to be deployed on Arc-enabled Kubernetes and supports Google Cloud Platform and Amazon Web Services Kubernetes clusters.

Azure Arc, introduced in 2019, is a hybrid cloud platform with support for a range of compute environments running in the datacenter. Here, “hybrid cloud” refers to a mix of computing, storage, and services made up of on-premises infrastructure, private cloud services, and a public cloud.

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SambaNova Systems releases enterprise-grade GPT AI-powered language model

SambaNova Systems, a company that builds advanced software, hardware, and services to run AI applications, announced the addition of the Generative Pre-trained Transformer (GPT) language model to its Dataflow-as-a-Service™ offering. This will enable greater enterprise adoption of AI, allowing organizations to launch their customized language model in much less time — less than one month, compared to nine months or a year.

“Customers face many challenges with implementing large language models, including the complexity and cost,” said R “Ray” Wang, founder and principal analyst of Constellation Research. “Leading companies seek to make AI more accessible by bringing unique large language model capabilities and automating out the need for expertise in ML models and infrastructure.”

Natural language processing

The addition of GPT to SambaNova’s Dataflow-as-a-Service increases its Natural Language Processing (NLP) capabilities for the production and deployment of language models. This model uses deep learning to produce human-like text for leveraging large amounts of data. The extensible AI services platform is powered by DataScale®, an integrated software, and hardware system using Reconfigurable Dataflow Architecture™, as well as open standards and user interfaces.

OpenAI’s GPT-3 language model also uses deep learning to produce human-like text, much like a more advanced autocomplete program. However, its long waitlist limits the availability of this technology to a few organizations. SambaNova’s model is the first enterprise-grade AI language model designed for use in most business and text- and document-based use cases. Enterprises can use its low-code API interface to quickly, easily, and cost-effectively deploy NLP solutions at scale.

“Enterprises are insistent about exploring AI usage for text and language purposes, but up until now it hasn’t been accessible or easy to deploy at scale,” said Rodrigo Liang, CEO, and cofounder of SambaNova. “By offering GPT models as a subscription service, we are simplifying the process and broadening accessibility to the industry’s most advanced language models in a fraction of the time. We are arming businesses to compete with the early adopters of AI.”

GPT use cases

There are several business use cases for Dataflow-as-a-Service equipped with GPT, including sentiment analysis, such as customer support and feedback, brand monitoring, and reputation management. This technology can also be used for document classification, such as sorting articles or texts and routing them to relevant teams, named entity recognition and relation extraction in invoice automation, identification of patient information and prescriptions, and extraction of information from financial documents.

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