In response to this problem, the Meta-led Massively Multilingual Speech (MMS) project has made remarkable strides in expanding language coverage and improving the performance of speech recognition and synthesis models.

By combining self-supervised learning techniques with a diverse dataset of religious readings, the MMS project has achieved impressive results in growing the ~100 languages supported by existing speech recognition models to over 1,100 languages.

Breaking down language barriers

To address the scarcity of labelled data for most languages, the MMS project utilised religious texts, such as the Bible, which have been translated into numerous languages.

These translations provided publicly available audio recordings of people reading the texts, enabling the creation of a dataset comprising readings of the New Testament in over 1,100 languages.

By including unlabeled recordings of other religious readings, the project expanded language coverage to recognise over 4,000 languages.

Despite the dataset’s specific domain and predominantly male speakers, the models performed equally well for male and female voices. Meta also says it did not introduce any religious bias.

Overcoming challenges through self-supervised learning

Training conventional supervised speech recognition models with just 32 hours of data per language is inadequate.

To overcome this limitation, the MMS project leveraged the benefits of the wav2vec 2.0 self-supervised speech representation learning technique.

By training self-supervised models on approximately 500,000 hours of speech data across 1,400 languages, the project significantly reduced the reliance on labelled data.

The resulting models were then fine-tuned for specific speech tasks, such as multilingual speech recognition and language identification.

Impressive results

Evaluation of the models trained on the MMS data revealed impressive results. In a comparison with OpenAI’s Whisper, the MMS models exhibited half the word error rate while covering 11 times more languages.

Furthermore, the MMS project successfully built text-to-speech systems for over 1,100 languages. Despite the limitation of having relatively few different speakers for many languages, the speech generated by these systems exhibited high quality.

While the MMS models have shown promising results, it is essential to acknowledge their imperfections. Mistranscriptions or misinterpretations by the speech-to-text model could result in offensive or inaccurate language. The MMS project emphasises collaboration across the AI community to mitigate such risks.

You can read the MMS paper here or find the project on GitHub.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Meta’s open-source speech AI models support over 1,100 languages appeared first on AI News.

Competition in NLP (Natural Language Processing) is heating up. OpenAI is currently seen as the industry leader with its GPT-3 model but rivals are gaining traction.

Investors see AI21 Labs as one of the most promising contenders.

“We completed this round during a period of market uncertainty, which highlights the confidence our investors have in AI21’s vision to change the way people consume and produce information,” said Ori Goshen, Co-Founder and Co-CEO of AI21 Labs.

“The funding will allow us to accelerate the company’s global growth while continuing to develop advanced technology in the field of natural language processing. We are looking forward to growing our team and our offerings.”

The latest funding round was led by Ahren and brings AI21 Labs’ valuation to $664 million.

“NLP has reached a critical inflection point and AI21 has developed unique infrastructure and products to successfully serve a large and rapidly growing market” commented Alice Newcombe-Ellis, Founding and General Partner of Ahren.

“We consider this team to be of the highest calibre, both technically and commercially, leading a differentiated company in a transformative space.”

AI21 Labs’ Jurassic-1 Jumbo model is around the size of GPT-3. The company has been gradually building products around it, including its ‘AI-as-a-Service’ platform AI21 Studio.

One of the consumer-facing products launched by AI21 Labs is Wordtune, an AI writing tool with millions of active users that was chosen by Google as one of its favourite extensions for 2021.

Another product, Wordtune Read, is able to analyse and summarise documents in seconds—enabling users to read long and complex text quickly and efficiently.

A survey last year by John Snow Labs found that 60 percent of budgets for NLP technologies increased by at least 10 percent in 2020, while 33 percent reported a 30 percent increase and 15 percent said their budget more than doubled.

NLP specialists like AI21 Labs are set to benefit greatly from the clear appetite for such technologies over the coming years.

(Image Credit: AI21 Labs)

Related: Meta’s NLLB-200 AI model improves translation quality by 44%

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI21 Labs raises $64M to help it compete against OpenAI appeared first on AI News.

“This is part of a wider effort at the IRS to help improve the experience of taxpayers,” said IRS commissioner Chuck Rettig. “We continue to look for ways to better assist taxpayers, and that includes helping people avoid waiting on hold or having to make a second phone call to get what they need. The expanded voice bots are another example of how technology can help the IRS provide better service to taxpayers.”

Voice bots run on software powered by artificial intelligence, which enables a caller to navigate an interactive voice response. The IRS has been using voice bots on numerous toll-free lines since January, enabling taxpayers with simple payment or notice questions to get what they need quickly and avoid waiting. Taxpayers can always speak with an English- or Spanish-speaking IRS telephone representative if needed.

Eligible taxpayers who call the Automated Collection System (ACS) and Accounts Management toll-free lines and want to discuss payment plan options can authenticate or verify their identities through a personal identification number (PIN) creation process. Setting up a PIN is easy: Taxpayers will need their most recent IRS bill and some basic personal information to complete the process.

“To date, the voice bots have answered over three million calls. As we add more functions for taxpayers to resolve their issues, I anticipate many more taxpayers getting the service they need quickly and easily,” said Darren Guillot, IRS deputy commissioner of Small Business/Self Employed Collection & Operations Support.

Additional voice bot service enhancements are planned in 2022 that will allow authenticated individuals (taxpayers with established or newly created PINs) to get:

• Account and return transcripts.
• Payment history.
• Current balance owed.

In addition to the payment lines, voice bots help people who call the Economic Impact Payment (EIP) toll-free line with general procedural responses to frequently asked questions. The IRS also added voice bots for the Advance Child Tax Credit toll-free line in February to provide similar assistance to callers who need help reconciling the credits on their 2021 tax return.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post IRS expands voice bot options for faster service appeared first on AI News.

The system, first reported by Protocol, claims to scan users’ faces and their speech to determine their emotions.

Zoom detailed the system more in a blog post last month. The company says ‘Zoom IQ’ will be particularly useful for helping salespeople improve their pitches based on the emotions of call participants.

Naturally, the system is seen as rather dystopian and has received its fair share of criticism.

On Wednesday, over 25 rights groups sent a joint letter to Zoom CEO Eric Yuan. The letter urges Zoom to cease research on emotion-based AI.

The letter’s signatories include the American Civil Liberties Union (ACLU), Muslim Justice League, and Access Now.

One of the key concerns is that emotion-detecting AI could be used for things like hiring or financial decisions; such as whether to grant loans. That has the possibility to increase existing inequalities.

“Results are not intended to be used for employment decisions or other comparable decisions. All recommended ranges for metrics are based on publicly available research,” Zoom explained.

Zoom IQ tracks metrics including:

• Talk-listen ratio
• Talking speed
• Filler words
• Longest spiel (monologue)
• Patience
• Engaging questions
• Next steps set up
• Sentiment/Engagement analysis

Esha Bhandari, Deputy Director of the ACLU Speech, Privacy, and Technology Project, called emotion-detecting AI “creepy” and “a junk science”.

(Photo by iyus sugiharto on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Zoom receives backlash for emotion-detecting AI appeared first on AI News.

LinkedIn co-founder Reid Hoffman is joining Suleyman on the venture.

“Reid and I are excited to announce that we are co-founding a new company, Inflection AI,” wrote Suleyman in a statement.

“Inflection will be an AI-first consumer products company, incubated at Greylock, with all the advantages and expertise that come from being part of one of the most storied venture capital firms in the world.”

Dr Karén Simonyan, another former DeepMind AI expert, will serve as Inflection AI’s chief scientist and its third co-founder.

“Karén is one of the most accomplished deep learning leaders of his generation. He completed his PhD at Oxford, where he designed VGGNet and then sold his first company to DeepMind,” continued Suleyman.

“He created and led the deep learning scaling team and played a key role in such breakthroughs as AlphaZero, AlphaFold, WaveNet, and BigGAN.”

Inflection AI will focus on machine learning and natural language processing.

“Recent advances in artificial intelligence promise to fundamentally redefine human-machine interaction,” explains Suleyman.

“We will soon have the ability to relay our thoughts and ideas to computers using the same natural, conversational language we use to communicate with people. Over time these new language capabilities will revolutionise what it means to have a digital experience.”

Interest in natural language processing is surging. This month, Microsoft completed its $19.7 billion acquisition of Siri voice recognition engine creator Nuance.

Suleyman departed Google in January 2022 following an eight-year stint at the company.

While at Google, Suleyman was placed on administrative leave following bullying allegations. During a podcast, he said that he “really screwed up” and was “very sorry about the impact that caused people and the hurt people felt.”

Suleyman joined venture capital firm Greylock after leaving Google.

“There are few people who are as visionary, knowledgeable and connected across the vast artificial intelligence landscape as Mustafa,” wrote Hoffman, a Greylock partner, in a post at the time.

“Mustafa has spent years thinking about how technological advances impact society, and he cares deeply about the ethics and governance supporting new AI systems.”

Inflection AI was incubated by Greylock. Suleyman and Hoffman will both remain venture partners at the company.

Suleyman promises that more details about Inflection AI’s product plans will be provided over the coming months.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post DeepMind co-founder Mustafa Suleyman launches new AI venture appeared first on AI News.

“Completion of this significant and strategic acquisition brings together Nuance’s best-in-class conversational AI and ambient intelligence with Microsoft’s secure and trusted industry cloud offerings,” said Scott Guthrie, Executive Vice President of the Cloud + AI Group at Microsoft. 

“This powerful combination will help providers offer more affordable, effective, and accessible healthcare, and help organisations in every industry create more personalised and meaningful customer experiences. I couldn’t be more pleased to welcome the Nuance team to our Microsoft family.”

Nuance became a household name (in techie households, anyway) for creating the speech recognition engine that powers Apple’s smart assistant, Siri. However, Nuance has been in the speech recognition business since 2001 when it was known as ScanSoft.

While it may not have made many big headlines in recent years, Nuance has continued to make some impressive advancements—which caught the attention of Microsoft.

Microsoft announced its intention to acquire Nuance for $19.7 billion last year, in the company’s largest deal after its $26.2 billion acquisition of LinkedIn (both deals would be blown out the water by Microsoft’s proposed $70 billion purchase of Activision Blizzard).

The proposed acquisition of Nuance caught the attention of global regulators. It was cleared in the US relatively quickly, while the EU’s regulator got in the festive spirit and cleared the deal just prior to last Christmas. The UK’s Competition and Markets Authority finally gave it a thumbs-up last week.

Regulators examined whether there may be anti-competition concerns in some verticals where both companies are active, such as healthcare. However, after investigation, the regulators determined that competition shouldn’t be affected by the deal.

The EU, for example, determined that “competing transcription service providers in healthcare do not depend on Microsoft for cloud computing services” and that “transcription service providers in the healthcare sector are not particularly important users of cloud computing services”.

Furthermore, the EU’s regulator concluded:

• Microsoft-Nuance will continue to face stiff competition from rivals in the future.
• There’d be no ability/incentive to foreclose existing market solutions.
• Nuance can only use the data it collects for its own services.
• The data will not provide Microsoft with an advantage to shut out competing software providers.

The companies appear keen to ensure that people are aware the deal is about more than just healthcare.

“Combining the power of Nuance’s deep vertical expertise and proven business outcomes across healthcare, financial services, retail, telecommunications, and other industries with Microsoft’s global cloud ecosystems will enable us to accelerate our innovation and deploy our solutions more quickly, more seamlessly, and at greater scale to solve our customers’ most pressing challenges,” said Mark Benjamin, CEO of Nuance.

Benjamin will remain the CEO of Nuance and will report to Guthrie.

(Photo by Omid Armin on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Microsoft acquires Nuance to usher in ‘new era of outcomes-based AI’ appeared first on AI News.

Last year, the European Commission drafted AI laws. While the US and China are set to dominate AI development with their vast resources, economic might, and light-touch regulation, European rivals – including the UK and EU members – believe they can lead in ethical standards.

In the draft of the EU regulations, companies that are found guilty of AI misuse face a fine of €30 million or six percent of their global turnover (whichever is greater). The risk of such fines has been criticised as driving investments away from Europe.

The EU’s draft AI regulation classifies systems into three risk categories:

• Limited risk – includes systems like chatbots, inventory management, spam filters, and video games.
• High risk – includes systems that make vital decisions like evaluating creditworthiness, recruitment, justice administration, and biometric identification in non-public spaces.
• Unacceptable risk – includes systems that are manipulative or exploitative, create social scoring, or conduct real-time biometric authentication in public spaces for law enforcement.

Unacceptable risk systems will face a blanket ban from deployment in the EU while limited risk will require minimal oversight.

Organisations deploying high-risk AI systems would be required to have things like:

• Human oversight.
• A risk-management system.
• Record keeping and logging.
• Transparency to users.
• Data governance and management.
• Conformity assessment.
• Government registration.

However, the cumbersome nature of the EU – requiring agreement from all member states, each with their own priorities – means that new regulations are often subject to more debate and delay than national lawmaking.

Reuters reports that two key lawmakers on Wednesday said the EU’s AI regulations will likely take over a year more to agree. The delay is primarily due to debates over whether facial recognition should be banned and who should enforce the rules.

“Facial recognition is going to be the biggest ideological discussion between the right and left,” said one lawmaker, Dragos Tudorache, in a Reuters interview.

“I don’t believe in an outright ban. For me, the solution is to put the right rules in place.”

With leading academic institutions and more than 1,300 AI companies employing over 30,000 people, the UK is the biggest destination for AI investment in Europe and the third in the world. Between January and June 2021, global investors poured £13.5 billion into more than 1,400 “deep tech” UK private technology firms—more than Germany, France, and Israel combined.

In September 2021, the UK published its 10-year National Artificial Intelligence Strategy in a bid to secure its European AI leadership. Governance plays a large role in the strategy.

“The UK already punches above its weight internationally and we are ranked third in the world behind the USA and China in the list of top countries for AI,” commented DCMS Minister Chris Philp.

“We’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it.”

As part of its strategy, the UK is creating an ‘AI Standards Hub’ to coordinate the country’s engagement in establishing global rules and is working with The Alan Turing Institute to update guidance on AI ethics and safety.

“We are proud of creating a dynamic, collaborative community of diverse researchers and are growing world-leading capabilities in responsible, safe, ethical, and inclusive AI research and innovation,” said Professor Sir Adrian Smith, Chief Executive of The Alan Turing Institute.

Striking a balance between innovation-stifling overregulation and ethics-compromising underregulation is never a simple task. It will be interesting to observe how AI regulations in Europe will differ across the continent and beyond.

(Photo by Christian Lue on Unsplash)

Related: British intelligence agency GCHQ publishes ‘Ethics of AI’ report

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post The EU’s AI rules will likely take over a year to be agreed appeared first on AI News.

As we wrap up the year, it’s time to look ahead at what to expect from the AI industry in 2022.

Tackling bias

Our ‘Ethics & Society’ category got more use than most others this year, and with good reason. AI cannot thrive when it’s not trusted.

Biases are present in algorithms that are already causing harm. They’ve been the subject of many headlines, including a number of ours, and must be addressed for the public to have confidence in wider adoption.

Explainable AI (XAI) is a partial solution to the problem. XAI is artificial intelligence in which the results of the solution can be understood by humans.

Robert Penman, Associate Analyst at GlobalData, comments:

“2022 will see the further rollout of XAI, enabling companies to identify potential discrimination in their systems’ algorithms. It is essential that companies correct their models to mitigate bias in data. Organisations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency. For example, in the Netherlands, the government’s use of AI to identify welfare fraud was found to violate European human rights.

Reducing human bias present in training datasets is a huge challenge in XAI implementation. Even tech giant Amazon had to scrap its in-development hiring tool because it was claimed to be biased against women.

Further, companies will be desperate to improve their XAI capabilities—the potential to avoid a PR disaster is reason enough.”

To that end, expect a large number of acquisitions of startups specialising in synthetic data training in 2022.

Smoother integration

Many companies don’t know how to get started on their AI journeys. Around 30 percent of enterprises plan to incorporate AI into their company within the next few years, but 91 percent foresee significant barriers and roadblocks.

If the confusion and anxiety that surrounds AI can be tackled, it will lead to much greater adoption.

Dr Max Versace, PhD, CEO and Co-Founder of Neurala, explains:

“Similar to what happened with the introduction of WordPress for websites in early 2000, platforms that resemble a ‘WordPress for AI’ will simplify building and maintaining AI models. 

In manufacturing for example, AI platforms will provide integration hooks, hardware flexibility, ease of use by non-experts, the ability to work with little data, and, crucially, a low-cost entry point to make this technology viable for a broad set of customers.”

AutoML platforms will thrive in 2022 and beyond.

From the cloud to the edge

The migration of AI from the cloud to the edge will accelerate in 2022.

Edge processing has a plethora of benefits over relying on cloud servers including speed, reliability, privacy, and lower costs.

Versace commented:

“Increasingly, companies are realising that the way to build a truly efficient AI algorithm is to train it on their own unique data, which might vary substantially over time. To do that effectively, the intelligence needs to directly interface with the sensors producing the data. 

From there, AI should run at a compute edge, and interface with cloud infrastructure only occasionally for backups and/or increased functionality. No critical process – for example,  in a manufacturing plant – should exclusively rely on cloud AI, exposing the manufacturing floor to connectivity/latency issues that could disrupt production.”

Expect more companies to realise the benefits of migrating from cloud to edge AI in 2022.

Doing more with less

Among the early concerns about the AI industry is that it would be dominated by “big tech” due to the gargantuan amount of data they’ve collected.

However, innovative methods are now allowing algorithms to be trained with less information. Training using smaller but more unique datasets for each deployment could prove to be more effective.

We predict more startups will prove the world doesn’t have to rely on big tech in 2022.

Human-powered AI

While XAI systems will provide results which can be understood by humans, the decisions made by AIs will be more useful because they’ll be human-powered.

Varun Ganapathi, PhD, Co-Founder and CTO at AKASA, said:

“For AI to truly be useful and effective, a human has to be present to help push the work to the finish line. Without guidance, AI can’t be expected to succeed and achieve optimal productivity. This is a trend that will only continue to increase.

Ultimately, people will have machines report to them. In this world, humans will be the managers of staff – both other humans and AIs – that will need to be taught and trained to be able to do the tasks they’re needed to do.

Just like people, AI needs to constantly be learning to improve performance.”

Greater human input also helps to build wider trust in AI. Involving humans helps to counter narratives about AI replacing jobs and concerns that decisions about people’s lives could be made without human qualities such as empathy and compassion.

Expect human input to lead to more useful AI decisions in 2022.

Avoiding captivity

The telecoms industry is currently pursuing an innovation called Open RAN which aims to help operators avoid being locked to specific vendors and help smaller competitors disrupt the relative monopoly held by a small number companies.

Enterprises are looking to avoid being held in captivity by any AI vendor.

Doug Gilbert, CIO and Chief Digital Officer at Sutherland, explains:

“Early adopters of rudimentary enterprise AI embedded in ERP / CRM platforms are starting to feel trapped. In 2022, we’ll see organisations take steps to avoid AI lock-in. And for good reason. AI is extraordinarily complex.

When embedded in, say, an ERP system, control, transparency, and innovation is handed over to the vendor not the enterprise. AI shouldn’t be treated as a product or feature: it’s a set of capabilities. AI is also evolving rapidly, with new AI capabilities and continuously improved methods of training algorithms.

To get the most powerful results from AI, more enterprises will move toward a model of combining different AI capabilities to solve unique problems or achieve an outcome. That means they’ll be looking to spin up more advanced and customizable options and either deprioritising AI features in their enterprise platforms or winding down those expensive but basic AI features altogether.”

In 2022 and beyond, we predict enterprises will favour AI solutions that avoid lock-in.

Chatbots get smart

Hands up if you’ve ever screamed (internally or externally) that you just want to speak to a human when dealing with a chatbot—I certainly have, more often than I’d care to admit.

“Today’s chatbots have proven beneficial but have very limited capabilities. Natural language processing will start to be overtaken by neural voice software that provides near real time natural language understanding (NLU),” commented Gilbert.

“With the ability to achieve comprehensive understanding of more complex sentence structures, even emotional states, break down conversations into meaningful content, quickly perform keyword detection and named entity recognition, NLU will dramatically improve the accuracy and the experience of conversational AI.”

In theory, this will have two results:

• Augmenting human assistance in real-time, such as suggesting responses based on behaviour or based on skill level.
• Change how a customer or client perceives they’re being treated with NLU delivering a more natural and positive experience.  

In 2022, chatbots will get much closer to offering a human-like experience.

It’s not about size, it’s about the quality

A robust AI system requires two things: a functioning model and underlying data to train that model. Collecting huge amounts of data is a waste of time if it’s not of high quality and labeled correctly.

Gabriel Straub, Chief Data Scientist at Ocado Technology, said:

“Andrew Ng has been speaking about data-centric AI, about how improving the quality of your data can often lead to better outcomes than improving your algorithms (at least for the same amount of effort.)

So, how do you do this in practice? How do you make sure that you manage the quality of data at least as carefully as the quantity of data you collect?

There are two things that will make a big difference: 1) making sure that data consumers are always at the heart of your data thinking and 2) ensuring that data governance is a function that enables you to unlock the value in your data, safely, rather than one that focuses on locking down data.”

Expect the AI industry to make the quality of data a priority in 2022.

(Photo by Michael Dziedzic on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

The post Editorial: Our predictions for the AI industry in 2022 appeared first on AI News.

Nuance gained renown for originally creating the backend of that little old virtual assistant called Siri (you might have heard of it?)

The company has since continued to focus on building its speech recognition capabilities and has a number of solutions which span particular industries such as healthcare to general omni-channel customer experience services.

Earlier this year, Microsoft decided Nuance is worth coughing up $19.7 billion for.

As such large deals often do, the proposed acquisition caught the eyes of several global regulators. In the case of the EU, it was referred to the Commission’s regulators on 16 November.

The regulator said on Tuesday that the proposed acquisition “would raise no competition concerns” within the bloc and that “Microsoft and Nuance offer very different products” after looking at potential horizontal overlaps between the companies’ transcription solutions.

Vertical links in the healthcare space were also analysed but it was determined that “competing transcription service providers in healthcare do not depend on Microsoft for cloud computing services” and that “transcription service providers in the healthcare sector are not particularly important users of cloud computing services”.

Furthermore, the regulator concluded:

• Microsoft-Nuance will continue to face stiff competition from rivals in the future.
• There’d be no ability/incentive to foreclose existing market solutions.
• Nuance can only use the data it collects for its own services.
• The data will not provide Microsoft with an advantage to shut out competing software providers.

The EU’s decision mirrors that of regulators in the US and Australia. However, the UK’s Competition and Markets Authority (CMA) announced its own investigation earlier this month.

When it announced the deal, Microsoft said that it aims to complete its acquisition by the end of 2021. The CMA is accepting comments until 10 January 2022 so it seems that Microsoft may have to hold out a bit longer.

(Photo by Annie Spratt on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

The post EU clears $19.7B Microsoft-Nuance deal without any small print appeared first on AI News.

Watson Discovery is an AI-powered intelligent search and text-analytics platform that can retrieve critical information buried in enterprise data.

In one case study, Woodside Energy had no way to retrieve the 30 years’ worth of valuable engineering and drilling knowledge that was buried in unstructured documentation. Using the existing NLP capabilities of Watson Discovery, the firm reportedly cut research time by more than 75 percent.

Among the new enhancements planned for Watson Discovery are:

• Pre-trained document structure understanding: Watson Discovery’s Smart Document Understanding feature now includes a new pre-trained model designed to automatically understand the visual structure and layout of a document without additional training from a developer or data scientist.
• Automatic text pattern detection: A new advanced pattern creation feature is available in beta that helps users to quickly identify business-specific text patterns within their documents. It can start learning the underlying text patterns from as little as two examples and then refines the pattern based on user feedback.
• Advanced NLP customisation capabilities: With a new custom entity extractor feature, IBM is simplifying the process of training NLP models to identify highly-customised, business-specific words by reducing the data prep effort, simplifying labeling with active learning and bulk annotation capabilities, and enabling simple model deployment to accelerate training time.

“The stream of innovation coming to IBM Watson from IBM Research is why global businesses in the fields of financial services, insurance, and legal services turn to IBM to help detect emerging business trends, gain operational efficiency and empower their workers to uncover new insights,” said Daniel Hernandez, General Manager of Data and AI, IBM.

“The pipeline of natural language processing innovations we’re adding to Watson Discovery can continue to provide businesses with the capabilities to more easily extract the signal from the noise and better serve their customers and employees.”

(Image Credit: IBM)

Looking to revamp your digital transformation strategy? Learn more about the Digital Transformation Week event taking place in Amsterdam on 23-24 November 2021 and discover key strategies for making your digital efforts a success.

The post IBM enhances Watson Discovery’s natural language processing capabilities appeared first on AI News.