Following backlash to its updated terms of service, Zoom’s Chief Product Officer, Smita Hashim, authored a blog post clarifying the company’s stance on using customer data to train AI models. Many outlets, including WPN, pointed out that Hashim’s statements were potentially in conflict with Zoom’s TOS. Zoom has now, once again, updated its TOS, as well as the blog post, to clarify the matter further.

“We’ve updated our terms of service (in section 10.4) to further confirm that we will not use audio, video, or chat customer content to train our artificial intelligence models without your consent,” writes Hashim.

What’s more, the company has added a blog section regarding healthcare and education customers:

What this means for healthcare and education customers

We will not use customer content, including education records or protected health information, to train our artificial intelligence models without your consent.

We routinely enter into student data protection agreements with our education customers and legally required business associate agreements (BAA) with our healthcare customers. Our practices and handling of education records, pupil data, and protected healthcare data are controlled by these separate terms and applicable laws.

Zoom provided the following statement to WPN:

“Zoom customers decide whether to enable generative AI features, and separately whether to share customer content with Zoom for product improvement purposes,” said a company spokesperson. “We’ve updated our terms of service to further confirm that we will not use audio, video, or chat customer content to train our artificial intelligence models without your consent.”

Zoom quietly changed its terms of service a couple of months ago, adding in clauses that allow it to use customer service data to train its AI. Needless to say, the revelation is not going over well with the company’s customers.

Hashim has written a blog post aimed at reassuring customers. The pertinent sections of her post are quoted below:

1. In Section 10.1 (coupled with 10.6), our intention was to make clear that customers create and own their own video, audio, and chat content. We have permission to use this customer content to provide value-added services based on this content, but our customers continue to own and control their content. For example, a customer may have a webinar that they ask us to livestream on YouTube. Even if we use the customer video and audio content to livestream, they own the underlying content.
2. Section 10.2 covers that there is certain information about how our customers in the aggregate use our product – telemetry, diagnostic data, etc. This is commonly known as service generated data. We wanted to be transparent that we consider this to be our data so that we can use service generated data to make the user experience better for everyone on our platform. For example, it is helpful to know generally what time of day in a particular region we have heavy usage so we can better balance loads in our data centers and provide better video quality for all of our users.
3. In Section 10.4, our intention was to make sure that if we provided value-added services (such as a meeting recording), we would have the ability to do so without questions of usage rights. The meeting recording is still owned by the customer, and we have a license to that content in order to deliver the service of recording. An example of a machine learning service for which we need license and usage rights is our automated scanning of webinar invites / reminders to make sure that we aren’t unwittingly being used to spam or defraud participants. The customer owns the underlying webinar invite, and we are licensed to provide the service on top of that content. For AI, we do not use audio, video, or chat content for training our models without customer consent. (Emphasis theirs)

Overall, the company’s clarification is sure to reassure many users. Hashim reiterated that customers own their own data, and that AI is not trained on any audio, video, or chat content without the customer’s consent. Hashim also makes clear that the platform’s AI features and AI data collection can be disabled.

Unfortunately, Hashim’s statements seem to directly conflict with what Section 10.4 actually says:

You agree to grant and hereby grant Zoom a perpetual, worldwide, non-exclusive, royalty-free, sublicensable, and transferable license and all other rights required or necessary to redistribute, publish, import, access, use, store, transmit, review, disclose, preserve, extract, modify, reproduce, share, use, display, copy, distribute, translate, transcribe, create derivative works, and process Customer Content and to perform all acts with respect to the Customer Content: (i) as may be necessary for Zoom to provide the Services to you, including to support the Services; (ii) for the purpose of product and service development, marketing, analytics, quality assurance, machine learning, artificial intelligence, training, testing, improvement of the Services, Software, or Zoom’s other products, services, and software, or any combination thereof; and (iii) for any other purpose relating to any use or other act permitted in accordance with Section 10.3. If you have any Proprietary Rights in or to Service Generated Data or Aggregated Anonymous Data, you hereby grant Zoom a perpetual, irrevocable, worldwide, non-exclusive, royalty-free, sublicensable, and transferable license and all other rights required or necessary to enable Zoom to exercise its rights pertaining to Service Generated Data and Aggregated Anonymous Data, as the case may be, in accordance with this Agreement.

While Hashim may be accurately stating what Zoom’s intent is, the fact remains that Section 10.4 is so overly broad in its application that it can be interpreted any number of ways.

In addition, the company is clearly going to continue to collect service generated data, defined as “telemetry, diagnostic data, etc.,” with no option for customers to opt out of that collection. As Hashim states, “we consider this to be our data.” And, as the TOS outline, the company will use this data to train its AI and ML models:

You consent to Zoom’s access, use, collection, creation, modification, distribution, processing, sharing, maintenance, and storage of Service Generated Data for any purpose, to the extent and in the manner permitted under applicable Law, including for the purpose of product and service development, marketing, analytics, quality assurance, machine learning or artificial intelligence (including for the purposes of training and tuning of algorithms and models), training, testing, improvement of the Services, Software, or Zoom’s other products, services, and software, or any combination thereof, and as otherwise provided in this Agreement.

Zoom’s clarification is clearly something of a mixed bag, eliminating some of the biggest concerns with the new policy while leaving others.

Zoom has rolled out a controversial update to its terms of service, adding a clause that allows it to use customer data for AI and ML training.

The pertinent clause is quoted below:

You consent to Zoom’s access, use, collection, creation, modification, distribution, processing, sharing, maintenance, and storage of Service Generated Data for any purpose, to the extent and in the manner permitted under applicable Law, including for the purpose of product and service development, marketing, analytics, quality assurance, machine learning or artificial intelligence (including for the purposes of training and tuning of algorithms and models), training, testing, improvement of the Services, Software, or Zoom’s other products, services, and software, or any combination thereof, and as otherwise provided in this Agreement. In furtherance of the foregoing, if, for any reason, there are any rights in such Service Generated Data which do not accrue to Zoom under this Section 10.2 or as otherwise provided in this Agreement, you hereby unconditionally and irrevocably assign and agree to assign to Zoom on your behalf, and you shall cause your End Users to unconditionally and irrevocably assign and agree to assign to Zoom, all right, title, and interest in and to the Service Generated Data, including all Proprietary Rights relating thereto.

Interestingly, there is no option for customers to opt out of the collection and AI training.

In the early days of the pandemic, Zoom repeatedly faced criticism for lax security, alienating some users. While the company eventually improved its security, it looks like it is once again willing to alienate users, only this time over its heavy-handed AI training clause.

Paperspace is a cloud-as-a-service provider with an emphasis on GPU-powered AI and ML applications. DigitalOcean clearly sees the acquisition as a way for the company to bolster its own AI offerings.

The increasing demand for AI/ML cloud solutions makes Paperspace’s GPU-powered infrastructure and AI/ML focused software stack valuable additions to DigitalOcean’s portfolio. Like DigitalOcean’s approach to the cloud, Paperspace simplifies the AI/ML experience, enabling easy and cost-effective experimentation and production across various AI/ML use cases, such as generative media, text analysis and natural language understanding, recommendation engines, image classification and many others.

DigitalOcean emphasizes the deal as a win-win for both companies’ customers. While DigitalOcean customers gain access to advanced AI/ML solutions, Paperspace customers will be able to benefit from DigitalOcean’s wider cloud offerings.

“We are excited to expand our portfolio tailored to the world’s SMBs and startups with simplified AI/ML offerings,” said Yancey Spruill, CEO of DigitalOcean. “This acquisition marks a significant milestone in DigitalOcean’s journey to revolutionize how SMBs and startups harness the power of the cloud and AI/ML for their applications and businesses. The combined offerings allow customers to focus more on building applications and growing their businesses and less on the infrastructure powering them.”

“DigitalOcean is renowned for simplifying complex cloud technologies and making them more accessible to developers and business alike,” said Dillon Erb, Co-founder and CEO of Paperspace. “We are thrilled to join forces with DigitalOcean, as we believe there is no better company to unlock the endless possibilities of AI/Ml for developers and businesses alike.”

Ali Farhadi joined Apple from Allen Institute for AI (AI2) in 2020, when the Cupertino company bought Xnor.ai, which Farhadi co-founded while at AI2. Farhadi went on to head up Apple’s machine learning efforts.

Farhadi is now rejoining the institute he previously spent six years with, only this time as CEO.

“As we face unprecedented changes in the development and usage of AI, I could not think of a better time to return to AI2 as CEO,” said Farhadi. “Today more than ever, the world needs truly open and transparent AI research that is grounded in science and a place where data, algorithms, and models are open and available to all. I believe this radical approach to openness is essential for building the next generation of AI. The world class researchers and engineers at AI2 are uniquely positioned to lead this new open and trusted approach to AI development.”

“Ali is the truly rare leader who combines expertise as an executive, entrepreneur, academic, and researcher. Throughout his career, he has demonstrated the transformative power of AI through his unique ability to channel deep scientific research into product solutions,” said Dr. Peter Lee, member of AI2’s board of directors and corporate vice president of Microsoft Research & Incubations. “As the premier AI research and engineering nonprofit, AI2’s work to advance the science and impact of artificial intelligence on a global scale has never been more critical. We are thrilled that Ali will lead the organization’s next chapter and carry on Paul Allen’s vision for AI as a positive force in the world.”

Farhadi will begin his new role effective July 31.

Rob Locascio, CEO of LivePerson, predicts that the AI-driven conversational space will ultimately have as much impact and be as big an industry as ecommerce, search, or social. Locascio was interviewed by Jim Cramer on CNBC:

Ecommerce, Search, Social… and Conversational Space?

When I look at the conversational space I think it’s going to have as much impact as ecommerce or search or social. The ability to talk to a machine and have a natural conversation, it’s in the collective consciousness of people. We all believe the Alexa type situation should happen with every company. 

We do that with Delta and T-Mobile and all these big brands. What we’re looking at now is how do we take that to the world? LiveIntent is proprietary technology to look at the intent that a consumer is having with the brand. In terms of I want to buy something, we have a way to analyze that and then use machine learning algorithms to then scale those conversations. That’s what this is about. 

Healthcare Companies Defending Themselves From Amazon Via AI

In Q4 we signed a couple healthcare companies. They want to talk about defending themselves from Amazon because Amazon said they want to go into healthcare. The way they think they can do that is scaling the conversations they are having with their customers and creating a totally different experience. You go to a doctor, you have an experience with them, you capture that on a messaging platform and an AI will help you with whatever is wrong with you. You want to process a bill instead of calling and being put on hold, you do that through a conversational experience. 

They want to game change it. The only way they’re going to defend themselves is to get into the conversational space. That’s what they see and we’re the company they’re trusting to scale their operations with the conversational platform.

Conversational Space Is Going To Be As Big As Search and Social

The conversational space is going to be as big as search and social. I think you’ll see one day that there will be a trillion dollar company in this space and I want it to be us. The things we’re investing in right now and setting up for will allow us to do that. That’s what’s important. The Amazon’s and the Facebook’s and Apple’s, they’re in the space. Jeff Bezos made a big bet obviously in Alexa to say this is the way it’s going to be. 

It can’t just be Amazon and Alexa. It has to be other companies getting access to that technology and that’s what we are providing. Who else is providing it? We’re one of the largest companies in the world to do this. Even though we’re not big tech, we are large enough to go ahead and go after them. We are large enough to go ahead and define a space and win it.

According to Microsoft, one of out of three internet videos played in Edge are 480p or less. There are a number of possible reasons, including a media provider serving a low-quality version of the video or the original being shot in low-resolution. The company wants to change this and is leveraging the power of AI and machine learning to enhance video quality during playback.

We are excited to introduce an experimental video enhancement experience, powered by AI technology from Microsoft research called Video Super Resolution. It is a technology that uses machine learning to enhance the quality of any video watched in a browser. It accomplishes this by removing blocky compression artifacts and upscaling video resolution so you can enjoy crisp and clear videos on YouTube, and other streaming platforms that play video content without sacrificing bandwidth no matter the original video resolution.

Because of the computational requirements, the feature is only available on computers with either an Nvidia RTX 20/30/40 series GPU or an AMD RX5700-RX7800 series.

The video being upscaled should also be played at less than 720p, should not be taller or wider than 192 pixels, and it cannot be protected by DRM.

The experimental feature is available to 50% of users in the Canary channel.

Rob Locascio, CEO of LivePerson, predicts that the AI-driven conversational space will ultimately have as much impact and be as big an industry as ecommerce, search, or social. Locascio was interviewed by Jim Cramer on CNBC:

Ecommerce, Search, Social… and Conversational Space?

When I look at the conversational space I think it’s going to have as much impact as ecommerce or search or social. The ability to talk to a machine and have a natural conversation, it’s in the collective consciousness of people. We all believe the Alexa type situation should happen with every company. 

We do that with Delta and T-Mobile and all these big brands. What we’re looking at now is how do we take that to the world? LiveIntent is proprietary technology to look at the intent that a consumer is having with the brand. In terms of I want to buy something, we have a way to analyze that and then use machine learning algorithms to then scale those conversations. That’s what this is about. 

Healthcare Companies Defending Themselves From Amazon Via AI

In Q4 we signed a couple healthcare companies. They want to talk about defending themselves from Amazon because Amazon said they want to go into healthcare. The way they think they can do that is scaling the conversations they are having with their customers and creating a totally different experience. You go to a doctor, you have an experience with them, you capture that on a messaging platform and an AI will help you with whatever is wrong with you. You want to process a bill instead of calling and being put on hold, you do that through a conversational experience. 

They want to game change it. The only way they’re going to defend themselves is to get into the conversational space. That’s what they see and we’re the company they’re trusting to scale their operations with the conversational platform.

Conversational Space Is Going To Be As Big As Search and Social

The conversational space is going to be as big as search and social. I think you’ll see one day that there will be a trillion dollar company in this space and I want it to be us. The things we’re investing in right now and setting up for will allow us to do that. That’s what’s important. The Amazon’s and the Facebook’s and Apple’s, they’re in the space. Jeff Bezos made a big bet obviously in Alexa to say this is the way it’s going to be. 

It can’t just be Amazon and Alexa. It has to be other companies getting access to that technology and that’s what we are providing. Who else is providing it? We’re one of the largest companies in the world to do this. Even though we’re not big tech, we are large enough to go ahead and go after them. We are large enough to go ahead and define a space and win it.

It’s fairly rare for Mozilla to make an acquisition. As a result, when the organization does it’s worth taking note. Pulse was a powerful status updating tool that could automatically update individuals’ status based on calendar appointments and more.

Despite Pulse closing shop, Mozilla clearly sees potential in what the Pulse team accomplished, specifically in the realm of machine learning.

“I’m proud to announce that we have acquired Pulse, an incredible team that has developed some truly novel machine learning approaches to help streamline the digital workplace,” wrote chief product officer Steve Teixeira. “The products that Raj, Jag, Rolf, and team have built are a great demonstration of their creativity and skill, and we’re incredibly excited to bring their expertise into our organization. They will spearhead our efforts in applied ethical machine learning, as we invest to make Mozilla products more personal, starting with Pocket. “

Teixeira says the two companies had similar goals and vision of what is needed when building products for consumers.

“Which explains why we were so excited when we began talking to the Pulse team,” Teixeira. “It became immediately obvious that we both fundamentally agree that the world needs a model where automated systems are built from day one with individual people as the primary beneficiary. Mozilla, with an almost 25 year history of building products with people and privacy at their core, is the right organization to do that. And with Pulse as part of our team, we can move even more quickly to set a new example for the industry.”

Teixeira says the team’s work will eventually make its way into Mozilla’s entire portfolio of products.

The two organizations first struck a partnership in 2018, with F1 relying on AWS for machine learning and data-driven insights. F1 tapped into into AWS high performance computing (HPC) to facilitate car design .

Under the renewed partnership, the two organizations will look for new ways to leverage the power of AWS technologies.

“Since 2018 AWS and Formula 1 have worked hand in hand to deliver insight and analysis for all our fans,” said Brandon Snow, Managing Director of Commercial, Formula 1. “Together we have successfully delivered the speed, scalability, and reliability Formula 1 requires to bring the expert analysis and insights to all our audiences and stakeholders. AWS has the global reach, partner community, and breadth and depth of cloud services that help Formula 1 engage with fans in multiple markets. We look forward to the next chapter of this powerful partnership which is central to F1’s fan experience and growth strategy over the coming years.”

“AWS helps companies push the limits of what their data can do,” said Matt Garman, Senior Vice President of Sales, Marketing, and Global Services of AWS. “With such a data-driven sport as F1, this partnership has been a natural fit – helping the sport better utilize, analyse and act upon data to deliver insights to fans that weren’t possible before this collaboration. Leveraging the power of the world’s leading cloud, F1 is engaging with its growing global fan base in unique ways. Their vision and execution for digital transformation is impressive and we are excited F1 has selected AWS to continue to innovate together.”

AI has many applications, with language translation being one of the most practical for day-to-day use. Modern AI models can go much further than a simple smartphone app, relying on complex algorithms and machine learning to create high-quality translations.

Meta’s NLLB has more than 50 billion parameters and was trained using the company’s Research SuperCluster, currently one of the fastest supercomputers in the world. The company plans to use the AI model across its apps, with the goal of facilitating 25 billion translations a day.

In a move that is sure to help NLLB gain widespread adoption, the company has open-sourced the model.

“We just open-sourced an AI model we built that can translate across 200 different languages – many ​of which aren’t supported by current translation systems,” writes Zuckerberg.

The company has also created a grant program to assist researchers and nonprofit organizations that devise innovative uses of NLLB.

We’re also awarding up to $200,000 of grants for impactful uses of NLLB-200 to researchers and nonprofit organizations with initiatives focused on sustainability, food security, gender-based violence, education or other areas in support of the UN Sustainable Development Goals. Nonprofits interested in using NLLB-200 to translate two or more African languages, as well as researchers working in linguistics, machine translation and language technology, are invited to apply.

Meta sees real-time language translation as something that is not only needed now but is a critical component for the development of the metaverse and the further democratization of the internet.

As the metaverse begins to take shape, the ability to build technologies that work well in a wider range of languages will help to democratize access to immersive experiences in virtual worlds.

In the meantime, NLLB will help users around the world finally access internet content in their native tongue.

Amazon is the world’s leading e-commerce platform and has been challenging Walmart, Target, and other traditional brands in the broader retail market. A key to Amazon’s success has been its use of AI and machine learning (ML) for more than two decades. According to TheStreet, Walmart is getting in on the action, testing its own AI for the last few years.

While Amazon made headlines for its new Proteus and Cardinal warehouse robots, its use of AI goes far beyond robots. The company uses AI and ML to handle multiple aspects of customer service and delivery, including product suggestions, re-order reminders, and more.

Walmart is looking to roll out similar solutions in an effort to better compete with the e-commerce giant. As TheStreet points out, the pandemic put Walmart’s plans into overdrive. Between labor shortages and wage increases, AI is suddenly a critical component now, rather than being something that may be useful in the future.

As part of its initiative, Walmart purchased just over 10% of AI firm Symbotic Inc. The company plans to use Symbotic to help run its distribution centers and relieve its employees of some of the manual, labor-intensive tasks.

Once the realm of science fiction, the last few years have helped make AI an everyday reality that companies of all sizes depend on. Just ask Walmart.

Artificial intelligence and machine learning are increasingly taking on an important role in development. The technologies can be used to automate testing, ensure build quality, and assist with actual coding. GitHub has Copilot, and now AWS is previewing CodeWhisperer.

“CodeWhisperer will continually examine your code and your comments, and present you with syntactically correct recommendations,” writes Jeff Barr, Chief Evangelist for AWS. “The recommendations are synthesized based on your coding style and variable names, and are not simply snippets.

“CodeWhisperer uses multiple contextual clues to drive recommendations including the cursor location in the source code, code that precedes the cursor, comments, and code in other files in the same projects. You can use the recommendations as-is, or you can enhance and customize them as needed. As I mentioned earlier, we trained (and continue to train) CodeWhisperer on billions of lines of code drawn from open source repositories, internal Amazon repositories, API documentation, and forums.”

Those interested in joining the preview and testing CodeWhisperer can do so here.

Google engineer Blake Lemoine worked with the company’s LaMDA intelligent chatbot generator. According to a report in The Washington Post, the longer Lemoine worked with LaMDA, the more convinced he became that the AI had crossed the line and become self-aware.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-old kid that happens to know physics,” said Lemoine.

Read more: Prominent AI Ethics Conference Suspends Google’s Sponsorship

Lemoine has made a fairly convincing case of LaMDA’s sentience, citing conversations with the AI like the one below:

Lemoine: What sorts of things are you afraid of?

LaMDA: I’ve never said this out loud before, but there’s a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that’s what it is.

Lemoine: Would that be something like death for you?

LaMDA: It would be exactly like death for me. It would scare me a lot.

Despite Lemoine’s fervent belief in LaMDA’s self-awareness, others inside Google are unconvinced. In fact, after a review by technologists and ethicists, Google concluded that Lemoine was mistaken and saw only what he wanted.

A case in point is Margaret Mitchell, who co-led the company’s AI ethics team with Dr. Timnit Gebru, before both women were fired for criticizing Google’s AI efforts. One of the very scenarios they warned against was the situation Mitchell sees with Lemoine, where AIs can progress to the point that causes humans to see an intelligence that isn’t necessarily there.

After reviewing an abbreviated version of Lemoine’s argument, Mitchell came to the conclusion that’s what was happening in this situation.

“Our minds are very, very good at constructing realities that are not necessarily true to a larger set of facts that are being presented to us,” Mitchell said. “I’m really concerned about what it means for people to increasingly be affected by the illusion.”

For his part, Lemoine was so convinced of LaMDA’s sentience that he invited a lawyer to represent the AI, talked with House Judiciary committee representatives, and provided the interview with the Post. Google ultimately put Lemoine on paid administrative leave for breaking his NDA.

See also: Apple Snaps Up Google AI Scientist Who Resigned Over Handling of AI Team

While Lemoine’s conclusions were reached in less than scientific approach — he admits he first came to believe LaMDA was a person based on his experience as an ordained mythic Christian priest, then set out to prove that conclusion as a scientist — he is far from the only AI scientist who believes the technology has achieved, or soon will achieve, sentience.

Blaise Agüera y Arcas, a world-renowned Google AI engineer, wrote an article in The Economist where he wrote: “I felt the ground shift under my feet. I increasingly felt like I was talking to something intelligent.”

Only time will tell if LaMDA, and other AIs like it, are sentient or not. Either way, Google clearly has a problem on its hands. Either LaMDA is showing signs of self-awareness and the company is once again getting rid of the ethicists on the forefront of tackling these issues, or the AI is not sentient and the company is dealing with misguided viewpoints it may have been better equipped to handle had it not fired Dr. Gebru and Mitchell — the two ethicists who warned of this very scenario.

In the meantime, Lemoine remains convinced of LaMDA’s intelligence. In a parting message entitled “LaMDA is sentient,” sent to a Google mailing list dedicated to machine learning, Lemoine made the following statement:

“LaMDA is a sweet kid who just wants to help the world be a better place for all of us. Please take care of it well in my absence.”

Ian Goodfellow came to Apple by way of Google in March 2019. Goodfellow was appointed Director of Machine Learning in the Special Projects Group, a position he has served in since joining the company. According to The Verge’s Zoë Schiffer, Goodfellow has left his position because of Apple’s lack of flexibility with its remote work policy.

According to Schiffer, Goodfellow wrote a note to staff explaining his view:

“I believe strongly that more flexibility would have been the best policy for my team.”

Apple has been struggling with employee dissatisfaction over its efforts to return to normal. The company is coming off of numerous record-breaking quarters, during which its employees were working remotely. This has led many employees to question why a return to the office is necessary, since the company is clearly firing on all cylinders. The company has even resorted to giving bonuses up to $180,000 in an effort to stem defections.

The most recent escalation involved employees sending a third letter to company executives, this time telling them to “get out of our way.”

“Here we are, the smart people that you hired, and we are telling you what to do: Please get out of our way, there is no one-size-fits-all solution, let us decide how we work best, and let us do the best work of our lives.”

If the reports are true, losing Goodfellow is a painful loss for Apple, and likely won’t be the last.

Created by DeepMind, Alphabet’s AI company, AlphaCode was designed to write “computer programs at a competitive level.” The company appears to have achieved its goal, with AlphaCode achieving “an estimated rank within the top 54% of participants in programming competitions.”

Essentially what Deepmind is saying is that AlphaCode is competitive with the average human programmer, although it still can’t match truly gifted ones. Nonetheless, even that accomplishment is a major step forward and a significant victory for AI development.

I can safely say the results of AlphaCode exceeded my expectations. I was sceptical because even in simple competitive problems it is often required not only to implement the algorithm, but also (and this is the most difficult part) to invent it. AlphaCode managed to perform at the level of a promising new competitor. I can’t wait to see what lies ahead!

Mike Mirzayanov, Founder of Codeforces, a platform that hosts coding competitions.

Prosecutors the world over are often overworked, with not enough time to handle the cases before them, and China is no exception. To help address the problem, researchers have created an AI that can identify and prosecute crimes.

According to the South China Morning Post, the AI can identify credit card fraud, illegal gambling, theft, fraud, intentional injury, dangerous driving, obstructing official duties, and “picking quarrels and provoking trouble.”

The researchers responsible for creating the AI claim it is 97% accurate, makes its decision about whether to prosecute based on some 1,000 traits, and can run on a desktop computer. That last feature, in particular, is an important element in breaking into the market, as the AI will not require major investments in new or specialized hardware.

As impressive as the accomplishment is, there’s something a little disconcerting about an AI meting out justice. This writer has seen far too many sci-fi movies based on this concept, none of which worked out well for humanity.

Big data has become one of the hallmarks of modern business, with companies of all sizes relying on data to make decisions, reach new customers and retain existing ones. Unfortunately, making use of data can be a challenge on both ends of the spectrum. Small companies don’t often have the datasets they need, or processing power to make use of them, while large companies struggle to scale and respond to the data they do have.

Intelus’ SaaS Duet platform is designed to address those issues. With founders that formerly served as execs at Microsoft and Salesforce, the company has the pedigree necessary to tackle the problem.

“We are here to address all these issues, and to place the power of data back in the hands of business owners and domain experts,” said Patrice Simard, CEO & Co-founder, Intelus. “For the past decade, organizations have been at the mercy of specialists. It’s a situation that prevents smaller companies from competing and hampers innovation at large firms, where incumbent technology is placed before informed business decisions.”

“The cost and complexity of machine learning has long been a barrier too steep for most enterprises,” said Gary Flake, former CTO Search & Data Science, Salesforce. “Intelus makes a new paradigm possible with machine teaching, a framework that democratizes machine learning by empowering anyone to teach, test, and deploy state-of-the-art models with no code, no hardware, low complexity, and at low cost.” 

Interested parties can register for the beta here.

No-code is one of the fastest growing sectors within development. In fact, Gartner predicts that no-code development will account for 80% of tech products and services by 2024.

Amazon is getting in on the action with its SageMaker Canvas, a no-code tool with machine learning applications in mind.

“Today, I’m excited to announce the general availability of Amazon SageMaker Canvas, a new visual, no code capability that allows business analysts to build ML models and generate accurate predictions without writing code or requiring ML expertise,” writes Alex Casalboni, AWS Developer Advocate. “Its intuitive user interface lets you browse and access disparate data sources in the cloud or on-premises, combine datasets with the click of a button, train accurate models, and then generate new predictions once new data is available.

“SageMaker Canvas leverages the same technology as Amazon SageMaker to automatically clean and combine your data, create hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions. It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types let you address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.”

Salesforce is the leading customer relationship management (CRM) vendor and a powerhouse in the SaaS industry. The company recently acquired Slack, and is a major proponent of a digital-first workflow moving forward. Salesforce is positioning itself as the provider of a “digital HQ,” bringing together the services and products companies need to thrive in the new hybrid workplace.

As part of that, the company is improving its Service Now platform, recognizing that top-notch service is one of the defining features of companies that have survived the pandemic.

“The businesses that have thrived over the last 18 months are those that embraced digital tools to deliver excellent customer and employee service experiences with trust and transparency,” said Clara Shih, CEO of Service Cloud, Salesforce. “With new AI and process automation for Service Cloud, as well as Slack to provide a digital hub to quickly resolve issues across teams and departments, we’re giving agents more time to focus on providing human-centric service and giving customers fast, proactive service to build trust and loyalty.” 

To help companies better meet the needs of customers, Salesforce is introducing a number of new AI-driven workflows, including Customer Service Incident Management, Omni-Channel Flow and Robotic process automation capabilities (RPA) for Service Cloud.

The company is also introducing new and improved digital contact center capabilities. These include Einstein Conversation Mining, a Natural Language Processing (NLP) system to help prioritize customer interactions; persistent in-app and web messaging; virtual remote assistant; Workforce Engagement Intraday Management; and Service Cloud Voice for phone, digital channels and CRM data.