GFN Thursday Delivers Seven New Games This Week

TGIGFNT: thank goodness it’s GFN Thursday. Start your weekend early with seven new games joining the GeForce NOW library of over 1,400 titles.

Whether it’s streaming on an older-than-the-dinosaurs PC, a Mac that normally couldn’t dream of playing PC titles, or mobile devices – it’s all possible to play your way thanks to GeForce NOW.

Get Right Into the Gaming

Test your tactical skills in the new authentic WW1 first person shooter, Isonzo.

Isonzo
The Great War on the Italian Front is brought to life and streaming from the cloud.

Battle among the scenic peaks, rugged valleys and idyllic towns of northern Italy. Choose from six classes based on historical combat roles and build a loadout from a selection of weapons, equipment and perks linked to that class. Shape a dynamic battlefield by laying sandbags and wire, placing ammo crates, deploying trench periscopes or sniper shields, and more.

Lead to charge to victory in this game and six more this week, including:

Members can also discover impressive new prehistoric species with the Jurassic World Evolution 2: Late Cretaceous Pack DLC, available on GeForce NOW this week.

Inspired by the fascinating Late Cretaceous period, this pack includes four captivating species that roamed the land, sea and air over 65 million years ago from soaring, stealthy hunters of the skies to one of the largest dinosaurs ever discovered.

Finally, kick off the weekend by telling us about a game that you love on Twitter or in the comments below.

The post GFN Thursday Delivers Seven New Games This Week appeared first on NVIDIA Blog.

Read More

Announcing Visual Conversation Builder for Amazon Lex

Amazon Lex is a service for building conversational interfaces using voice and text. Amazon Lex provides high-quality speech recognition and language understanding capabilities. With Amazon Lex, you can add sophisticated, natural language bots to new and existing applications. Amazon Lex reduces multi-platform development efforts, allowing you to easily publish your speech or text chatbots to mobile devices and multiple chat services, like Facebook Messenger, Slack, Kik, or Twilio SMS.

Today, we added a Visual Conversation Builder (VCB) to Amazon Lex—a drag-and-drop conversation builder that allows users to interact and define bot information by manipulating visual objects. These are used to design and edit conversation flows in a no-code environment. There are three main benefits of the VCB:

  • It’s easier to collaborate through a single pane of glass
  • It simplifies conversational design and testing
  • It reduces code complexity

In this post, we introduce the VCB, how to use it, and share customer success stories.

Overview of the Visual Conversation Builder

In addition to the already available menu-based editor and Amazon Lex APIs, the visual builder gives a single view of an entire conversation flow in one location, simplifying bot design and reducing dependency on development teams. Conversational designers, UX designers, and product managers—anyone with an interest in building a conversation on Amazon Lex—can utilize the builder.

Designers and developers can now collaborate and build conversations easily in the VCB without coding the business logic behind the conversation. The visual builder helps accelerate time to market for Amazon Lex-based solutions by providing better collaboration, easier iterations of the conversation design, and reduced code complexity.

With the visual builder, it’s now possible to quickly view the entire conversation flow of the intent at a glance and get visual feedback as changes are made. Changes to your design are instantly reflected in the view, and any effects to dependencies or branching logic is immediately apparent to the designer. You can use the visual builder to make any changes to the intent, such as adding utterances, slots, prompts, or responses. Each block type has its own settings that you can configure to tailor the flow of the conversation.

Previously, complex branching of conversations required implementation of AWS Lambda—a serverless, event-driven compute service—to achieve the desired pathing. The visual builder reduces the need for Lambda integrations, and designers can perform conversation branching without the need for Lambda code, as shown in the following example. This helps to decouple conversation design activities from Lambda business logic and integrations. You can still use the existing intent editor in conjunction with the visual builder, or switch between them at any time when creating and modifying intents.

The VCB is a no-code method of designing complex conversations. For example, you can now add a confirmation prompt in an intent and branch based on a Yes or No response to different paths in the flow without code. Where future Lambda business logic is needed, conversation designers can add placeholder blocks into the flow so developers know what needs to be addressed through code. Code hook blocks with no Lambda functions attached automatically take the Success pathway so testing of the flow can continue until the business logic is completed and implemented. In addition to branching, the visual builder offers designers the ability to go to another intent as part of the conversation flow.

Upon saving, VCB automatically scans the build to detect any errors in the conversation flow. In addition, the VCB auto-detects missing failure paths and provides the capability to auto-add those paths into the flow, as shown in the following example.

Using the Visual Conversation Builder

You can access the VCB via the Amazon Lex console by going to a bot and editing or creating a new intent. On the intent page, you can now switch between the visual builder interface and the traditional intent editor, as shown in the following screenshot.

For the intent, the visual builder shows what has already been designed in a visual layout, whereas new intents start with a blank canvas. The visual builder displays existing intents graphically on the canvas. For new intents, you start with a blank canvas and simply drag the components you want to add onto the canvas and begin connecting them together to create the conversation flow.

The visual builder has three main components: blocks, ports, and edges. Let’s get into how these are used in conjunction to create a conversation from beginning to end within an intent.

The basic building unit of a conversation flow is called a block. The top menu of the visual builder contains all the blocks you are able to use. To add a block to a conversation flow, drag it from the top menu onto the flow.

Each block has a specific functionality to handle different use cases of a conversation. The currently available block types are as follows:

  • Start – The root or first block of the conversation flow that can also be configured to send an initial response
  • Get slot value – Tries to elicit a value for a single slot
  • Condition – Can contain up to four custom branches (with conditions) and one default branch
  • Dialog code hook – Handles invocation of the dialog Lambda function and includes bot responses based on dialog Lambda functions succeeding, failing, or timing out
  • Confirmation – Queries the customer prior to fulfillment of the intent and includes bot responses based on the customer saying yes or no to the confirmation prompt
  • Fulfillment – Handles fulfillment of the intent and can be configured to invoke Lambda functions and respond with messages if fulfillment succeeds or fails
  • Closing response – Allows the bot to respond with a message before ending the conversation
  • Wait for user input – Captures input from the customer and switches to another intent based on the utterance
  • End conversation – Indicates the end of the conversation flow

Take the Order Flowers bot as an example. The OrderFlowers intent, when viewed in the visual builder, uses five blocks: Start, three different Get slot value blocks, and Confirmation.

Each block can contain one more ports, which are used to connect one block to another. Blocks contain an input port and one or more output ports based on desired paths for states such a success, timeout, and error.

The connection between the output port of one block and the input port of another block is referred to as an edge.

In the OrderFlowers intent, when the conversation starts, the Start output port is connected to the Get slot value: FlowerType input port using an edge. Each Get slot value block is connected using ports and edges to create a sequence in the conversation flow, which ensures the intent has all the slot values it needs to put in the order.

Notice that currently there is no edge connected to the failure output port of these blocks, but the builder will automatically add these if you choose Save intent and then choose Confirm in the pop-up Auto add block and edges for failure paths. The visual builder then adds an End conversation block and a Go to intent block, connecting the failure and error output ports to Go to intent and connecting the Yes/No ports of the Confirmation block to End conversation.

After the builder adds the blocks and edges, the intent is saved and the conversation flow can be built and tested. Let’s add a Welcome intent to the bot using the visual builder. From the OrderFlowers intent visual builder, choose Back to intents list in the navigation pane. On the Intents page, choose Add intent followed by Add empty intent. In the Intent name field, enter Welcome and choose Add.

Switch to the Visual builder tab and you will see an empty intent, with only the Start block currently on the canvas. To start, add some utterances to this intent so that the bot will be able to direct users to the Welcome intent. Choose the edit button of the Start block and scroll down to Sample utterances. Add the following utterances to this intent and then close the block:

  • Can you help me?
  • Hi
  • Hello
  • I need help

Now let’s add a response for the bot to give when it hits this intent. Because the Welcome intent won’t be processing any logic, we can drag a Closing response block into the canvas to add this message. After you add the block, choose the edit icon on the block and enter the following response:

Hi! I am the Order Flowers Bot. How can I help you today?

The canvas should now have two blocks, but they aren’t connected to each other. We can connect the ports of these two blocks using an edge.

To connect the two ports, simply click and drag from the No response output port of the Start block to the input port of the Closing response block.

At this point, you can complete the conversation flow in two different ways:

  • First, you can manually add the End conversation block and connect it to the Closing response block.
  • Alternatively, choose Save intent and then choose Confirm to have the builder create this block and connection for you.

After the intent is saved, choose Build and wait for the build to complete, then choose Test.

The bot will now properly greet the customer if an utterance matches this newly created intent.

Customer stories

NeuraFlash is an Advanced AWS Partner with over 40 collective years of experience in the voice and automation space. With a dedicated team of Conversational Experience Designers, Speech Scientists, and AWS developers, NeuraFlash helps customers take advantage of the power of Amazon Lex in their contact centers.

“One of our key focus areas is helping customers leverage AI capabilities for developing conversational interfaces. These interfaces often require specialized bot configuration skills to build effective flows. With the Visual Conversation Builder, our designers can quickly and easily build conversational interfaces, allowing them to experiment at a faster rate and deliver quality products for our customers without requiring developer skills. The drag-and-drop UI and the visual conversation flow is a game-changer for reinventing the contact center experience.”

The SmartBots ML-powered platform lies at the core of the design, prototyping, testing, validating, and deployment of AI-driven chatbots. This platform supports the development of custom enterprise bots that can easily integrate with any application—even an enterprise’s custom application ecosystem.

“The Visual Conversation Builder’s easy-to-use drag-and-drop interface enables us to easily onboard Amazon Lex, and build complex conversational experiences for our customers’ contact centers. With this new functionality, we can improve Interactive Voice Response (IVR) systems faster and with minimal effort. Implementing new technology can be difficult with a steep learning curve, but we found that the drag-and-drop features were easy to understand, allowing us to realize value immediately.“

Conclusion

The Visual Conversation Builder for Amazon Lex is now generally available, for free, in all AWS Regions where Amazon Lex V2 operates.

Additionally, on August 17, 2022, Amazon Lex V2 released a change to the way conversations are managed with the user. This change gives you more control over the path that the user takes through the conversation. For more information, see Understanding conversation flow management. Note that bots created before August 17, 2022, do not support the VCB for creating conversation flows.

To learn more, see Amazon Lex FAQs and the Amazon Lex V2 Developer Guide. Please send feedback to AWS re:Post for Amazon Lex or through your usual AWS support contacts.


About the authors

Thomas Rindfuss is a Sr. Solutions Architect on the Amazon Lex team. He invents, develops, prototypes, and evangelizes new technical features and solutions for Language AI services that improves the customer experience and eases adoption.

Austin Johnson is a Solutions Architect at AWS , helping customers on their cloud journey. He is passionate about building and utilizing conversational AI platforms to add sophisticated, natural language interfaces to their applications.

Read More

PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status is challenging. Even in clinical settings, HRV is sensitive to acute stressors such as physical activity, mental stress, hydration, alcohol, and sleep. Wearable devices provide convenient HRV measurements, but the irregularity of measurements and uncaptured stressors can bias conventional analytical methods. To better interpret HRV measurements for downstream healthcare applications, we…Apple Machine Learning Research

Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering

Teachers often conduct surveys in order to collect data from a predefined group of students to gain insights into topics of interest. When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report. In the analysis step, traditionally, the teacher has to read each of the responses and decide on how to group them in order to extract insightful information. Even though it is possible to group the responses only using certain keywords, such an approach would…Apple Machine Learning Research

Microsoft Research Summit 2022: What’s Next for Technology and Humanity?

Microsoft Research Summit setup 2022

Today, we are experiencing waves of breakthroughs in computing that are transforming just about every aspect of our lives. Artificial intelligence is changing the way we develop and create. Human language technologies are revolutionizing the workflows of healthcare professionals. Deep learning is accelerating our ability to understand and predict natural phenomena, from atomic to galactic scales. Meanwhile, the foundations of cloud computing are undergoing a reinvention from the atoms up. 

Realizing the benefits of these new breakthroughs demands that we come together in new ways across the global research community. The vibrancy of invention and innovation increasingly lies at the intersections among traditional research disciplines, from the highly theoretical and to the immediately applicable. Ensuring that the continuing advancement of technology is beneficial to all requires communication, collaboration and co-innovation across the communities that create new technologies and those that aim to use them to improve their lives. 

That’s why I’m excited to invite you to join us for this year’s Microsoft Research Summit, which will take place on October 18-20, 2022. This virtual event is where the global research community convenes to explore how emerging research might best address societal challenges and have significant impact on our lives in the coming years. This year’s event will feature over 120 speakers, including researchers and leaders from across the research community at Microsoft, alongside partners and collaborators from industry, academia and government who are advancing the frontiers of research in computing and across the sciences. 

Each of our three days will begin with a plenary session during which we’ll explore the potential impact of deep learning on scientific discovery, the opportunity to use technology to make healthcare more precise and accessible, and the re-invention of foundational technologies to enable the cloud of the future. These plenaries will lead into tracks that dive deeper into research that spans from more efficient and adaptable AI, to technologies that amplify human creativity and help foster a more sustainable society.

For further details – and to register to attend – check out the Microsoft Research Summit website

We hope you will join us. 

The post Microsoft Research Summit 2022: What’s Next for Technology and Humanity? appeared first on Microsoft Research.

Read More

CCF: Bringing efficiency and usability to a decentralized trust model

A list of bullet points describing key features of Confidential Consortium Framework: distributed trust, secure enclaves, no admin access, ledger approval by consensus, and flexible authentication and authorization. Next to them is a green and blue geometric sphere.

Online trust has come a long way since the time of centralized databases, where information was concentrated in one location and the security and validation of that information relied on a core set of people and systems. While convenient, this model of centralized management and oversight had a number of drawbacks. Trust depended on how the workflows of those systems were established and the skillset and integrity of the people involved. It created opportunities for such issues as duplicate digital transactions, human error, and bias, as witnessed in recent history in the financial industry. In response to these systemic issues, a now-famous paper published in late 2008 proposed a distributed ledger, where new transactions could be added and validated only through participant consensus. This model of decentralized trust and execution would become known as distributed ledger technology, or blockchain, and it offered a more trustworthy alternative to centrally managed databases and a new way to store and decentralize data.

In a distributed trust model, network participants validate transactions over a network by performing computation on those transactions themselves and comparing the outputs. While their identities are private and those performing the transactions typically have pseudonyms, the transactions themselves are public, greatly limiting the use cases for decentralized computation systems. One use case where decentralized computation doesn’t work involves handling financial transactions so that they’re compliant with Know Your Client (KYC) standards and anti-money laundering (AML) regulations while also respecting privacy laws. Another involves managing medical records, where multiple organizations, such as healthcare providers and insurers, jointly govern the system.

Distributed trust with centralized confidential computation 

While blockchain provided a more reliable option to centralized databases, it isn’t a perfect solution. The Confidential Computing team at Microsoft Research wanted to build a system that retained the advantages of decentralized trust while keeping transactions confidential. This meant we had to develop a way to centralize computation. At the time, no system offered these capabilities.

To tackle this issue, we developed Confidential Consortium Framework (CCF), a framework for building highly available stateful services that require centralized computation while providing decentralized trust. CCF is based on a distributed trust model like that of blockchain while maintaining data confidentiality through secure centralized computation. This centralized confidential computation model also provides another benefit—it addresses the substantial amount of energy used in blockchain and other distributed computation environments.

As widely reported in the media, blockchain comes at a great environmental cost. Cryptocurrency—the most widespread implementation of blockchain—requires a significant amount of computing power to verify transactions. According to the Cambridge Center for Alternative Finance (CCAF), bitcoin, the most common cryptocurrency, as of this writing, currently consumes slightly over 92 terawatt hours per year—0.41 percent of global electricity production, more than the annual energy draw of countries like Belgium or the Philippines.

Our goal was to develop a framework that reduced the amount of computing power it takes to run a distributed system and make it much more efficient, requiring no more energy than the cost of running the actual computation.

To apply the technology in a way that people can use, we worked with the Azure Security team to build Azure confidential ledger, an Azure service developed on CCF that manages sensitive data records in a highly secure way. In this post, we discuss the motivations behind CCF, the problems we set out to solve, and the approaches we took to solve them. We also explain our approach in supporting the development of Azure confidential ledger using CCF.

Icon depicting three separate nodes on a network that can communicate to one another but cannot read each other's data.

Overcoming a bias for blockchain 

We discovered a strong bias for blockchain as we explained our research to different groups that were interested in this technology, including other teams at Microsoft, academic researchers exploring blockchain consensus, and external partners looking for enterprise-ready blockchain solutions. This bias was in the form of certain assumptions about what was needed to build a distributed ledger: that all transactions had to be public, that computation had to be geographically distributed, and that it had to be resilient to Byzantine faults from executors. First recognizing these biases and then countering them were some of the biggest challenges we had to surmount.

We worked to show how CCF broke from each of these assumptions while still providing an immutable ledger with distributed trust. We also had to prove that there were important use cases for maintaining confidentiality in a distributed trust system. We went through multiple rounds of discussion, explaining how the technology we wanted to build was different from traditional blockchains, why it was a worthwhile investment, and what the benefits were. Through these conversations, we discovered that many of our colleagues were just as frustrated as we were by the very issues in blockchain we were setting out to solve.

Additionally, we encountered skepticism from internal partner teams, who needed more than a research paper to be convinced that we could successfully accomplish our research goals and support our project. There were healthy doubts about the performance that was possible when executing inside an encrypted and isolated memory space, the ability to build a functional and useable system with minimal components that needed to be trusted, and how much of the internal complexity it was possible to hide from operators and users. Early versions of CCF and sample apps were focused on proving we could overcome those risks. We built basic proofs of concept and gave numerous demonstrations showing how we could implement distributed trust with centralized confidential computation. In the end, it was the strength of these demos that helped us get the resources we needed to pursue our research.

Building the compute stack

Another challenge involved was reimagining a secure compute stack for an enclave—the secured portion of the hardware’s processor and memory. At the time, enclaves were very resource constrained compared with traditional hardware, and we could run only small amounts of code on very little memory.

In addition, capabilities are limited when performing computation in an enclave. For example, the code can’t access anything outside the enclave, and it’s difficult to get the code to communicate with an external system. This challenge required us to design and build an entire compute stack from scratch with all the elements needed to establish consensus, implement transactional storage, establish runtimes for user languages, and so on.

Another consideration was the need to build a system that people could use. As researchers, we wanted our work to have real impact, but it was tempting to push the state of the art in the area of confidential computing research and develop very elaborate technology in these enclaves. However, these types of innovations cannot be deployed in actual products because they’re exceedingly difficult to explain and apply. We had committed to creating something that product teams could implement and use as a foundation for building real systems and products, so we worked to calibrate the guarantees and threat model so that our system could be used in actual products.

Establishing a root of trust with CCF

CCF strengthens the trust boundary in scenarios in which both distributed trust and data confidentiality are needed by decreasing the size of the trusted computing base (TCB)—the components of a computing environment that must be trusted for the appropriate level of security to be applied—reducing the attack surface. Specifically, CCF allows operators to greatly decrease or even eliminate their presence in the TCB, depending on the governance configuration.

Instead of a social root of trust—such as a cloud service provider or the participant consensus used in blockchain networks—CCF relies on trusted hardware to enforce transaction integrity and confidentiality, which creates a trusted execution environment (TEE). These TEEs are isolated memory spaces that are kept encrypted at all times, even when data is executing. The memory chip itself strictly enforces this memory encryption. Data in TEEs is never readable.

Decentralized trust is underpinned by remote attestation, providing the guarantee to a remote entity that all computation of user data takes place in a publicly verifiable TEE. The combination of this attestation with the isolated and encrypted TEE creates a distributed trust environment. Nodes in the network establish mutual trust by verifying their respective attestations, which affirm that they’re running the expected code in a TEE. The operator starting the nodes, which can be automated or manual, indicates where in the network they can find each other.

Service governance is performed by a flexible consortium, which is separate from the operator. CCF uses a ledger to provide offline trust. All transactions are reflected in a tamper-protected ledger that users can review to audit service governance and obtain universally verifiable transaction receipts, which can verify the consistency of the service and prove the execution of transactions to other users. This is particularly valuable for users who need to comply with specific laws and regulations.

A circular flowchart connecting three ledgers, each marked with a padlock around a circle representing a confidential network. The arrows connecting the ledgers read
Figure 1: In a confidential network, data is encrypted at rest, in transit, and in use because it’s run in a trusted execution environment. All network administration occurs outside the trust boundary. The network constitution governs participants, configuration, and code, making it resilient to fraud, theft, or unintended data manipulation.

Laying the foundation for Azure confidential ledger

We collaborated with the Azure Security team to refine and improve CCF so that it could be used as a foundation for building new Azure services for confidential computing. We applied Azure API standards and ensured that CCF complied with Azure best practices, including enabling it to log operations and perform error reporting and long-running queries. We then developed a prototype of an Azure application, and from this, the Azure Security team developed Azure confidential ledger, the first generally available managed service built on CCF, which provides tamper-protected audit logging that can be cryptographically verified.

Looking forward

We were pleasantly surprised by how quickly we discovered new use cases for CCF and Azure confidential ledger, both within Microsoft and with third-party users. Now, most of the use cases are those we had not initially foreseen, from atmospheric carbon removal to securing machine learning logs. We’re extremely excited by the potential for CCF to have much more impact than we had originally planned or expected when we first started on this journey, and we’re looking forward to discovering some of the countless ways in which it can be applied.

The post CCF: Bringing efficiency and usability to a decentralized trust model appeared first on Microsoft Research.

Read More

Reinventing the Wheel: Gatik’s Apeksha Kumavat Accelerates Autonomous Delivery for Wal-Mart and More

As consumers expect faster, cheaper deliveries, companies are turning to AI to rethink how they move goods.

Foremost among these new systems are “hub-and-spoke,” or middle-mile, operations, where companies place distribution centers closer to retail operations for quicker access to inventory. However, faster delivery is just part of the equation. These systems must also be low-cost for consumers.

Autonomous delivery company Gatik seeks to provide lasting solutions for faster and cheaper shipping. By automating the routes between the hub — the distribution center — and the spokes — retail stores — these operations can run around the clock efficiently and with minimal investment.

Gatik co-founder and Chief Engineer Apeksha Kumavat joined NVIDIA’s Katie Burke Washabaugh on the latest episode of the AI Podcast to walk through how the company is developing autonomous trucks for middle-mile delivery.

Kumavat also discussed the progress of commercial pilots with companies such as Walmart and Georgia-Pacific.

She’ll elaborate on Gatik’s autonomous vehicle development in a virtual session at NVIDIA GTC on Tuesday, Sept. 20. Register free to learn more.

You Might Also Like

Driver’s Ed: How Waabi Uses AI, Simulation to Teach Autonomous Vehicles to Drive

Teaching the AI brains of autonomous vehicles to understand the world as humans do requires billions of miles of driving experience. The road to achieving this astronomical level of driving leads to the virtual world. Learn how Waabi uses powerful high-fidelity simulations to train and develop production-level autonomous vehicles.

Polestar’s Dennis Nobelius on the Sustainable Performance Brand’s Plans

Driving enjoyment and autonomous driving capabilities can complement one another in intelligent, sustainable vehicles. Learn about the automaker’s plans to unveil its third vehicle, the Polestar 3, the tech inside it, and what the company’s racing heritage brings to the intersection of smarts and sustainability.

GANTheftAuto: Harrison Kinsley on AI-Generated Gaming Environments

Humans playing games against machines is nothing new, but now computers can develop their own games for people to play. Programming enthusiast and social media influencer Harrison Kinsley created GANTheftAuto, an AI-based neural network that generates a playable chunk of the classic video game, Grand Theft Auto V.

Subscribe to the AI Podcast: Now Available on Amazon Music

The AI Podcast is now available through Amazon Music.

In addition, get the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn.

Make the AI Podcast better: Have a few minutes to spare? Fill out this listener survey.

 

The post Reinventing the Wheel: Gatik’s Apeksha Kumavat Accelerates Autonomous Delivery for Wal-Mart and More appeared first on NVIDIA Blog.

Read More

How our principles helped define AlphaFold’s release

How our principles helped define AlphaFold’s release

Our Operating Principles have come to define both our commitment to prioritising widespread benefit, as well as the areas of research and applications we refuse to pursue. These principles have been at the heart of our decision making since DeepMind was founded, and continue to be refined as the AI landscape changes and grows. They are designed for our role as a research-driven science company and consistent with Google’s AI principles.Read More