Tag: AI

Revolutionizing Design: An In-Depth Look at Claude Design by Anthropic Labs

Revolutionizing Design: An In-Depth Look at Claude Design by Anthropic Labs

In the dynamic landscape of design and creative collaboration, Anthropic Labs has unveiled a groundbreaking tool — Claude Design. Launched with the aspiration to democratize design capabilities, Claude Design empowers users from various backgrounds to create, refine, and share polished visual work effortlessly. At its core, Claude Design is powered by the robust Claude Opus 4.7 vision model and is now available in a research preview for Claude Pro, Max, Team, and Enterprise subscribers. This strategic rollout aims to revolutionize how visual content is developed and shared within organizations.

Claude Design addresses a critical challenge in the design process: the limitation of exploration due to time constraints. Traditionally, designers have had to ration their creative endeavors due to resource limitations, but Claude Design is set to change this narrative. It offers both experienced designers and non-designers, such as founders, product managers, and marketers, a comprehensive platform to turn their ideas into tangible visual assets. Starting with a simple descriptive input, the tool generates a preliminary design, which users can then refine through interactive conversations, inline comments, direct edits, or customized sliders. Moreover, Claude seamlessly aligns with your organization’s design system, ensuring visual consistency across all projects.

Among its diverse applications, Claude Design shines in creating realistic prototypes. Designers can transform static mockups into interactive prototypes with ease, facilitating user testing and feedback gathering without the need for a single line of code. Product Managers benefit similarly, as they can craft detailed product wireframes and mockups for further refinement or handoff to development teams via Claude Code.

The tool is equally transformative for founders and marketers. It enables the swift creation of on-brand pitch decks and presentations, starting from a rough outline to a polished product within minutes, complete with export options to PPTX and Canva. Marketing teams can produce compelling landing pages, social media assets, and campaign visuals in collaboration with designers.

Claude Design also breaks new ground in frontier design, offering a platform where anyone can build code-powered prototypes enriched with advanced features such as voice, video, shaders, 3D elements, and built-in AI. This feature positions Claude Design as a critical tool for innovative and tech-driven design solutions.

The operational flow of Claude Design is intuitive. Upon onboarding, Claude constructs a dedicated design system tailored to your team’s branding guidelines by scanning codebases and existing design files. This automation ensures that subsequent projects adhere to your brand’s color palette, typography, and design components. Furthermore, users can refine this system over time, maintaining multiple systems as needed.

Claude Design supports diverse starting points. Whether you begin from a text prompt, upload existing assets, or capture elements directly from your website, the tool integrates these seamlessly into your design workflow. Its fine-grained controls allow for precise adjustments, while its collaboration features enable seamless organizational sharing and editing.

Once your design reaches fruition, Claude Design facilitates easy sharing and export options, supporting formats like Canva, PDF, PPTX, and standalone HTML files. For development-ready designs, Claude packages all necessary elements into a handoff bundle for smooth transition to Claude Code, ensuring a seamless move from design to implementation.

Anthropic Labs is committed to expanding Claude Design’s integrations, allowing teams to synchronize with existing tools for an enhanced workflow. As accessibility broadens, organizations are invited to integrate Claude Design into their creative arsenal, harnessing its full potential to transform the way they visualize and actualize their ideas. For Enterprise users, Claude Design remains off by default until activated by an admin, underscoring tailored control and security within organizational settings. This strategic launch of Claude Design embodies a new era in design innovation, paving the way for creativity without constraints.

For more information on Claude Design by Anthropic Labs, visit the official article: Claude Design by Anthropic Labs.

April 18, 2026 AI-Powered Tools, Artificial Intelligence (AI), Generative AI No comments yet

The Open-Source Revolution: Sarvam AI’s 30B and 105B Models

The Open-Source Revolution: Sarvam AI’s 30B and 105B Models

Sarvam AI stands at the forefront of innovation, driven by the mission to deliver advanced artificial intelligence solutions through locally cultivated efforts within India. Established with the vision to blend technology and indigenous expertise, Sarvam AI focuses on developing scalable and impactful AI models. Operating under the IndiaAI initiative, the company is committed to facilitating cutting-edge advancements in artificial intelligence, ensuring its technologies remain accessible and beneficial to both local and global community. The recent open-source release of their pioneering models, Sarvam 30B and 105B, underscores their dedication to propelling India’s capabilities and prominence on the international AI stage.

In an exciting development on March 6, 2026, Sarvam AI introduced their groundbreaking AI models, Sarvam 30B and Sarvam 105B, to the global community. This endeavor, entirely developed in India under the ambitious IndiaAI mission, marks a pivotal moment in the AI landscape with its open-source release. The models signify a comprehensive full-stack effort in AI creation, leveraging indigenous resources from tokenization to inference deployment.

Sarvam 30B and 105B are engineered to offer advanced reasoning capabilities, having been trained on extensive, high-quality datasets native to India. These models are designed for scalable deployment across a variety of hardware platforms, from high-end GPUs to personal devices, ensuring efficient performance paired with minimal computational overhead.

Sarvam 30B facilitates Samvaad, a conversational agent platform, while Sarvam 105B serves as the core for Indus, an AI assistant engineered for handling complex workflows. Internationally competitive, both models excel particularly in Indian languages, even surpassing larger models on language benchmarks due to their optimized tokenization approach.

The architecture of these models embraces a Mixture-of-Experts (MoE) framework, which employs sparse expert routing and attention mechanisms, effectively managing parameter scaling challenges. The training comprised several phases, integrating diverse sources including code, multilingual content, and mathematical data, with a pronounced focus on Indian languages. This approach ensured a robust and wide-ranging informational foundation.

Fine-tuning involved high-quality prompts across domains, refining the models’ abilities to navigate intricate tasks. Safety fine-tuning specifically addressed India-centric risks, ensuring responses are culturally and relevantly aware. Reinforcement learning further enriched their capabilities, focusing on diverse prompt handling, structured responses, correct reasoning, and tool utilization.

Notably, Sarvam 105B distinguishes itself with formidable performance across knowledge domains, achieving top-tier results in multiple benchmarks. The models underscore an investment in the Indian AI ecosystem, showcasing strong capacities in Indian languages and optimized economic viability for deployment—Sarvam 30B designed for varied inference deployments, while Sarvam 105B tailored for server-based operations, maximizing efficiency and throughput.

This release is not merely technical; it signifies a strategic push towards sovereign AI technologies in India. Sarvam AI extends global outreach by offering model weights and API access, intending to provide foundational infrastructure for advancing future AI innovations within the country. Supported extensively by the Indian government and in collaboration with Nvidia, these models symbolize a significant technical milestone and a strategic vision toward AI autonomy.

Looking ahead, Sarvam AI aspires to scale these efforts, utilizing the developed infrastructure and expertise to train even more sophisticated models. This initiative heralds a promising future for AI advancements, both within India and globally, reinforcing India’s position as a prominent player in the AI domain.

March 27, 2026 Artificial Intelligence (AI), Artificial Intelligence (AI), Deep Tech No comments yet

The End of Sora: OpenAI’s Strategic Shift

The End of Sora: OpenAI’s Strategic Shift

The recent shutdown of OpenAI’s video generation model, Sora, marks a pivotal moment in the company’s strategic shift towards more promising ventures in the advancing field of AI. The decision to retire Sora, which once embodied OpenAI’s creative ambitions in generative video technology, signals the onset of a broader, more calculated approach focused on core products and sustainability.

The Rise and Challenges of Sora

Launched in September 2025, Sora’s debut was nothing short of spectacular. The application quickly soared to the top of Apple’s App Store charts and amassed over a million downloads in under five days. Its capability to generate realistic, cinematic video clips from text prompts captivated users and skyrocketed its popularity. However, the rapid rise came with significant challenges. OpenAI grappled with content regulation as users started creating videos featuring intellectual property, like Pokémon characters, and historical figures in unauthorized contexts. This led to the introduction of protective measures to curb such misuse.

Moreover, OpenAI found itself embroiled in legal skirmishes, notably with Cameo, over trademark issues related to Sora’s features. Despite efforts to address these hurdles, they highlighted the underlying complications associated with video generation models. Such legal and ethical concerns raised questions about sustainable operational models, considering the costly nature of running such advanced AI technologies at scale.

OpenAI’s Strategic Realignment

The choice to discontinue Sora underscores a strategic realignment undertaken by OpenAI. As the company prepares for potential initial public offerings (IPO), it is prioritizing the enhancement and monetization of its principal AI models. This pivot entails a more profound focus on emerging areas like robotics and world simulations that promise real-world applications and profitable, long-term returns.

Fidji Simo, the new product head hired by OpenAI CEO Sam Altman, has clearly articulated a keen focus on steering the company away from peripheral projects, like Sora, towards optimizing its primary business targets. Simo’s appointment reads as a commitment to consolidating the company’s flagship models and ensures they remain fiscally viable and impactful in a burgeoning, yet competitive, AI landscape.

Partnerships and Future Focus

This decisive move is also reflective of broader market dynamics and partnerships shaping OpenAI’s trajectory. A noteworthy collaboration with The Walt Disney Company solidifies OpenAI’s stake in valuable content licensing deals. Disney’s $1 billion investment reflects trust in OpenAI’s future pursuits, even as it steps back from video generation. This partnership illustrates to potential investors that OpenAI’s calibrated focus aligns with significant industry players’ interests, paving the way for expanded cooperation in applying AI technologies responsibly and innovatively.

Conclusion

OpenAI’s revised focus, while perhaps disappointing to advocates of video generation technologies, is not without merit. Robotics and AI-assisted real-world solutions present prospective markets and align with OpenAI’s mission to directly impact societal problems. By refining resource allocation towards these ends, OpenAI is setting a course for achieving scalable impact and ensuring its models’ technological and economic sustainability. In retrospect, Sora’s journey from breakthrough success to a quiet halt reflects the trials inherent in pioneering frontiers of AI. OpenAI’s pivot from Sora to more promising, integrated AI initiatives showcases agility and strategic foresight, navigating the AI domain with judicious anticipation of future trends in artificial intelligence and automation. Sora’s shutdown, while a momentous decision, symbolizes a broader narrative of innovation, collaboration, and continued evolution in the AI sphere.

March 25, 2026 AI-Powered Tools, Artificial Intelligence (AI), Generative AI No comments yet

Introducing MAI-Image-2: A Leap Forward in Text-to-Image Technology

In the dynamic world of artificial intelligence, innovations emerge with awe-inspiring regularity. Today, Microsoft proudly announces the launch of MAI-Image-2, which has shot to the rank of the third-best text-to-image model family on the Arena.ai leaderboard. This leap forward places Microsoft alongside industry giants in the realm of creative AI tools.

Central to this breakthrough is the MAI Playground, an interactive platform where creatives can test drive the latest iterations of Microsoft’s AI models. Beyond just testing, the Playground serves as a feedback conduit directly to Microsoft’s developers, ensuring that user insights fuel future enhancements.

Built for Creatives, Guided by Creatives

The development journey of MAI-Image-2 was marked by deep collaboration with photographers, designers, and visual storytellers. These conversations illuminated areas where AI could truly transform everyday creative workflows. The result is a tool finely tuned to meet the nuanced demands of visual artistry.

Enhanced Photorealism and Realistic Text Generation

At the heart of MAI-Image-2 is its extraordinary ability to render photorealistic images replete with natural lighting and life-like skin tones. Environments are crafted to feel authentic, reducing the need for extensive post-production edits. This realism ensures that creatives can invest more time in conceptualization rather than correction.

A distinctive feature is its capability for reliable in-image text generation. Whether it’s a movie poster title or a subtle street sign in a cinematic scene, MAI-Image-2 excels in producing text that feels integrated and intentional. This opens new avenues for creators to generate infographics, presentations, and visual narratives with minimal friction.

Rich, Detail-Oriented Scene Creation

Beyond realism, MAI-Image-2 caters to creative extremities – from surreal dreamscapes to opulent compositions. Its ability to generate rich, detailed environments makes it a preferred choice for artists challenging the boundaries of imagination. By transforming fantastical concepts into tangible imagery, it empowers creators to explore uncharted aesthetic territories.

Commercial and Developer Access

Beginning its rollout on platforms like Copilot and Bing Image Creator, MAI-Image-2’s reach is expanding. For businesses like WPP that require scalable image generation solutions, API access is already available. Moreover, a broader invitation is extended to developers through Microsoft Foundry, promising a wave of innovative applications across industries.

Businesses eager to harness MAI-Image-2 for commercial purposes are invited to apply for access, ensuring that this technological marvel is also a business enabler.

The Road Ahead: Pioneering with Superintelligence

Microsoft’s AI Superintelligence team assures there’s much more to anticipate. With the new GB200 cluster operational, the roadmap for MAI presents untapped potentials. Collaborating closely with product teams, MAI models are being positioned to impact billions, fostering creativity and innovation at an unprecedented scale.

Join the Movement

Microsoft extends an open invitation to brilliant, motivated individuals with a low ego and a high ambition. If you resonate with this ethos, the team offers an exciting frontier in AI innovation waiting to be explored. As they work on the next generation of models, the doors are open for those ready to leave a mark on the AI landscape.

As MAI-Image-2 rolls out to users worldwide, the call is not just to witness but to participate actively in its evolution. Whether through feedback in the Playground or commercial applications, every user contributes to a model that is as collaborative as it is powerful. The promise of AI-driven creativity is no longer a distant vision—it is here, ready and waiting in the form of MAI-Image-2.

For more details, visit the original article here.

March 21, 2026 AI-Powered Tools, Artificial Intelligence (AI), Generative AI No comments yet

Unlocking AI’s Future with NVIDIA’s NemoClaw: A Leap Towards Safety and Privacy

Unlocking AI’s Future with NVIDIA’s NemoClaw: A Leap Towards Safety and Privacy

In an era defined by artificial intelligence (AI) and digital transformation, the importance of safety and privacy cannot be overstated. NVIDIA, a vanguard of technological innovation, understands this intricate balance more than most. Their latest development, NemoClaw, epitomizes their commitment to enhancing AI systems with unparalleled security and privacy protocols. This open-source stack, a sophisticated complement to OpenClaw, is set to redefine the paradigms of AI-driven technology, addressing the core concerns of privacy and data management in unprecedented ways. Read more about NemoClaw here.

The Dawn of a Sophisticated Security Architecture

NemoClaw’s introduction represents a leap forward in the realm of AI security. As AI systems become inherently more complex, their ability to self-evolve opens myriad opportunities—and risks. NemoClaw mitigates these risks by embedding advanced security measures into the fabric of AI operations. It integrates seamlessly with NVIDIA’s Agent Toolkit software, enhancing the security and efficacy of OpenClaw systems. This synergy facilitates robust privacy enforcement and the establishment of stringent security policies that govern AI behavior, turning potential vulnerabilities into strengths.

Empowering Users Through Control

One of the fundamental achievements of NemoClaw lies in empowering users with control over AI behavior and data sovereignty. In an age where data privacy concerns dominate global discourse, NemoClaw positions itself as a guardian of ethical AI deployment. By enabling user-defined control, it adheres to the principles of transparency and accountability, ensuring that AI systems act in accordance with user expectations and ethical norms. This capability is not merely a technological feat; it is a cornerstone of responsible AI development, promising users peace of mind alongside cutting-edge innovation.

Balancing Innovation and Ethics

With NemoClaw, NVIDIA addresses the delicate balance between innovative functionalities and stringent security requirements. This framework does not just provide security; it catalyzes comprehensive AI operations, ensuring they are grounded in ethical standards. The open-source nature of NemoClaw allows for continuous evolution and enhancement, making it adaptable to emerging technologies and threats. In doing so, NVIDIA sets a precedent for industry standards, sparking a global conversation on the future of AI safety and privacy.

A Use Case: Secure AI in Autonomous Environments

Imagine a network of autonomous vehicles operating within a bustling urban environment. These vehicles must navigate complex traffic scenarios, communicate with infrastructure, and adapt to dynamic changes, all while protecting sensitive data and ensuring passenger safety. Here, NemoClaw offers a transformative solution. By implementing NemoClaw, autonomous systems can leverage self-evolving AI models under the guidance of user-defined security protocols. This not only enhances operational efficiency but also safeguards critical data assets and maintains user privacy. NemoClaw ensures that these vehicles make real-time decisions that are both ethical and secure, fostering an environment of trust and reliability.

Influencing Global Standards

NVIDIA’s initiative with NemoClaw extends beyond technological innovation; it is a catalyst for evolving industry standards and shaping user expectations worldwide. The ethical deployment of AI is rapidly becoming a non-negotiable aspect of technological advancement. By leading this charge, NVIDIA encourages a paradigm shift towards transparent, accountable, and secure AI systems. Their efforts underscore the importance of building technologies that serve societal needs while ensuring those needs are met in a safe and private manner.

A Vision for the Future

Looking forward, NVIDIA’s NemoClaw represents a vision for the future of AI—one that is deeply intertwined with safety, privacy, and ethical considerations. It encourages developers, businesses, and consumers to engage in a dialogue on how AI can be utilized to enhance lives without compromising on critical values. NemoClaw is more than a technological advancement; it is a movement towards responsible AI implementation, championing the notion that future technologies must prioritize human-centric values.

Conclusion

As the world moves deeper into the age of AI, NVIDIA’s NemoClaw emerges as a beacon of how technology can be both advanced and safe. It offers a framework where security and privacy are not just additions but integral components of the AI lifecycle. For businesses and developers navigating the complexities of AI, NemoClaw provides the toolkit necessary to build systems that are ethical, secure, and user-focused. In embracing NemoClaw, stakeholders are investing not only in technology but in a future where AI serves humanity with integrity and trust.

March 17, 2026 AI-Powered Tools, Artificial Intelligence (AI), Artificial Intelligence (AI) No comments yet

The Allure and Pitfalls of Vibe-Coded Apps

The Allure and Pitfalls of Vibe-Coded Apps: Why You Should Reconsider Paying for Them

The burgeoning landscape of app development is witnessing a novel trend: vibe-coded apps. Essentially crafted using artificial intelligence and minimal developer intervention, these apps are captivating due to their simplistic production process. Yet, despite their allure, they present several pronounced risks that potential buyers should be wary of.

One Prompt Away from Compromise: The Security Risks

At the heart of vibe-coded apps lies AI’s ability to generate fully-functioning applications through mere textual prompts. This ease of creation has meant anyone can fashion an app that seems impressive at face value. However, AI, as intelligent as it is, has limitations—particularly hallucinations that can result in incorrect or unreliable code. When buying an app developed without traditional coding oversight, users risk compromising their data security. Stories abound of vibe-coded apps storing user passwords in plaintext or featuring broken authentication systems due to flawed AI-generated code.

The Unchecked Work: Closed Source Concerns

A key concern levelled against vibe-coded apps revolves around their often closed-source nature. Unlike open-source software, which benefits from communal scrutiny and collaboration, closed-source vibe-coded apps remain cryptic. This opacity means zero accountability, with no practice of code validation. Developers themselves might have minimal understanding of the underlying code, leading to unchecked, potentially harmful applications being monetized and distributed.

Build in a Weekend: A Warning Rather than a Boast

Ever come across an app promoted with statements such as being built in a weekend or solo within 48 hours? Rather than being laudable, this indicates a rushed product potentially lacking rigorous testing and vulnerability assessments. Reliable applications demand time, care, and thorough testing, something vibe-coded creations often lack. Users might find themselves dealing with apps that fail spectacularly when asked to perform beyond the developer’s brief testing scenarios.

AI-Generated Apps Can Be Obscured: Red Flags

Not all vibe-coded apps showcase their genesis through AI models. Some savvy AI-utilizing developers polish these apps to professional standards, making them indistinguishable from traditional, manually-coded applications. However, subtle signs often surface when associated promotional materials also appear AI-generated. Such posts exhibit a distinct tone, commonly lacking depth and authenticity, thereby hinting at the app’s AI-crafted nature.

DIY Made Easy: Why Buy When You Can Create?

Perhaps one of the strongest arguments against purchasing vibe-coded apps is accessibility; if a developer can build it with AI, so can you. While your outcome may harbor similar risks, the knowledge of these pitfalls can aid you in refining functionalities and bolstering security for personal use. Altering the app to suit your needs may involve eliminating unsafe features, allowing for a secure, custom-made product irrespective of coding acumen.

Knowing the Limitations: The Place of Vibe Coding

Vibe coding, despite its risks, has a designated space within technological innovation. With adequate oversight, it provides a platform for rapid prototyping and exploration. Hobbyists can enjoy tinkering with ideas without starting from scratch, appreciating the simplicity AI promises. However, the end products, particularly when monetized and distributed, warrant caution.

Conclusion: Buyer Beware but Creator Empowered

In conclusion, vibe-coded apps, while novel and interesting, are often not what they seem. Their surface-level allure masks significant security vulnerabilities, lack of proper validation, and potential for misuse. Potential buyers should exercise caution and critically evaluate what they’re paying for, considering the security and reliability of the product. Moreover, the democratization of app creation through AI heralds a shift towards personal empowerment in tech, allowing would-be buyers to feasibly become creators. As AI continues reshaping tech paradigms, users and developers must navigate these changes with informed care, proactively safeguarding personal and communal digital terrains.

March 17, 2026 AI-Powered Tools, Application Development, Generative AI No comments yet
defining new normals in CRM - 4 trends in spotlight

Defining the new normal in CRM – 4 trends in the spotlight

Do you remember the times we used to go to our neighborhood mom and pop stores for some quick errands? The shopkeeper recognized and greeted us, enquired about our family’s well-being, offered the product we regularly bought, and if the product was unavailable at that moment, offered to deliver it at our house at no additional cost.

And he did that for all his regular customers.

An excellent way to establish rapport and nourish a healthy relationship with customers, through personalization.

That’s exactly what CRM is about in 2017, only businesses do it these days with the help of computers and internet, for millions of such customers. Today, customers not only knock business’ brick-and-mortar doors, but also, online – via social media, apps, websites, and the latest addition to the CRM kitty, chatbots.

What drives CRM in 2017?

Rise of the bots

Chatbots are perhaps the next big thing after apps. Every brand now has one in place – whether on its website or on social media sites such as Facebook Messenger. These bots act as lead generators, capturing customer queries and providing them quick information.

Here’s the Adidas Women UK Chatbot that promotes fitness among women and helps them book fitness sessions.

CRM

We spoke more about bots in our previous blogs, ‘Chatbots – a botched play or a game changer?’, and ‘Batman or Superman – What should your business’ chatbot be?’

Smarter Mobile CRM

Mobile CRM is no longer just providing data on salesperson’s smartphones, online and offline. Some of the new features in Mobile CRM apps are:

  • Geolocation – Enables salesperson to identify which customers are in their vicinity, allowing them to drop by for quick, meaningful visits. This leads to effective usage of the salespersons work hours.
  • AI – Smart assistants pull customer data from their social profiles to glean a deeper understanding of customers. This helps the salesperson stay informed on the customer and personalize the pitch better.

Data-driven Personalization

Personalization is more than a “Hi *|FNAME|* ” in your mass mailers these days. Brands are leveraging machine learning to analyze customer data and identify patterns in behaviour. This helps them recommend the right products at the right time to the right customer.

For example, brands can use geofences to identify customers in store vicinity, pull their data from the CRM to analyze past purchases and push customized offer messages to their smartphones.

Leveraging Crowdsourcing

Business are deepening engagement with customers via crowdsourcing. Customers, ranging from various demographic segments are pulled together for surveys, polls, discussions, and online forums to provide feedback on new products, services and more. This helps customers feel more invested in the process, while it enables businesses to directly engage with customers and gain insights for their product roadmap.

A company that put crowdsourcing to good use is perhaps Lego Ideas. It is a website that acts as a forum for Lego fans to come together and suggest new Lego set ideas and designs. Have a look at their portal!

Lego Ideas - crowdsourcing

CRM has undergone a sea change in the last few years, and with technological advancements in AI and machine learning, the transformation will occur at an exponential rate. Businesses, globally, are recognizing the benefits of leveraging these advanced CRM features to create exceptional customer experiences, and widen mindshare as well as wallet share.

 

Automated CRM, powered by machine learning that helps your salespeople do what they do best. Score more deals. Contact AgilizTech to learn more.

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August 23, 2017 CRM No comments yet

Batman or Superman – What should your business’ chatbot be?

The Batman vs Superman battle continues, but this time in the realm of chatbots.

The dark knight is admired by many as he is the superhero sans any superpowers. He can’t fly, he doesn’t have immeasurable strength. But what he does have – excellent deduction skills, martial arts prowess and of course, tons of money. And in today’s world, Batman can exist.

Superman, on the other hand, is an extra-terrestrial messiah, who has extraordinary strength and can fly around (no thanks to the cape).

And when we take this analogy to chatbots, Batman is our trusted Retrieval model chatbot, while Superman is the Generative model.

We spoke about chatbots in our previous post, Chatbots – a botched play or a game changer?, and in this post, we dive a bit deeper.

Now what are retrieval and generative models?

Retrieval Chatbots

Retrieval chatbots can be understood in the form of a database and queries. There are scripted answers to scripted or near scripted questions. Let’s take an example.

Josh – the book recommending bot

Josh is a virtual assistant bot that suggests books depending on the genre entered by the user. Ben wants to gift his 9-year old niece a book. Watch how Ben (the user) interacts with Josh.

Retrieval Chatbot example - Josh

Here, the chatbot is retrieving titles as per the genres listed in its database and suggesting books to the user. This is a matching mechanism at work. A query is fired and that fetches a response from the database. Easy, peasy.

But many bots are failing this spectacularly, by not being able to tackle out of syllabus questions. Or not being able to empathize, to understand sarcasm or the worst of all, irony.

Retrieval bots are slowly improving and with breakthroughs in NLP, we might just be able to make them work better.

Generative Chatbots are different in the sense that they don’t follow the script.  They communicate with human users and learn to think on their feet and offer new lines.

Alice is an excellent example of an artificial intelligence bot that can have a fairly reasonable conversation with humans. No wonder, Alice won the Loebner prize thrice! Apple’s Siri is another amazing goal-based dialog agent. But these agents follow given heuristic patterns. Generative chatbots are those that use probabilistic techniques on existing data and create new lines. Deep Neural Network is the breakthrough technology that is helping shape generative chatbots, such as the LnH.

A Twitterbot , the LnH: The Band can compose on-demand new music based on the genre entered by the user. It has created 700 such new songs!

 

Let’s now look at another example of generative chatbots – Microsoft Tay. Tay, for those who aren’t aware, was the conversational AI chatbot that interacted with Twitter users and learnt with each tweet. It generated new content on its own, depending on what was tweeted to it. It was going pretty well, until people started training it to post racist comments.

A timeline view of Tay going from angelic to NSFW:

It becomes especially difficult when the chatbot has an open domain setting, that is, in the absence of a very specific goal. The chatbot cannot be programmed for just a few keywords and must communicate intelligently with the human on a larger set of topics. It sounds impossible, but research on deep learning is still on to make generative models work. The who’s who of the tech world – Google, Facebook, IBM and Microsoft – is piling up billions of dollars to solve the question mystifying us all – intelligence.

What chatbot should my business adopt?

It may seem like a fire or frying pan situation as Generative Chatbots are unpredictable and Retrieval Chatbots don’t know to handle irregular situations. So, the best solution, that businesses ideally ought to follow are a combination of Retrieval and Generative chatbots. Superman and Batman combined?

Excellent deduction plus massive strength?

Since that may be awhile in the oven, businesses are now going the retrieval mode. We looked at some of the top businesses out there using retrieval chatbots:

  1. Burberry

A renowned name in luxury fashion, they spare no expense for their bot, the Burberry Messenger Bot, either. Users can enter product keywords and browse through new products.

The chatbot displays a teaser video and provides a key button to touch. This speaks volumes of how the brand is trying to increase the bot’s appeal and thereby enhance customer engagement.

2. Dominos

A bigshot in the fast-food industry, Dominos helps its customers order pizzas using its chatbot for door delivery and carryout. The customer can now order via the Messenger chatbot and just pick up the order from the outlet when it is ready. The bot also offers order tracking facilities so that you don’t need to keep calling the delivery guy a hundred times.

 

3. Ebay

Conversational Commerce anyone? Ebay is on Messenger now, and it offers personal shopping assistance in the form of ShopBot.

Batman, that is, retrieval chatbot seems to be a safe bet – it is trained to answer specific questions and achieve a specific goal. You can be assured of not finding any grammatical errors, but be ready to face some annoyed customers when they at times it prompts “I am unable to understand that.”, or something of that sort. Retrieval chatbots do not yet possess artificial intelligence and it may seem like ‘the person is there but the lights are dim’. But the most important point is that they can be trained better using NLP and Machine Learning. Generative Chatbots are still not there yet. The work is on, and we are on the brink of technological advancements that can make these chatbots come alive with intelligence of their own.

At AgilizTech, we believe that chatbots are going to be the medium of B2C and B2B conversation in the near future. We’re exploring this exciting new realm of possibilities and are gearing up to leverage AI and Machine Learning to build revolutionary chatbots.

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August 1, 2017 Chatbot No comments yet
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