Organizations of all sizes are racing to deploy generative AI to help drive overall efficiency and remove costs from their businesses. As with any new technology, deployment generally comes with barriers that can stall progress and implementation timelines.

LXT’s latest AI maturity report reflects the views of 315 executives working in AI and reveals the top bottlenecks companies face when deploying generative AI.

These include:

• Security and privacy concerns 
• Accuracy of the output of the solution 
• Availability of quality training data 
• Fine-tuning the foundational model 
• Prompt tuning 
• Accuracy of foundational models 

Only 1% of respondents stated they were not experiencing any bottlenecks with their generative AI deployments.  

1. Security and privacy concerns

39% of respondents highlighted security and privacy concerns as their top bottleneck in deploying generative AI. This is not surprising as generative AI models require a large amount of training data. Companies must ensure proper data governance to avoid exposing personal data and sensitive information, such as names, addresses, contact details, and even medical records.

Additional security concerns for generative AI models include adversarial attacks where bad actors cause the models to generate inaccurate and even harmful outputs. Finally, care must be taken to ensure that generative AI models do not generate content that mimics intellectual property which could lead to legal hot water.

Companies can mitigate these risks by obtaining consent from anyone whose data is being used to train its generative AI models, similar to how consent is obtained when using individuals’ photos on websites. Additional data governance procedures should be implemented as well, including data retention policies and redacting personally identifiable information (PII) to maintain individual confidentiality.

2. Accuracy of the output of the solution

Neck and neck with security and privacy concerns, 38% of respondents stated that the accuracy of generative AI’s output is a top challenge. We’ve all seen the news articles about chatbots spewing out misinformation and even going rogue.

While generative AI has immense potential to streamline business processes, it needs guardrails to eliminate hallucinations, ensure accuracy, and maintain customer trust. For example, companies should have 100% clarity on the source and accuracy of the data being used to train their models and should maintain documentation on these sources.

Further, human-in-the-loop processes for evaluating the accuracy of training data, as well as the output of generative AI systems, are crucial. This can also help reduce data bias so that generative AI models operate as intended.

3. Availability of quality training data

High-quality training data is essential, as it directly impacts AI models’ reliability, performance, and accuracy. It allows models to make better decisions and create more trustworthy outcomes.

36% of respondents in LXT’s survey stated that the availability of high-quality training data is a challenge with generative AI deployments. Recent press even has highlighted that human-written text could be used up for chatbot training by 2023. This bottleneck prevents companies from being able to scale their models efficiently, which then impacts the quality of their output. 

When it comes to deploying any AI solution, the data needed to train the models should be treated as an individual product with its own lifecycle. Companies should be deliberate about planning for the amount and type of data they need to support the lifecycle of their AI solution. A data services partner can provide guidance on data solutions that will create optimal results.

4. Fine-tuning the foundational model

Fine-tuning includes improving open-source, pre-trained foundation models, which often incorporate instructional fine-tuning, reinforcement learning with human or AI feedback (RLHR/RLAIF), or domain-specific pre-training.

32% of respondents stated that fine-tuning the foundational model can be a challenge when deploying generative AI, as it requires a deep understanding of large foundation model (LFM) training improvements and transformer models. To fine-tune models, companies must also have employees who can speed up training processes using tools for multi-machine training and multi-GPU setups, for example.

However, despite its potential gains, fine-tuning has several issues. Post-training inaccuracies can increase, the model can be overloaded with large domain-specific corpora and have minimized generalization ability, and more.

To combat potential issues in fine-tuning models, reinforcement learning and supervised fine-tuning are useful methods, as they help to remove harmful information and bias from responses that LLMs generate.

5. Prompt tuning

Prompt tuning is a technique that adjusts the prompts that inform a pre-trained language model’s response without a complete overhaul of its weights. These prompts are integrated into a model’s input processing. 24% of respondents stated that prompt tuning presents challenges when deploying generative AI.

Prompt tuning is just one method that can be used to improve an LLM’s performance on a task. Fine-tuning and prompt engineering are other methods that can be used to improve model performance, and each method varies in terms of resources needed and training required.

In the case of prompt tuning, this method is best suited for maintaining a model’s integrity across tasks. It does not require as many computational resources as fine-tuning and does not require as much human involvement compared to prompt engineering. 

Companies deploying generative AI should evaluate their use case to determine the best way to improve their language models.

6. Accuracy of foundational models

23% of respondents in LXT’s survey said that the accuracy of foundational models is a challenge in their generative AI deployments. Foundational models are pre-trained to perform a range of tasks and are used for natural language processing, computer vision, speech processing, and more.

Foundational models provide companies with immediate access to quality data without having to spend time training their model and without having to invest as heavily in data science resources.

There are some challenges with these models, however, including lack of accuracy and bias. If the model is not trained on a diverse dataset, it won’t be inclusive of the population at large and could result in AI solutions that alienate certain demographic groups. It’s critical for organizations using these models to understand how they were trained and tune them for better accuracy and inclusiveness.

Get more insights in the full report 

With the rapidly evolving field of AI, keeping up-to-date with the trends and developments is essential for success in AI initiatives. LXT’s Path to AI Maturity report gives you a current picture of the state of AI maturity in the enterprise, the amount of investment made, the top use cases for generative AI, and much more.

Download the report today to access the full research findings.

Gradient emerges as a pivotal player in artificial intelligence, offering innovative solutions that automate business operations across diverse industries.

Catering to a broad spectrum of enterprises, Gradient has carved a niche in highly regulated, data-rich sectors like financial services and healthcare.

By leveraging their long-context, domain-specific models – Albatross for Financial Services and Nightingale for Healthcare – Gradient empowers businesses to simplify complex processes, ensuring efficiency and compliance.

In this article, we delve into the unique facets of Gradient's offerings, exploring how they streamline AI integration, enhance productivity, and pave the way for future collaborations between humans and AI in the enterprise space.

Let's jump into it. 👇

Target market focus

Can you elaborate on the specific segments within the enterprise AI space that Gradient caters to? For instance, is there a focus on specific industries or company sizes?

Gradient automates business operations within enterprises across every industry, working directly with operational leaders and technical teams. 

While Gradient has a diverse portfolio of customers that range in size and vertical, we’ve seen a lot of success within highly regulated, data-rich industries such as financial services and healthcare. 

Today, most of these customers are leveraging Gradient’s long context, domain-specific models – Albatross for Financial Services and Nightingale for Healthcare – to help power custom AI solutions that simplify their business needs.

Core value proposition

In a nutshell, how would you describe the primary benefit Gradient offers to businesses seeking to leverage AI? Is it the ease of integration, industry-specific expertise, or something else entirely?

At Gradient, our goal is to accelerate AI adoption in enterprise - minimizing the effort required to integrate AI, while maximizing the overall value. Today, most companies face similar challenges in automating their business operations, due to the complexity and fluidity of data processing. 

With Gradient, we solve enterprise automation and power 100% of the AI automation business process for industries like financial services. As a result, our customers can completely remove their teams from labor-intensive processes like KYC automation and instead focus on work that is of higher value to their business. 

Compound AI systems

Your concept of "compound AI systems" is intriguing. Can you delve deeper into how these systems work and what advantages they provide compared to traditional AI solutions?

Enterprise automation shouldn’t be powered by a single AI model but a compound AI system. Empirically, we’ve seen that one model, even with multiple calls, can only get so far. Compound AI systems consisting of multiple agents and other components, like memory and routing, are the key to maximizing performance on enterprise workflows. 

All these different components allow the system to formulate a plan, route to the best expert model to complete each step, and critique its own output, resulting in higher accuracy and more reliability on enterprise tasks.

Data security and privacy

Security and privacy are paramount concerns for businesses considering AI solutions. How does Gradient ensure the safety and compliance of its clients' data throughout the AI development and deployment process?

Safety, security, and compliance are all top of mind for our team at Gradient, which is why we ensure that our customer’s data stays with their team every step of the way. Gradient offers dedicated deployments in all major cloud providers and on-premise to adhere to some of the most highly regulated industries. 

Our customers also have the ability to choose from a wide range of models, including SOTA open-source models that provide full transparency into the model’s architecture. Last but not least, Gradient is built for enterprise customers, which means it meets the highest standards in regulatory compliance - achieving some of the most reputable certifications, such as SOC 2 Type 2, HIPAA, and GDPR.

Open-source LLMs and fine-tuning

You offer a platform for personalizing open-source large language models (LLMs). Can you provide an example of a successful use case where a client leveraged this capability to address a specific business need?

Of course! In general, our platform enables enterprise customers to combine their private data with an LLM of their choice to create a custom model. This enhances the capabilities of the model so that it understands its organization and ensures that it’s capable of addressing its specific needs. 

Some of our most recent use cases include extracting unstructured data from clinical notes, claims processing, and KYC. Our team has also developed open-source and proprietary long-context models that have made it possible for customers to tune AI without actually having to fine-tune their models. This enables faster time to value, less risk for our customers, and the need for an experienced technical team.

AI assistant development

Your "AI Foundry" seems like a powerful tool for building custom AI assistants. Could you walk us through the typical workflow for a client developing an assistant through this platform?

The Gradient AI Foundry enables limitless enterprise automation for our customers by leveraging a combination of agentic workflow primitives and custom Gradient LLMs that are fine-tuned to maximize performance across each task. To get started, our customers simply provide their data and objectives to our Foundry. 

The Foundry then creates a self-improving agent that automates the workflow that was just described. Once complete, the Foundry accumulates learnings and knowledge to accelerate other areas of the business where Gradient may be able to support or improve.

Metrics and ROI demonstration

How does Gradient help clients measure the return on investment (ROI) achieved through their AI implementations? Are there specific metrics or success stories you can share?

Gradient helps automate business operations by providing the most comprehensive solution for enterprise automation. Given that Gradient supports a variety of industries (e.g. healthcare, financial services, etc.) ROI and success metrics are generally unique to their respective industry (e.g. improving investment predictions by 20%). 

However, some of the metrics that have consistently overlapped across our portfolio of customers include 1) cost savings 2) reduction in hours spent on manual tasks 3) time saved on AI development, and 4) increased productivity.

Future of work and AI integration

As AI continues to evolve, how do you envision Gradient’s solutions impacting the future of work and how humans will collaborate with AI in the enterprise landscape?

As LLMs become more and more capable, we’ll see agents (and in turn Gradient’s solutions) be able to take on more and more autonomy reliably, transforming the way companies are structured. Teams will be able to delegate the monotonous parts of their work to AI and spend more time on strategic and high-leverage tasks. In this future, every team will work in partnership with AI to supercharge their productivity and impact.

Challenges and differentiation

In the competitive AI for enterprise space, what are some of the biggest challenges Gradient faces? How do you differentiate yourselves from other providers in the market?

At Gradient we are deeply invested in the business value AI drives for our customers and ensure that all our enterprise AI products fully solve the business problem. 

Today Gradient is the only AI platform that can automate the entire enterprise, offering inference and fine-tuning, agent solutions, and a range of models to choose from. Because of the flexibility in our automation system, customers using Gradient are able to support an infinite amount of workflows without extensive development. 

As a result, we’re not only providing the fastest way to help our customers fully automate end-to-end workflows using AI, but we ensure that our customers are finding solutions that establish I from the get go.

Community engagement

Does Gradient participate in any industry events, conferences, or open-source communities? If so, how do you see these engagements contributing to your company's growth and the broader AI development ecosystem?

Absolutely! We’re a big believer in connecting with our customers and giving back to the open-source community. Whether it's a local hackathon or a major industry conference, you'll likely find us there as an active participant or speaker. 

As for the open-source community, we always look for new ways to pay it forward. Most recently, our team released the first 1M Context Length Llama-3 70B and 4M Context Length Llama-3 8B on Hugging Face. 

The response from the community has been extraordinary, which is why we’ve continued to work with other thought leaders on improving and setting the standard for evaluating the quality of long context models (e.g. NIAH, RULER, etc.).

Want to know more about generative AI? Catch the latest insights in our report:

Generative AI 2024 report

Unlock the secrets to faster workflows with the Generative AI 2024 Report. Learn how 56.4% of companies leverage AI to boost efficiency and stay competitive.

AI Accelerator InstituteMarisa Garanhel

Amazon has announced a commitment of up to $230 million to support startups developing generative AI-powered applications.

With approximately $80 million allocated to Amazon's second AWS Generative AI Accelerator program, this significant investment aims to position AWS as the preferred cloud infrastructure for startups creating generative AI models for their products, applications, and services.

AWS Generative AI Accelerator program

A substantial portion of the new funding, including the entirety of the amount designated for the accelerator program, will be provided as compute credits for AWS infrastructure. These credits are non-transferable to other cloud service providers like Google Cloud and Microsoft Azure.

To enhance the program, Amazon is ensuring that startups in this year's Generative AI Accelerator cohort will have access to experts and technology from Nvidia, the program's presenting partner. Additionally, these startups will be invited to join the Nvidia Inception program, which offers opportunities to connect with potential investors and gain additional consulting resources.

Growth of the Generative AI Accelerator Program

The Generative AI Accelerator program has seen substantial growth. Last year's cohort, consisting of 21 startups, received up to $300,000 in AWS compute credits, totaling an investment of around $6.3 million.

"With this new effort, we will help startups launch and scale world-class businesses, providing the building blocks they need to unleash new AI applications that will impact all facets of how the world learns, connects, and does business," said Matt Wood, VP of AI products at AWS.

Amazon's broader generative AI efforts

Amazon's increasing investment in generative AI technology includes initiatives such as the $100 million AWS Generative AI Innovation Center, free credits for startups utilizing major AI models, and its Project Olympus model.

These efforts come as Amazon strives to catch up with tech giants in the rapidly growing and competitive generative AI space. Although Amazon claims that its generative AI businesses have reached "multiple billions" in run rate, the company is often viewed as lagging behind.

Challenges and setbacks

AWS initially planned to unveil its generative AI model similar to OpenAI’s ChatGPT, code-named Bedrock, at its annual conference in November 2022. However, significant bugs delayed the launch, transforming Bedrock into Amazon’s model hosting service. Despite Amazon PR's dispute, the postponement highlights some challenges.

The Alexa division has encountered its own issues, including technical difficulties and internal conflicts, as Fortune's Sharon Goldman reported. Despite a high-profile press demonstration of a "next-gen" Alexa nine months ago, the updated version is still not ready for release due to insufficient training data, inadequate access to training hardware, and other obstacles.

Missed opportunities and regulatory scrutiny

Amazon also missed early investment opportunities in leading AI startups Cohere and Anthropic. After being rejected by Cohere, Amazon co-invested $4 billion in Anthropic alongside Google. This co-investment reflects Amazon's attempts to stay competitive in the AI startup investment landscape.

Compounding these challenges is the recent departure of Howard Wright, AWS' head of startups, who managed relationships with startups. Furthermore, Amazon faces growing scrutiny from regulators regarding Big Tech's investments in AI startups.

The U.S. Federal Trade Commission has opened an inquiry into Microsoft's backing of OpenAI and Google and Amazon's investments in Anthropic. European policymakers have also expressed skepticism towards such deals, adding another layer of complexity to Amazon's generative AI ambitions.

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AI Accelerator InstituteMarisa Garanhel

This article is based on Burak Gokturk’s brilliant talk at the AI Accelerator Summit in San Jose. As an AIAI member, you can enjoy the complete recording here. For more exclusive content, head to your membership dashboard.

The world of generative AI has been moving at a blistering pace, with new models and platforms seeming to launch every week. In my 20-plus years in the field, I've never seen such a whirlwind of innovation.

While the last couple of years have generated tremendous excitement around generative AI's potential, actually deploying it to create business value is still a major challenge for many organizations.

Despite trying out generative AI experimentally, a lot of teams are struggling with how to effectively implement and operationalize it in production settings. There's a clear need for comprehensive, enterprise-ready platforms that provide flexibility, customization, and robust model lifecycle management.

I'm Burak Gokturk, and I lead Google Cloud AI – you may know some of our products like Vertex AI, Vertex AI Vision, and Vertex AI Search. In this article, I'll outline the key requirements we've identified for successfully deploying generative AI at scale based on working with hundreds of leading companies. I'll then dive into how Google Cloud's Vertex AI platform addresses those needs.

Let’s get to it.

Meeting enterprise needs for generative AI

Through discussions with hundreds of organizations building generative AI applications, we've identified several key requirements for an enterprise-grade platform:

1. Flexibility: For many customers, it's important to be on a platform with choice and flexibility. You've probably noticed there's a new generative AI model launching every other week. Customers have seen that and they don’t want to get locked into just one model long-term.
2. Customization: These generative AI models are essentially trained on mostly public data. But every customer has their own use case and data. You need a platform with tuning, grounding, and customization mechanisms to handle that.
3. Deployment: Once you figure out a model and customize it with your data, how do you actually get it to production to create business value? Choosing a platform with deployment, evaluation, testing, monitoring capabilities, and all the necessary metrics, is going to be super critical.

With the customer needs I’ve just described in mind, we built Vertex AI. It has multiple layers, including Agent Builder, to make building generative AI applications and agents easy.

Another vital layer of Vertex AI is Model Garden; it offers over 130 models. That might not sound like many – after all, there are thousands of generative AI models out there – but we've curated the models we believe will be the most useful for our customers. We really believe in providing choice, with first-party models, partner models, and open-source options.

The evolution of Gemini Pro

Chances are, you’re aware of Gemini; we launched it on AI Studio and Vertex AI in December 2023. Since then, there's been a lot of interest, with over a million developers using Gemini daily. But how did we get here? 

The first model we launched was Gemini 1.0 Pro, which has cool capabilities like multimodal support and high performance. Earlier this year, we announced a new version of Gemini 1.0 Pro, which is significantly faster and higher quality. But as I said before, you'll see new models and revisions launching literally every week, not just from Google but across the globe. 

When we launched 1.0 Pro in December, we didn't stop there. About six weeks later, we released Gemini 1.5 Pro. It's a much bigger model with significantly better reasoning capabilities. It also has something no other model in the world offers – a large context window. That means the AI can recall much more information during a session.

Gemini 1.5 Pro is now in public preview on Vertex AI and AI Studio, so you can easily try it out. To give you an idea of what it can do, you can input an image or a video clip with no description, and it can analyze the contents.

For example, I gave it a short personal clip of Draymond Green talking to a referee at a Warriors game. I just asked, “What is this?” and it immediately responded, “This is Draymond Green from the Golden State Warriors, talking with a referee.”

In today’s day and time, which is defined by rapid technological advancements, the fusion of artificial intelligence (AI) and gaming presents a globally innovative opportunity to tackle some of the world's most pressing issues.

Traditionally viewed as two separate, distinct domains, AI and gaming together have the potential to revolutionize the way we approach work, life, and complex global challenges.

This combination builds a superpower technology that can connect AI's analytical prowess and gaming's engaging, educational frameworks to create innovative solutions for real-world problems such as forest fires, poverty, and homelessness.

The merging of AI and gaming offers an innovative and revolutionary approach to problem-solving that goes beyond conventional methods in current existence. AI offers some unequaled analytical power and predictive capabilities, enabling precise and efficient strategies for addressing complex issues.

Concurrently, gaming provides immersive and interactive environments that can train, educate, and engage individuals in meaningful ways. When these technologies are integrated, they open up new possibilities into opportunities for developing effective and creative solutions to the world's most daunting challenges.

Turning possibilities into opportunities

Taking a look at Plato's Allegory of the Cave, where insight is achieved by seeing beyond the shadows to understand true reality, we can turn possibilities into opportunities by exceeding traditional problem-solving paradigms with the combination of AI and gaming.

By integrating AI and gaming, we can uncover innovative and effective strategies to address really complex global issues. This combination of AI and gaming together is not just a real gamechanger, but it also broadens our perspectives and equips us with innovative tools to combat real-world, everyday challenges like forest fires, poverty, and homelessness, ultimately shining a light on paths to a brighter and more sustainable future. 

Like Plato’s Allegory of a Cave, it is the things we don’t see. Many times, we do not see opportunities because they seem so small, but it is the things that seem like the smallest changes that can create the biggest impact.

For example, a one-degree increase in temperature in a specific region, county, or state can lead to significant local impacts. This seemingly small change can result in more frequent and intense heatwaves, affecting public health, particularly among vulnerable populations.

It can disrupt agriculture by altering crop growth cycles and reducing yields, leading to economic losses for farmers. These are two of the many side effects of such a small shift in temperature for a region.

In summation, we can certainly use AI and gaming as a way to garner insight into how our actions affect things like the weather. Gaming for AI can profoundly impact human existence and the natural world in both small and large ways, leading to a healthier, more sustainable, and efficient world, benefiting both humans and nature.

You’re not just another Joe Schmo 

What if you could be the person who solves a food crisis with a great idea? In this case, you’re not just another Joe Schmo; you are someone who has made an expansive difference.

The key is access both for great ideas and for the ideas to be actualized. Currently, everyday citizens primarily engage with societal economics and decision-making processes as voters and lobbyists, often in peripheral capacities.

This marginal involvement poses significant challenges for individuals who wish to be heard and actively participate in shaping their communities. The complexity and opacity of the decision-making mechanisms that impact their daily lives—both in the short and long term—further exacerbate these challenges.

Everyday citizens are frequently far removed from the intricate workings of policy-making and economic planning, leading to a disconnect between public needs and decision-makers' actions.

This gap hinders the ability of communities to leverage the valuable insights and contributions that citizens can offer. However, the integration of Artificial Intelligence (AI) and gaming presents a promising solution to bridge this gap. AI can process and analyze vast amounts of data, providing citizens with accessible information and actionable insights.

Simultaneously, gaming can engage individuals in interactive and immersive experiences, making complex concepts more understandable and decision-making processes more transparent. Together, AI and gaming can create platforms that empower citizens to participate more effectively in their communities.

These technologies can facilitate better understanding, communication, and collaboration, enabling citizens to contribute meaningfully to the decisions that shape their lives. In this way, AI and gaming can transform the role of citizens from passive observers to active participants in the governance and development of their communities. 

Additionally, these technologies provide a sort of 'checks and balances' to existing and ongoing corruption within political and economic decision-making. AI's data analysis capabilities can uncover patterns of corruption and inefficiencies, while gaming can foster greater transparency and accountability by involving citizens directly in oversight and decision-making processes.

This dual approach enhances citizen engagement and promotes integrity and trust within the systems that govern their communities.

Sim City, Glacia, United States, 09990

What if you could live in the perfect city? No crime, no poverty, no pollution or disease, and many of the things that plague humans today. Yes this is a utopia, and while currently not possible, we can certainly aim to get to this place.

I call it Sim City, Glacia, United States, 09990. Welcome to a place where your most vivid life simulations transform into reality. Here, water flows abundantly, ensuring a lush and vibrant environment. Everything around you is a striking shade of green, the zip code perfectly matching the hex code 09990, creating a harmonious and visually stunning landscape.

In Sim City, every detail is meticulously crafted to offer an unparalleled experience, making it the ultimate destination for those seeking a blend of nature and technology in perfect balance.

One great example of a video game that could, in concept, integrate well with AI is the video game "Sim City." Traditionally, Sim City has been a platform where players manage and build cities, dealing with various challenges such as crime, pollution, and traffic. By leveraging AI within Sim City, we can transform it into a powerful tool for real-world problem-solving. Here are some of the opportunities I see available by using Sim City as an existing concept:

1. Citizen engagement and idea generation:

Sim City can serve as a simulation platform where everyday citizens experiment with urban planning and policy decisions in a risk-free environment. With AI analyzing these simulations, we can gather valuable data on what strategies work best for addressing issues like crime, poverty, and homelessness.

Players' innovative solutions can be evaluated and refined using AI algorithms, creating a repository of effective strategies that urban planners and policymakers can consider.

Additionally, we can track ownership of such ideas by incorporating other solutions such as NFTs and blockchain. For example, the ownership of such innovative platforms can be commercialized, with profits traced and returned to individual citizens.

This model can inspire economic prosperity by ensuring that the financial benefits derived from these technologies are shared among the community members who use and contribute to them. By distributing profits back to citizens, we can create a more inclusive and equitable economic system, further motivating public involvement and investment in community development initiatives.

2. Environmental management and disaster preparedness:

Sim City can integrate AI-driven models for environmental management and disaster preparedness. For example, players can simulate the impact of various policies on forest fires, pollution levels, and climate change.

AI can provide real-time feedback on the potential outcomes of different strategies, helping players understand the long-term effects of their decisions. This approach can create a ‘Butterfly Effect’ scenario, allowing players to emotionally connect with virtual citizens and see how potential outcomes might reflect real-life consequences.

This gamified experience can educate citizens on the importance of sustainable practices and disaster preparedness, fostering a more informed and proactive society.

3. Traffic and urban infrastructure:

Traffic congestion and inefficient urban infrastructure are significant challenges in many cities. Players can experiment with different urban designs and traffic management strategies by incorporating AI into Sim City. AI can analyze the effectiveness of these strategies, providing insights into optimizing traffic flow and reducing congestion.

This data can be invaluable for urban planners looking to implement smart city solutions.

4. Social issues and community building:

Sim City can also address social issues such as poverty and homelessness. Players can test various social policies, such as affordable housing projects, job creation programs, and community support initiatives.

AI can evaluate the impact of these policies on the virtual city's population, highlighting successful approaches that can be applied in the real world. This interactive and educational experience can raise awareness and drive public support for effective social policies.

This is not a game, this is real life 

I recently had to move from one condo to another in San Diego because my landlord was ending their contract with my building. While packing, I had to dispose of my perishable foods as I was about to travel internationally for a few weeks. Naturally, I didn't want to waste the food.

The timing was incredibly inconvenient. I was notified about the move just 36 hours before my flight from Los Angeles International, leaving me with a very tight schedule. I hoped to find people in need to give the food to, but ironically, despite having so much to give, I couldn't find anyone to take it during my drive from San Diego to Los Angeles.

Regrettably, I ended up throwing it away because I didn't have the time to search for people in need without risking missing my flight. Many U.S. cities are exploring AI automation and predictive technologies to locate and assist those who are destitute and economically disadvantaged. However, I don't see these efforts being democratized to address the issue effectively. The core problem is access—connecting disadvantaged individuals back to mainstream society.

A food revolution for poverty

The dilemma of having to dispose of perishable foods before an international trip due to a last-minute move is a common issue, reflecting broader challenges in resource redistribution.

However, combining AI and gaming can offer innovative solutions to this problem, akin to a real-world SimCity. Imagine a simulation-based resource allocation platform that uses AI to predict, simulate, and optimize the flow of surplus resources in urban environments.

This platform would track and forecast when and where surplus food will become available by integrating data from grocery stores, restaurants, and households. It would identify areas with the highest need for resources through socioeconomic data and real-time inputs from local organizations.

AI could match surplus food providers with recipients in need, optimizing routes to ensure timely delivery and reducing waste. Gamification elements would engage users in managing and improving the virtual city’s resource allocation, offering challenges to allocate resources and build community involvement efficiently.

Users could log surplus food or request assistance via a mobile app, receiving push notifications about nearby opportunities to donate or collect resources. Collaborating with food banks, shelters, and community organizations would enhance the platform’s effectiveness by providing real-time data and aiding in distribution.

In practical terms, when faced with surplus food, you could log it into the platform, and the AI would instantly match it with individuals or organizations along your route who need food.

The platform would provide optimized routes for dropping off the food without significantly detouring from your travel path, allowing you to donate efficiently without risking missing your flight.

Real-time feedback on the impact of your donation would also be provided, possibly through in-game representations of the positive effects on the community. By integrating AI and gamification elements inspired by SimCity, we can create a dynamic platform that efficiently redistributes resources, reduces waste, and connects surplus with need, transforming resource management into a collaborative and rewarding effort.

Conclusion: A new era of innovative problem-solving

The fusion of AI and gaming promises to transform our approach to solving some of the world's most pressing challenges. By leveraging AI's unparalleled analytical capabilities and gaming's immersive, engaging frameworks, we can develop innovative and effective strategies for addressing complex issues like forest fires, poverty, and homelessness.

This powerful combination broadens our perspectives and equips us with the tools needed to create a healthier, more sustainable, and efficient world.

Integrating AI and gaming can revolutionize citizen engagement in societal economics and decision-making processes. Platforms like Sim City, enhanced with AI, can empower everyday citizens to experiment with urban planning and policy decisions in a risk-free environment.

These simulations can generate valuable data and innovative solutions that urban planners and policymakers can consider, ultimately fostering a more inclusive and equitable economic system.

Moreover, AI and gaming's potential applications extend to environmental management, disaster preparedness, traffic optimization, and addressing social issues.

Gamified experiences can educate and engage the public, raising awareness and driving support for sustainable practices and effective social policies. AI and gaming can enhance transparency, accountability, and trust within governance systems by transforming passive observers into active participants.

In practical scenarios, such as the challenge of redistributing surplus food, AI and gaming can offer real-world solutions that optimize resource allocation and reduce waste. By creating platforms that match surplus food providers with recipients in need, we can ensure timely delivery and efficient resource management. Gamification elements can further engage users, making the process collaborative and rewarding.

In conclusion, we make a better world for all. The integration of AI and gaming is not just a game changer but a revolutionary approach to problem-solving that holds the potential to transform our world. By harnessing these technologies, we can uncover innovative strategies, foster citizen participation, and create a brighter, more sustainable future for all.

Want more from Paul? Read his articles below:

Paul Anthony Claxton - AI Accelerator Institute

Paul Anthony Claxton is a Managing General Partner at Q1 Velocity Venture Capital. He has 10 years of experience as a serial entrepreneur and fund manager, and has co-founded several ventures.

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We are thrilled to announce the launch of the Generative AI 2024 report, a comprehensive analysis of the latest trends, tools, benefits, and challenges in the generative artificial intelligence field.

Sponsored by WEKA, this report provides an in-depth look at how AI practitioners and end users navigate the landscape of generative AI tools, offering valuable insights into their preferences, priorities, and pain points.

In this preview, we present key findings from Section 8 of the report, focusing on the main generative AI tools of choice, their perceived benefits, and the challenges faced by users. This snapshot highlights significant shifts from last year, revealing emerging trends and changing attitudes towards these transformative technologies.

Download the complete Generative AI 2024 Report to delve deeper into these insights and explore the full breadth of data and analysis.

Main generative AI tool of choice, benefits, and challenges

Not only does ChatGPT continue to be the main tool of choice compared to last year (15.5%), but it also had a higher percentage of use among both practitioners (47.5%) and end users (41%).

Copilot was mentioned more frequently this year; however, in spite of ChatGPT still leading the generative AI tool choice, we saw a much wider variety of tools mentioned than in 2023.

Among practitioners, we saw the following tools:

Among end users, we saw the following tools:

What is the main benefit of your number one choice?

In 2023, efficiency (26.7%) was ruled as the main benefit of respondents’ number one tool of choice.

This year, we saw a shift in priorities, with practitioners (32.2%) and end users (27.3%) agreeing that quality is now more important. Users are looking for tools that can effectively perform their tasks while being extremely accurate and reliable. This switch could be due to generative AI tools being increasingly used for vital business functions, and errors in these situations can be costly.

Efficiency was still the second most important reason for practitioners (15.3%), while end users highlighted speed (22%) instead.

This difference highlights how practitioners and end users have varied requirements; the former can often work with complex and multifaceted tasks that need tools to maximize resources while keeping waste to a minimum, while the latter might prioritize how quickly a tool can deliver what they need according to everyday tasks.

What is the main challenge of your number one choice?

For the second year in a row, biases, errors, and limitations of generative AI are considered to be the main challenge of the number one tool of choice. 

This is more important for end users (50.6%) than for practitioners (43.9%), who equally value generative AI data security in second place with 13% and 23.6%, respectively.

Perhaps surprisingly, this represents a change from last year when generative AI technology was newer. In 2023, the limited information pool was the second biggest challenge, with generative AI data security falling after.

This could point to a general increase in awareness about the tools and their capabilities, especially with higher discussions behind the ethics and governance of generative AI.

Bonus: How do you address ethical considerations and potential biases with generative AI tools?

As previously mentioned, the ethics behind generative AI can be a concern, and this year, we wanted to know how respondents are addressing these considerations as they continue to use the tools.

The plurality of practitioners (44.1%) highlighted regular audits and assessments as the main way to address ethical challenges. Suggesting a structured and proactive approach within this community, regular audits often lead to the identification and mitigation of potential issues for transparency with stakeholders and compliance with ethical norms or regulations.

This, however, was a very close second for end users (31.1%), who mainly reported that they didn't specifically address any ethical concerns (32.5%). This could indicate the existence of a slight gap in awareness or resources to address issues, and end users might be less equipped to fully delve into the ethical considerations of generative AI tools.

Curious to know more insights about generative AI in 2024? Don't miss out – download your FREE copy of the report today!

Amazon Web Services (AWS) is strategically positioning its cloud infrastructure business to capitalize on the transformative potential of generative artificial intelligence (AI) across various industries, including space.

According to Clint Crosier, AWS director of aerospace and satellite, over 60% of the company’s space and aerospace customers are already integrating AI into their operations, a significant increase from single digits just three years ago.

Predicting growth in generative AI

Crosier anticipates similar growth for generative AI in the space sector over the next few years. Generative AI employs deep-learning models to answer questions or create content based on patterns identified in extensive datasets, representing a substantial advancement over traditional machine-learning algorithms.

Crosier told SpaceNews in an interview that mathematical advancements, an explosion of available data, and more affordable, efficient processing chips create a "perfect storm" for the rise of generative AI, driving greater adoption of cloud-based applications.

AWS's internal reorganization

"In the last year, AWS has fundamentally reorganized itself internally to place the right teams and organizational structure in place so that we can really double down on generative AI," Crosier said.

AWS has established a "generative AI for space" cell comprising a small team that engages with cloud customers to develop next-generation capabilities. These efforts include a generative AI laboratory where customers can experiment with new uses of these emerging technologies.

Key areas of application

Crosier identifies three primary areas for using generative AI in space: geospatial analytics, spacecraft design, and constellation management. Earth observation satellite operators like BlackSky and Capella Space leverage AI extensively to derive more insights from their geospatial data, although they have not fully embraced generative AI.

In the manufacturing sector, engineers are exploring how generative AI models, informed by design parameters, could produce innovative concepts by drawing on potentially overlooked data from other industries, such as automotive.

"Whether you’re designing a satellite, rocket, or spacecraft, generative AI can explore global data spanning decades and provide novel design concepts for your team to refine," Crosier said.

Enhancing constellation management

Generative AI also promises to help operators manage increasingly crowded orbits by simulating various testing scenarios.

"If I have a constellation of 600 satellites, generative AI can model numerous scenarios to determine the top 25 cases for optimal design, saving time and money," Crosier explained.

AWS's initiatives to accelerate the adoption of emerging computing capabilities include scholarships and a commitment announced in November to provide free AI training to two million people worldwide by the end of 2025.

Want more about AI in space? Read the article below:

4 uses of computer vision in space exploration

Both computer vision and deep learning can work together towards it, with computer vision algorithms capable of further improving autonomous performance.

AI Accelerator InstituteMarisa Garanhel

This article is based on Santosh Radha’s brilliant talk at the AI Accelerator Summit in San Jose. As an AIAI member, you can enjoy the complete recording here. For more exclusive content, head to your membership dashboard.

Generative AI is revolutionizing how we interact with technology. From chatbots that converse like humans to image generators producing stunning visuals, this incredible tech is transforming our world. 

But beneath these mind-blowing capabilities lies a massive computing infrastructure packed with technical complexities that often go unnoticed.

In this article, we'll dive into the realm of high-performance computing (HPC) and the challenges involved in productionizing generative AI applications like digital twins. We'll explore the explosive growth in computing demands, the limitations of traditional HPC setups, and the innovative solutions emerging to tackle these obstacles head-on.

But first, let me quickly introduce myself. I'm Santosh, and my background is in physics. Today, I head research and product at Covalent, where we focus on orchestrating large-scale computing for AI, model development, and other related domains.

Now, let’s get into it.

The rise of generative AI

Recently, at the GDC conference, Jensen Huang made an interesting observation: he called generative AI the “defining technology of our time” and termed it the fourth industrial revolution. I'm sure you'd all agree that generative AI is indeed the next big thing. 

We've already had the first industrial revolution with steam-powered machines, followed by the advent of electricity, and then, of course, computers and the internet. Now, we're witnessing a generative AI revolution that's transforming how we interact with various industries and touching almost every sector imaginable.

We’ve moved beyond machine learning; generative AI is making inroads into numerous domains. It’s used in climate tech, health tech, software and data processing, enterprise AI, and robotics and digital twins. It’s these digital twins that we’re going to focus on today.

Digital twins: Bridging the physical and virtual worlds

In case you’re not familiar with digital twins, let me explain the concept. A digital twin is a virtual representation of a physical system or process. It involves gathering mathematical data from the real-world system and feeding it into a digital model.

For instance, let's consider robotics and manufacturing applications. Imagine a large factory with numerous robots operating autonomously. Computer vision models track the locations of robots, people, and objects within the facility. The goal is to feed this numerical data into a database that a foundational AI model can understand and reason with.

With this virtual replica of the physical environment, the AI model can comprehend the real-world scenario unfolding. If an unexpected event occurs – say, a box falls from a shelf – the model can simulate multiple future paths for the robot and optimize its recommended course of action.

Another powerful application is in healthcare. Patient data from vital signs and other medical readings could feed into a foundational model, enabling it to provide real-time guidance and recommendations to doctors based on the patient's current condition.

The potential of digital twins is immense. However, taking this concept into real-world production or healthcare environments presents numerous technical challenges that need to be addressed.

The computing power behind the scenes

Let's shift our focus now to what powers these cutting-edge AI applications and use cases – the immense computing resources required. 

A few years ago, giants like Walmart were spending the most on cloud computing services from providers like AWS and GCP – hundreds of millions of dollars every year. However, in just the last couple of years, it's the new AI startups that have emerged as the biggest consumers of cloud computing resources. 

For example, training ChatGPT-3 in 2022 reportedly cost around $4 million in computing power alone. Its successor, ChatGPT-4, skyrocketed to an estimated $75 million in computing costs. And Google’s recently launched Gemini Ultra is said to have stacked up nearly $200 million in computing expenditure.

OpenAI's newly unveiled GPT-4 Omni (GPT-4o) model promises to change the roles of finance professionals (and others) forever.

This advanced language model represents a major leap forward in artificial intelligence capabilities, offering improved experiences across text, voice, and vision.

“This is the first time that we are really making a huge step forward when it comes to the ease of use,” Mira Murati, OpenAI Technology Chief.

Some exciting new features include the ability to ask GPT-4o to translate languages using nothing but an image and soon, the ability to have a more natural, ‘real-time voice conversation and the ability to converse with ChatGPT via real-time video.’

OpenAI reports that GPT-4o “can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds”. Impressively, this is very similar to a natural human response time during a conversation.

The best part though, is that GPT-4o is available to free users. However, it will have some usage limits.

Free users can access these features:

• Experience GPT-4 level smarts with greater speed.
• Get answers combining model knowledge and web info.
• Create charts and visualizations from data.
• Discuss photos you take by uploading them.
• Upload files for help with summarizing, writing, or analyzing (including Excel files).
• Keep conversations on track with built-in memory.

Table of contents

• Introducing GPT-4o
• How to upload Excel files and create charts
• Analyzing financial data in GPT-4o
• Voice conversations
• Desktop app – GPT-4o
• FAQs

Introducing GPT-4o (OpenAI’s official update)

Alright, all that sounds great. But let’s get into the reason you’re here – to learn how GPT-4o can be used in finance and data analysis.

Let’s get into it.👇🏼

How to upload Excel files and create charts in GPT-4o

1. Upload Excel spreadsheet files

You can now upload Excel, CSV, and other files directly to GPT-4o. No more copying and pasting data from your file into ChatGPT. Now that you can upload your files directly, it makes the entire process of analyzing complex data sheets a lot easier and less time-consuming. You can also upload other files like documents, PDFs, and more. 

To upload an Excel file (or any file) to GPT-4o, simply click the paperclip icon on the bottom left corner of your screen:

Once you've clicked it, you'll be able to upload your file.

It will then appear on your screen like this:

Below your file, you can write your prompts, ask ChatGPT to assess and analyze your file, create charts with the data, provide insights and advice, and more.

2. Analyze data and create charts

Once you’ve uploaded your data file, you can start asking questions to help analyze the data. You can even ask GPT-4o to create a chart based on the data you’ve provided. 

Here are a few simple examples of the types of charts and graphs that GPT-4o can create:

[Note: We've used simplified fictional data for demonstration purposes.]

Analyzing financial data in GPT-4o

To help you get to grips with how you can use GPT-4o in finance or data analytics, let’s look at an example.

We’ve used some ‘dummy data’ to test and demonstrate how finance can leverage this technology in their roles.

So, let’s begin by assuming we’ve just uploaded the data below and GPT-4o has put it all into this nice, clean table for us:

Now we're ready to begin analyzing this data. So, we asked ChatGPT this question as a starting point:

Analyze the cash flow forecast for 2024 and identify any potential liquidity issues. Highlight any months where the cash balance is projected to fall below the minimum required level of $50,000.

When we asked GPT-4o about the monthly cash flow, it provided a list showing the cash balance and liquidity position for each month.

For example, January was noted as having a cash balance of $60,136.25 and no liquidity issues since the cash balance is above the minimum required level of $50,000.

Moving through the months, it provided similar insights. In May, GPT-4o reported a cash balance of $47,788.65 and therefore notified us that there is a liquidity issue identified because the cash balance was below the minimum required level of $50,000.

If we want to look into the data even further, we can ask more questions to get GPT-4o to perform data analysis.

Here are some examples of the questions we asked, and the responses that GPT-4o gave in return:

Q. Which months have the lowest cash balance, and what could be the potential reasons for this?

Q. Calculate the average monthly cash inflows and outflows for 2024.

Q. Identify and explain any months where cash inflows are significantly lower than average.

We’ve used quite a simple example, but it shows just how well GPT-4o can analyze financial data. We suggest trying it out for yourself and seeing what types of insights you can get back from it.

By leveraging GPT-4o, you can process and analyze data to discover deeper insights and improve decision-making.

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Voice conversations

Aside from data analysis, GPT-4o offers extra capabilities for natural conversations and streamlined workflows.

With the new Voice Mode, you can engage in voice conversations with the AI directly from your computer. This feature lets you do things like brainstorm ideas or discuss pressing topics in finance using speech input and output. 

Desktop app – GPT-4o

OpenAI is launching a new ChatGPT desktop app for macOS, so you can now access GPT-4o via the app (it's also available for both free and paid plan users).

This app integrates with your computer, so you can access the AI instantly using a keyboard shortcut (Option + Space).

From there you can ask questions, take screenshots, and discuss them directly within the app.

The desktop app also offers features like screenshot capture and annotation, making it easier to collaborate with the AI on visual content or seek clarification on specific elements within an image. This might be particularly useful if you want help analyzing charts, graphs, etc.

Math problems with GPT-4o [video]

OpenAI posted the video below to demonstrate how you can use GPT-4o to help with math problems.

It's interesting to see how well this AI tool performs mathematical problems. Perhaps it'll be just as useful for helping with things like financial formulas or calculating metrics for your next report, etc.

Of course, it's important to double-check the answers you get to make sure you're getting accurate responses!

FAQs

What is GPT-4o?

GPT-4o is OpenAI’s latest flagship model that can reason across audio, vision, and text in real-time. OpenAI claims it's a step “towards much more natural human-computer interaction.” 

How is GPT-4o different than other versions of ChatGPT?

GPT-4o differs from other versions of ChatGPT by offering improved performance, including faster response times and better handling of complex queries, while retaining the comprehensive language capabilities of GPT-4.

Is GPT-4o free?

Yes, GPT-4o is available to users of the free version of ChatGPT.

Is GPT-4o better than GPT-4?

GPT-4o is considered as an optimized enhancement of GPT-4, offering better performance in terms of speed and efficiency. However, the core language capabilities remain consistent with GPT-4.

Is ChatGPT 4o available?

Yes, GPT-4o is now available to users. 

What does GPT-4o do?

GPT-4o can assess, summarize, and converse with users via text, visuals, and audio. It also answers your queries by combining both model knowledge and information from the internet, helping to provide even better insights.

What's new about ChatGPT 4o?

GPT-4o is a new, optimized version of OpenAI's GPT-4, designed to enhance performance and efficiency while maintaining the robust language understanding and generation capabilities of its predecessor. It can now converse with you using images, audio, and video.

Say goodbye to photobombs.

Adobe is introducing an AI-driven Generative Remove feature to its Lightroom photo editor. This feature simplifies the removal of unwanted elements like that annoying person in the background. Currently in public beta, it works seamlessly across the Lightroom ecosystem on mobile, desktop, and web platforms.

Streamlined editing with Firefly AI

Lightroom's Generative Remove effortlessly replaces unwanted elements using Adobe's Firefly AI engine. Paint over the area you want to remove, and Lightroom sends this information to Adobe's Firefly servers, which process the data and return the edited image.

In contrast to Adobe Photoshop's Reference Image feature, which allows users to generate new images using Firefly, Lightroom's AI enhancements are designed to streamline a photographer's workflow.

Tackling complex edits with ease

Removing distracting elements from images is often challenging. Traditionally, tools like Lightroom's Content Aware Remove match surrounding areas to hide elements. While effective for simple backgrounds, this method becomes cumbersome for larger objects or complex backgrounds.

The Firefly-powered Generative Remove excels at handling larger objects against any background, reducing what once took hours and technical expertise to a quick and easy task. Lightroom now empowers everyone to be an editing wizard. Moreover, Generative Remove offers three different versions of the edit, letting you choose the best one.

Comparing to Google’s Magic Eraser

Although Generative Remove is impressive, it might seem familiar to users of Google Photos. The new features are similar to Google's Magic Eraser tool and do not offer the same capabilities as Google's Magic Editor, which can alter scene lighting or rearrange subjects.

Adobe's Generative Remove reflects the company's ongoing approach to AI, as seen with last year's AI-powered noise removal tool. These enhancements build on existing tools, providing practical improvements rather than groundbreaking new features.

This focus on better tools over flashy innovations likely aligns with what working photographers seek. Adobe appears content to let others handle more dramatic AI-powered capabilities, like post-capture scene rearrangement.

Want to know more about generative AI? Read the article below:

Generative AI from an enterprise architecture strategy perspective

Eyal Lantzman, Global Head of Architecture, AI/ML at JPMorgan, gave this presentation at the London Generative AI Summit in November 2023.

AI Accelerator InstituteEyal Lantzman

Sony is making strides in the surgical robotics market with its newly developed microsurgery assistance robot. The Tokyo-based company recently announced its advanced system designed for automatic surgical instrument exchange and precision control.

Robot development and functionality

Sony, known for its electronic technologies, created this robot to assist in microsurgical procedures. The robot, used in conjunction with a microscope, works on extremely small tissues such as veins and nerves.

It tracks the movements of a surgeon’s hands and fingers using a highly sensitive control device. These movements are then replicated on a small surgical instrument that mimics the movement of the human wrist.

Addressing practical challenges

The new system aims to overcome practical challenges conventional surgical assistant robots face, such as interruptions and delays from manually exchanging surgical instruments. Sony’s R&D team achieved this through the miniaturization of parts, allowing for automatic exchange.

The system comprises a tabletop console operated by a surgeon and a robot that performs procedures on a patient. The surgeon’s hand movements on the console are replicated at a reduced scale (approximately 1/2 to 1/10) at the tip of the robot arm’s surgical instrument. Sony’s researchers envision the robot assistant being used in various surgical procedures.

Key features of the robot

1. Automatic instrument exchange

One of the robot’s standout features is its ability to automatically exchange instruments, made possible by the miniaturization of surgical tools. Multiple instruments can be compactly stored near the robot arm, allowing the left and right arms to make small movements to exchange tools quickly without human intervention.

2. High-precision control

The robot employs a highly sensitive control device to provide stable and precise control necessary for microsurgical procedures. This compact and lightweight device accurately reflects the delicate movements of human fingertips.

The tip of the surgical instrument, designed with multiple joints, moves smoothly like a human wrist. Sony aims for the robot to enable nimble operation and smooth movements that feel almost imperceptible to the user.

3. Advanced display technology

Equipped with a 1.3-type 4K OLED microdisplay developed by Sony Semiconductor Solutions Corporation, the robot provides operators with high-definition images of the surgical area and the movement of instruments.

4. Performance and testing

In February, Sony conducted an experiment at Aichi Medical University. Surgeons and medical practitioners who do not specialize in microsurgery used the prototype to create an anastomosis in animal blood vessels successfully.

Sony claims this was the world’s first instance of microvascular anastomosis using a surgical assistance robot with an automatic instrument exchange function.

Expert insights

Munekazu Naito, a professor in the Department of Anatomy at Aichi Medical University, remarked on humans' superior brain and hand coordination, which allows for precise and delicate movements.

He noted that mastering microsurgery typically requires extensive training, but Sony’s robot demonstrated exceptional control over novice surgeons' movements.

This technology enabled them to perform intricate tasks with skills comparable to experienced experts. Naito hoped surgical assistance robots would expand physicians' capabilities and enhance advanced medical practices.

Future development and goals

Sony plans to collaborate with university medical departments and medical institutions to further develop and validate robotic assistance technology's effectiveness. The ultimate goal is to resolve issues in the medical field and contribute to medical advancements through innovative robotic technology.

Read more about how AI and computer vision are advancing healthcare by reading the eBook below:

Computer Vision in Healthcare eBook 2023

Unlock the mystery of the innovative intersection of technology and medicine with our latest eBook, Computer Vision in Healthcare.

AI Accelerator InstituteMarisa Garanhel

This article is based on Ismail Madni’s brilliant talk at the Product Marketing Summit in Austin, hosted by our sister community, Product Marketing Alliance.

More and more AI capabilities are being added to product roadmaps every single day. Even companies that aren’t using true AI are still incorporating increasingly advanced capabilities into their product. 

This gives us all a golden opportunity to rethink how we price our offerings and the story behind that pricing – and that’s what I’m excited to talk to you about today.

A brief history of software pricing models

Let's start by taking a quick look at the history of software pricing models. 

We need to understand not just pricing and packaging, but also the storytelling around it. However, back in the 80s and 90s, there really wasn't much of a pricing story to be told. It was mostly one-time, large upfront purchases for on-premise software. You'd have some annual maintenance fees too. The story was just “this is the cost versus the value.”

In the late 90s and early 2000s, we saw the rise of cloud products with subscription models like Salesforce – monthly, annual, or multi-year recurring payments. It was cheaper upfront and the products were constantly updated, so there was real value there. But the pricing story didn’t evolve much – it was just “it's cheaper to buy with a subscription.”

Today, we see a lot of subscription and usage-based pricing models. I'm a big fan of usage-based pricing because it directly ties the cost to the value the customer receives – the actual outcomes you're providing them. It's much more of a pay-as-you-go approach. 

There are tons of examples of usage-based pricing in B2B – examples like Zapier (per task/zap), Eventbrite (per event), Snowflake, and AWS (per resource). Their pricing directly ties to the outcomes being delivered. It's a win-win – vendors have to keep providing value, while customers only pay for what they actually use. 

How to tell a story with usage-based pricing

AI capabilities fit beautifully into the usage-based approach and the stories you can craft around it. The usage metrics you select as pricing inputs can directly shape that narrative.

Are you creating new workflows and saving time? Are you making people more efficient? Broadly speaking, AI is going to do one of those things – help users move faster, be more productive, save money, or enable new ways of working. 

Your value drivers dictate the value metrics, which in turn suggest the pricing approach. If your product enables new workflows, you can price based on that workflow enablement. If it helps coding or copywriting happen faster, you can price accordingly – per article or line of code, for instance.

How to craft compelling messaging around your pricing strategy

To see how you can use your pricing model as the foundation of a story that resonates with buyers, let’s look at a couple of real-world examples – one from Intercom and one from GitHub.

MediaTek has unveiled the Dimensity 9300+, its newest addition to the Dimensity series of mobile chips.

The Dimensity 9300+ features enhanced clock speeds and is engineered to boost the processing of on-device generative AI. It supports a wide range of large language models (LLMs) and provides several other performance improvements compared to its predecessor, the Dimensity 9300.

JC Hsu, MediaTek's Corporate Senior Vice President, emphasized, "The Dimensity 9300+ will enhance our ability to foster a vibrant generative AI application ecosystem through extensive LLM support and on-device LoRA Fusion capabilities. It delivers remarkable enhancements and speeds for LLM inference, processing tokens much more quickly, thus improving the overall user experience."

Advanced core and AI engine

The chip incorporates an All-Big-Core architecture utilizing TSMC’s third-generation 4nm process. It includes one Arm Cortex-X4 core clocked at up to 3.4 GHz, along with three Cortex-X4 cores and four Cortex-A720 cores.

MediaTek has significantly advanced AI processing capabilities in the Dimensity 9300+ through its new NeuroPilot Speculative Decode Acceleration technology, part of the company's cutting-edge generative AI engine, the APU 790. This engine supports LLMs across a spectrum from 1 billion to 33 billion parameters and enhances the efficiency of running LLMs.

According to MediaTek, the Dimensity 9300+ can process LLMs with seven billion parameters at a speed of 22 tokens per second, more than double the processing rate of comparable mass-market solutions.

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Gaming performance and connectivity enhancements

For gamers, the Dimensity 9300+ includes a second-generation hardware raytracing engine powered by an Arm Immortalis-G720 GPU. This setup provides rapid raytracing capabilities at a seamless 60 FPS and supports console-like global illumination effects. The chip also benefits from MediaTek’s newest HyperEngine gaming technologies.

The chip's MediaTek Adaptive Gaming Technology (MAGT) optimizes power efficiency during gameplay in popular titles, helping to prolong battery life and maintain device coolness.

Furthermore, the integration of HyperEngine’s Network Observation System (NOS) enhances WiFi and cellular network performance simultaneously and utilizes advanced network prediction technology. When activated, MediaTek’s NOS can save up to 10% in power and up to 25% in cellular data usage.

Imaging and AI video capabilities

The chip's Imagiq 990 ISP supports 18-bit RAW processing, enabling superior photo and video quality in low-light conditions. This ISP includes a built-in AI Semantic Analysis Video Engine that provides features like real-time video capture and scene segmentation to improve video quality.

Additionally, the Dimensity 9300+ incorporates MediaTek’s MiraVision 990, featuring sophisticated AI depth engine technologies to enhance visual content on smartphones.

Support for a range of AI applications

The chip is designed for a broad array of AI applications, providing:

• Support for on-device LoRA Fusion and NeuroPilot LoRA Fusion 2.0, which aids developers in rapidly launching new generative AI applications involving text, images, and music.
• Compatibility with the latest LLMs, including 01.AI Yi-Nano, Alibaba Cloud Qwen, Baichuan AI, ERNIE-3.5-SE, Google Gemini Nano, and Meta Llama 2 and Llama 3.
• ExecuTorch Delegation for deployed on-device inferencing.

The Dimensity 9300+ also features a 5G R16 modem that supports 4CC-CA Sub-6GHz, capable of achieving download speeds of up to 7Gbps and is equipped with AI-driven situational awareness.

Computer vision is expanding quickly and has the potential to completely change how we interact with technology, being at the forefront of many cutting-edge advancements, from self-driving automobiles to augmented reality.

Reading a computer vision book can be an excellent approach to learning and acquiring insight into this field and its applications.

From the principles of computer vision to more advanced technologies, these books will provide you with a thorough overview of the area and its applications – whether you’re a student, researcher, or professional.

In this article, you’ll find 12 of the best books on computer vision:

• Computer Vision: Algorithms and Applications (Texts in Computer Science)
• Computer Vision: Principles, Algorithms, Applications, Learning
• Multiple View Geometry in Computer Vision
• Computer Vision: Models, Learning, and Inference
• Computer Vision: A Modern Approach (International Edition)
• Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
• Modern Computer Vision with PyTorch: Explore Deep Learning Concepts and Implement Over 50 Real-World Image Applications
• Learning OpenCV 4 Computer Vision with Python 3: Get to Grips with Tools, Techniques, and Algorithms for Computer Vision and Machine Learning
• Deep Learning for Vision Systems
• Concise Computer Vision: An Introduction into Theory and Algorithms
• Computer Vision Metrics: Survey, Taxonomy, and Analysis
• Programming Computer Vision with Python: Tools and Algorithms for Analyzing Images

Computer Vision: Algorithms and Applications (Texts in Computer Science), by Richard Szeliski

This computer vision book looks at the variety of techniques involved in analyzing and interpreting images, and describes real-world applications where vision is used successfully – in both specialized applications and consumer-level tasks.

The book takes a scientific approach to the formulation of computer vision issues, which are analyzed using classical and deep learning models and solved through rigorous engineering principles.

Often referred to as the “bible of computer vision”, it’s a must-read for those at a senior level, as it acts as a general reference text for fundamental techniques.

Computer Vision: Principles, Algorithms, Applications, Learning, by E. R. Davies

Davies covers computer vision’s fundamental methodologies while he explores its theoretical side, such as algorithmic and practical design limitations. Made for undergraduate and graduate students, researchers, engineers, and professionals, the book offers an up-to-date approach to modern problems.

The latest edition also includes:

• A new chapter on object segmentation and shape models.
• Three new chapters on machine learning, two covering basic classification concepts and probabilistic models, the other the principles of deep learning networks.
• Personalized programming examples: illustrations, codes, hints, methods, and more.
• Discussions on the EM algorithm, RNNs, geometric transformations, semantic segmentation, and more.
• Examples and applications of developing real-world computer vision systems.
• And more.

Multiple View Geometry in Computer Vision, by Richard Hartley & Andrew Zisserman

This textbook covers mathematical principles and techniques of multiple view geometry, a key area in computer vision. It also goes over the basic theory of projective geometry, which is the geometry of image formation, and the estimation of camera motion and structure from image sequences.

You’ll also find insights into image rectification, 3D scene recovery, and stereo correspondence. The book explains how to define objects in algebraic form for more straightforward computation, and it addresses the main geometric principles. It offers you a clear understanding of computer vision’s structure in a real-world scenario.

Computer Vision: Models, Learning, and Inference, by Simon J. D. Prince

The book Computer Vision: Models, Learning, and Inference offers a thorough introduction to the subject. The book covers a wide range of topics, such as picture generation, feature detection and extraction, object recognition, motion analysis, and machine learning techniques to enhance computer vision systems' performance.

Along with a review of current research, it also contains a thorough description of computer vision's mathematical and statistical underpinnings. The book offers a thorough and current introduction to the discipline and is written with graduate students, researchers, and practitioners of computer vision and related fields in mind.

Computer Vision: A Modern Approach (International Edition), by David A. Forsyth

This computer vision book covers the essential ideas and methods of computer vision. It’s divided into four main sections, starting with an introduction to computer vision and a rundown of its foundational mathematical techniques.

Image formation, covering subjects like image sensing, picture processing, and image analysis, is covered in the book's second section. Object recognition, encompassing subjects like feature detection and matching, object recognition, and object tracking, is covered in the third section of the book.

The book's concluding section discusses complex subjects including stereo, motion, and scene analysis. The writers illustrate the theories and methods covered in the book using a range of real-world examples and applications.

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow, by Anirudh Koul, Siddha Ganju & Mehere Kasam

This book combines the Python programming language, the Keras and TensorFlow libraries, and several computer vision techniques to walk you through constructing actual projects using deep learning approaches for the cloud, and for mobile and edge devices.

It offers practical examples and code snippets to aid with your comprehension while covering subjects like picture classification, object identification, and video analysis. Developers, data scientists, and engineers who want to understand how to apply deep learning to create useful projects for the cloud, mobile platforms, and edge devices should read this book.

Modern Computer Vision with PyTorch: Explore Deep Learning Concepts and Implement Over 50 Real-World Image Applications, by V Kishore Ayyadevara & Yeshwanth Reddy

Many recent developments in various computer vision applications are fueled by deep learning. This book uses a practical way to teach you how to use PyTorch1.x on real-world datasets to solve more than 50 computer vision problems.

Ayyadevara and Reddy take you through how to train a neural network from scratch with NumPy and PyTorch. Plus, how to combine computer vision and NLP to perform object detection, how to deploy a deep learning model on the AWS server by using FastAPI and Docker, and much more.

Learning OpenCV 4 Computer Vision with Python 3: Get to Grips with Tools, Techniques, and Algorithms for Computer Vision and Machine Learning, by Joseph Howse and Joe Minichino

If you want to learn how to use the OpenCV library to develop computer vision and machine learning applications, then this book is for you. Howe includes practical examples and code snippets to help you understand the principles and their applications while covering a wide range of topics. These include image processing, object detection, and machine learning.

The computer vision book is also created for readers with some Python programming knowledge and is based on OpenCV 4, the most recent release of the library.

Deep Learning for Vision Systems, by Mohamed Elgendy

Building intelligent, scalable computer vision systems that can recognize and respond to things in pictures, movies, and real life is something you can learn how to do with Deep Learning for Vision Systems.

You’ll understand cutting-edge deep learning techniques such as:

• Image classification and captioning
• An intro to computer vision
• Transfer learning and advanced CNN architectures
• Deep learning and neural networks

Concise Computer Vision: An Introduction into Theory and Algorithms, by Reinhard Klette

In this book, Klette offers a general introduction to the core ideas in computer vision, emphasizing key mathematical ideas and methods. At the end of each chapter, the book provides programming exercises and quizzes.

The book covers a wide range of related computer vision subjects, including mathematical ideas, picture segmentation, image recognition, and the fundamental parts of a computer vision system.

Computer Vision Metrics: Survey, Taxonomy, and Analysis, by Scott Krig

Krig offers a thorough explanation of the many metrics applied in computer vision. The book provides an overview of the state-of-the-art in computer vision metrics, a taxonomy of metrics based on their properties, and an evaluation of their advantages and disadvantages.

The book covers a wide range of subjects, such as performance evaluation for machine learning-based computer vision systems, object recognition and tracking, and image and video quality assessment.

Programming Computer Vision with Python: Tools and Algorithms for Analyzing Images, by Jan Erik Solem

This is a thorough computer vision reference for the Python programming language. The book covers a wide range of topics, including 3D reconstruction, object recognition, feature extraction, and image processing. Additionally, it offers a summary of the most well-known Python libraries and frameworks for computer vision, including OpenCV, scikit-image, and scikit-learn.

It includes more complex topics like feature extraction, object recognition, and 3D reconstruction too, and provides an introduction to computer vision and the fundamentals of image processing, such as image filtering, thresholding, and color spaces.

You can learn and apply the principles covered with the aid of real-world examples and code snippets. The book also provides a detailed explanation of the algorithms used, which is especially helpful if you want to go deeper into the theory behind computer vision.

Want more about computer vision? Check out our Computer Vision in Healthcare eBook:

Computer Vision in Healthcare eBook 2023

Unlock the mystery of the innovative intersection of technology and medicine with our latest eBook, Computer Vision in Healthcare.

AI Accelerator InstituteMarisa Garanhel

In today's rapidly evolving digital landscape, artificial intelligence (AI) stands as a transformative force with the potential to revolutionize industries, spur innovation, and drive economic growth.

However, unlocking the full potential of AI requires overcoming significant computational challenges. This is where AI accelerators come into play. AI accelerators, specialized hardware designed to optimize AI workloads, play a crucial role in accelerating AI adoption, powering economic growth, and fostering social development.

Acceleration of AI adoption

AI accelerators serve as catalysts for the widespread adoption of AI technologies across various sectors. By enhancing the speed and efficiency of AI computations, these specialized hardware solutions enable organizations to harness the power of AI for a wide range of applications, from autonomous vehicles and healthcare diagnostics to finance and cybersecurity.

Furthermore, AI accelerators facilitate the deployment of AI models at scale, making it feasible for businesses of all sizes to integrate AI into their operations.

Driving economic growth

The widespread adoption of AI, fueled by AI accelerators, has the potential to drive significant economic growth.

By automating repetitive tasks, optimizing processes, and enabling data-driven decision-making, AI technologies enhance productivity and efficiency across industries, leading to increased output and competitiveness. Moreover, AI-powered innovations create new business opportunities, drive entrepreneurship, and stimulate job creation.

As businesses leverage AI to improve products and services, enhance customer experiences, and unlock new revenue streams, they contribute to economic expansion and prosperity.

Fostering social development

Beyond economic benefits, AI accelerators play a vital role in fostering social development and addressing pressing societal challenges. AI-driven solutions have the potential to revolutionize healthcare delivery, improving diagnostics, personalized treatment plans, and patient outcomes.

In education, AI-powered tools can enhance learning experiences, personalize instruction, and bridge gaps in access to quality education. Furthermore, AI technologies enable advancements in areas such as environmental monitoring, disaster response, and public safety, contributing to the well-being and resilience of communities worldwide.

Challenges and opportunities

While AI accelerators hold immense promise, their widespread adoption faces challenges such as high costs, technical complexity, and ethical considerations.

Addressing these challenges requires collaboration between governments, industry stakeholders, and the research community to develop affordable, accessible, and ethical AI solutions.

Moreover, investments in AI education and workforce development are essential to ensure that societies can fully harness the benefits of AI technology while mitigating potential risks.

Conclusion

In conclusion, AI accelerators play a pivotal role in accelerating AI adoption, driving economic growth, and fostering social development.

By enhancing the speed, efficiency, and scalability of AI computations, these specialized hardware solutions empower organizations to unlock the full potential of AI across various sectors.

As we navigate the digital age, embracing AI accelerators presents an unprecedented opportunity to harness the power of AI for the benefit of economies and societies worldwide, ushering in a new era of innovation, prosperity, and human progress.

Help shape the generative AI industry by sharing your expertise:

Generative Artificial Intelligence Report 2024

We’re diving deep into the world of generative artificial intelligence with our new report: Generative AI 2024, which will explore how and why companies are (or aren’t) using this technology.

AI Accelerator InstituteMarisa Garanhel