Leading the AI transformation of your company
Prof. Gregory LaBlanc, Lecturer, Haas School of Business and Berkeley LawWatch Now
11:45 Minutes The average reading duration of this insightful report.
AIoT is a revolutionary blend of AI and IoT that creates a connected world with limitless opportunities. Smart devices can collaborate to make informed decisions without human intervention, transforming various industries. As AI and IoT converge, their applications will become more advanced, presenting new prospects for businesses and consumers.
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AIoT combines sensors, AI, data and ambient computing elements to create a responsive, context-aware environment. It uses embedded devices and natural user interfaces to provide services based on detected requirements and user input.
AIoT can revolutionize how users interact with technology, offering greater convenience and seamless connectivity. The benefits include: intuitive and seamless experience without commands, automated decision making, efficiency and convenience.
While creating an AIoT system, a well-balanced architecture is crucial to manage data processing speed and costs. There is a flow of information in the system based on the external inputs, that ultimately results in a response based on analysed data points by AI and ML algorithms. Download Complete Research
The 5 step enterprise strategy include the following:
Use Cases for following domains are discussed:
Implementing a complex system like AIoT requires careful planning, collaboration, and attention to detail. Data management, privacy concerns, and integration with various systems can pose significant obstacles to successful implementation.
The AIoT space is dominated by key players such as IBM, Microsoft, Siemens, GE, Cisco, Huawei, ABB, Bosch, SAP, and Honeywell. Download Complete Research
14:40 Minutes The average duration of a captivating reports.
Foundational for identity verification and rights access, government-issued IDs face breaches and inefficiency within centralized systems. Enter Decentralized Identity solutions, redistributing verification control to individuals. But is it applicable for equally for all government services?
Decentralization of government-issued IDs is a complex issue with potential benefits and drawbacks. Whether government issued Ids should be decentralized or not depends on various factors that we delineate. Any decision on decentralization should be carefully considered and implemented with caution to ensure that they do not breach security or undermine the government’s programs. Download Complete Research
We detail out the current Issuing process typically followed by governments today and identify several challenges and issues that face it today. This includes Ids like the Passport, Licenses, Voter cards and Social security cards. All of them are critical and everyone can do with an easier and more fool proof process for the same.
We evaluate the different government functions such as Education, healthcare, elections, security, taxation, etc and analyze which of these would be most suitable to be decentralized. We map them on a matrix of Complexity & Coordination and the Need for scrutiny to give us an easy framework for assessment. Download Complete Research
We end by considering the long term onjectives and intended outcomes of such an exercise.We feel that Decentralized Identity solutions can rebuild trust in public institutions by empowering residents with data control.
12:49 Minutes The average duration of a captivating reports.
Generative AI is forming a new economic ecosystem, reshaping the behaviour of key players in the IT industry, generating opportunities for super-scalers, and unveiling numerous niches for startups. The outlines of this new IT landscape are emerging, prompting a closer examination.
Generative AI has caused significant disruption, expanding its offerings and services well beyond traditional AI domains. This has led to an explosion of potential use cases for customers who aren't AI experts. Unlike before, customers no longer require a team of AI experts, curated data, or precisely measurable outcomes to adopt AI tool and gain immediate benefits. The interaction with GenAI is so seamless and intuitive that the onboarding for new customers is frictionless, eliminating barriers to adoption and facilitating rapid technology spread. The high variability in potential inputs and priming of generative models allows for a diverse range of applications impacting nearly every imaginable aspect of people activities. This is a foundation of a new era of Artificial Intelligence.
In this primer we leveraged our knowledge of 50+ GenAI-related and VC-backed startups to reconstruct the technological stack of the forming GenAI space.
Large tech companies are leveraging their existing technological and capital advantages to create the framework for the GenAI market landscape, which we are going to explore in this section.
While offering of the LLMs on the current scale and heavy focus on unstructured data are somewhat new, the other elements of the tech stack closely mirror those needed for any large computational modeling. Established companies in the field of traditional AI are at an advantage, as they can expand and repurpose preexisting software, infrastructure, and services. Download Complete Research
While large players are occupying a sizable portion of the GenAI tech stack, there remains more than enough room for GenAI startups to flourish. The landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly.
Bottom-right (AIOps): Here, startups may offer tools for easier adoption of LLMs, facilitating the initial process of customizing and implementing these models.
Ascending (Integration): Moving upwards represents the process of integrating LLMs into various applications and business operations. Startups could offer integration services, templates, or frameworks to streamline this, or build an entire end to end app for a selected market niche.
Moving left (Service platforms): As we move leftwards, the focus shifts from core LLM functionality to auxiliary services. This could range from platforms offering specialized training data, to marketplaces for LLM apps, to optimization tools. These firms may automate the need for certain experts.
This taxonomy can serve as a foundational overview for anyone looking to understand the current state of the LLM ecosystem. It’s also worth noting that the landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly. Let’s inspect each block in greater detail:
The future of the GenAI landscape is going to be defined by several processes:
While enhancing the users with great capabilities, the LLM-based service is neither a freebie, nor a cornucopia. Each implementation of LLMs carries its own advantages and downsides. In this section of the Appendix, we discuss what can and cannot be realistically expected from a GenAI model in each of the most popular use cases.
We start with primary properties of a pre-trained LLM model, underlying its strong sides and functionalities as well as build-in flaws. And we move to the current ways of augment LLM model to work around the flaws. Download Complete Research
The table of 78 startups we have based our analysis on is presented in this section.
The states of startups are set to the August of 2023.
Author@lab45: Rinat Sergeev
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