Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request.

Generative AI relies on sophisticated machine learning models called deep learning models algorithms that simulate the learning and decision-making processes of the human brain. These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions and respond with relevant new content.

AI has been a hot technology topic for the past decade, but generative AI, and specifically the arrival of ChatGPT in 2022, has thrust AI into worldwide headlines and launched an unprecedented surge of AI innovation and adoption. Generative AI offers enormous productivity benefits for individuals and organizations, and while it also presents very real challenges and risks, businesses are forging ahead, exploring how the technology can improve their internal workflows and enrich their products and services. According to research by the management consulting firm McKinsey, one third of organizations are already using generative AI regularly in at least one business function.¹ Industry analyst Gartner projects more than 80% of organizations will have deployed generative AI applications or used generative AI application programming interfaces (APIs) by 2026.

The current growth of mobile data usage and emergence of new applications have greatly motivated the Third Generation Partnership Project (3GPP) to work on Long Term Evolution (LTE). LTE is the most recent standard in the mobile network technology to be developed based on GSM/EDGE and UMTS/HSPA network technologies with the aim of optimizing the capacity and speed of 3G mobile communication networks. In this chapter, the structures and features of fourth generation (4G) LTE are investigated at the early stages of telemedical research. The chapter also provides a comparison of WiMAX and LTE standards on various aspects, as well as the potential of technology in healthcare applications. Issues and challenges of wireless technologies in healthcare applications and services are finally presented.

Getting Ready For GA4: Saving Your Historical Data
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Ref: https://www.searchenginejournal.com/ga4-historical-data/447519/#close


Migrating your data to GA4 is not currently possible. Here's what you can do instead to prepare before the July 2023 deadline.

As you’re preparing to set up Google Analytics (GA4), you’re probably asking the same thing we were: What’s the best method for migrating our historical data?

There should be a way to do this, right?

In this column, you’ll learn whether we can merge data in GA4 and three DIY ways to save your historical data.

Can You Migrate Your Data To GA4?
The primary concern is whether GA users can transfer or migrate Universal Analytics data into their Google Analytics 4 property.

Unfortunately, you cannot migrate your data to GA4, and it’s not likely to be a feature we’ll see added in the coming months.

Migrating your data to GA4 is not likely to be a feature because the two versions use completely different data models.

I spoke with Charles Farina, Head of Innovation at Adswerve, and he explained that:

“It is the difference in schema and dimension definitions/calculations that make merging the data not possible.”

Schema refers to how the data is organized and the language used to ensure compatibility. Essentially it is the blueprint.


He explains you can see the differences in schema well when comparing the BigQuery integrations for UA and GA4.

“The UA export is sess-ionized, meaning each row in the export is a session, and every interaction is nested in that row. The GA4 export is very different, where each row is the event (interaction) itself,” shared Farina.

Another key reason is how dimensions and metrics are defined and calculated in GA4 compared to UA.

Google has a great support page that goes over many of these.

For example, let’s look at one of the most common KPIs, “Users.”

Universal Analytics reports on Total Users or all users, while GA4 focuses on Active Users or users that have visited the website at least once in the past 28 days.


Even if we could migrate UA data to GA4, it would be like comparing apples to oranges.

If you’re wondering why this change is happening, you’ll find the answer in our article, Google Analytics 4 FAQs: Stay Calm & Keep Tracking.

How To Export Google Analytics Historical Data
Google does empathize and encourages users to export their historical data.

“We know your data is important to you, and we strongly encourage you to export your historical reports during this time.”

Most principal investigators spend many hours laboring over proposals to fund their research programs – and for good reason. While competing for funding is the big business for researchers, some have opted to fund their programs in other ways, like building a research consortium.

Most principal investigators spend many hours laboring over proposals to fund their research programs – and for good reason. While competing for funding is the big business for researchers, some have opted to fund their programs in other ways, like building a research consortium.

The most powerful computers of the day have typically been called supercomputers. They have historically been very expensive and their use limited to high-priority computations for government-sponsored research, such as nuclear simulations and weather modeling.

Many researchers are not concerned with the idea of AI surpassing human intelligence in the near or immediate future. This is also referred to as superintelligence, which Nick Bostrum defines as “any intellect that vastly outperforms the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” Despite the fact that Strong AI and superintelligence is not imminent in society, the idea of it raises some interesting questions as we consider the use of autonomous systems, like self-driving cars. It’s unrealistic to think that a driverless car would never get into a car accident, but who is responsible and liable under those circumstances? Should we still pursue autonomous vehicles, or do we limit the integration of this technology to create only semi-autonomous vehicles which promote safety among drivers? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops.

AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach. The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols—whence the symbolic label. The bottom-up approach, on the other hand, involves creating artificial neural networks in imitation of the brain’s structure—whence the connectionist label.

As the name suggests, the AI multi-agent shopping system is a shopping assistant, more like a recommendation engine. First, the system is fed with the details of a wide range of products. When a user registers in the system and searches for a specific product, the system returns the result of the most relevant items that match the search keywords entered by the user.

Why R&D?

What is research? And what is development? A layman would ask these questions to begin with. Follow-up questions would be: Does development really require research? Finally people may ask why develop? Why take such a hassle? Let’s copy and do something. The R&D story will end before it begins with this type of attitude. This is pretty similar to the Sri Lankan context which has resulted in lowly performance as a country.

What we need to understand is that the innovation through R&D is the major competitive advantage a country could have. According to an address by Dr. A.P.J Abdul Kalam to a gathering of tech students at Mahindra Ecole College in 2015, he claims that there are two sets of countries – developed countries and developing countries. The difference between these two are the competitiveness. Dr. Kalam takes the Global Competitiveness Index as an example. If we look at current rankings, Singapore is ranked one in the index.