March 2023 - TVS Next

Latest developments in cloud computing

Bhavya Upadhyay Digital Engineering, Modernization March 20, 2023

Cloud computing is a rapidly evolving technology transforming how businesses and organizations manage their IT infrastructure. In recent years, we’ve seen significant developments in cloud computing that have led to greater flexibility, scalability, and cost savings for users.

Are you keeping up with the latest trends in cloud computing? It can be hard to keep track of all the new features and products being released, but it’s crucial to stay on top of what’s happening in this rapidly evolving industry. In this blog post, we’ll look at the latest buzz around cloud computing, including new features from major providers and exciting innovations to watch out for. Whether you’re a cloud expert or just getting started, there’s something here for everyone. So, let’s dive in!

Trends in Cloud Computing

Artificial Intelligence and Machine Learning

The advancement of artificial intelligence (AI) and machine learning (ML) has taken great strides in the past few years, with cloud technology leading the change. With cloud computing, AI development is faster, more secure, and more scalable than ever. This has enabled developers to quickly create algorithms that can take on more complex tasks such as natural language processing, computer vision, and analytics.

By leveraging this powerful combination of sophisticated software algorithms and powerful cloud computing capabilities, researchers have made massive advancements in artificial intelligence and machine learning research that were previously impossible – making way for an even brighter future for artificial intelligence and machine learning. Some AI-based industries boosted by cloud advancements include predictive analytics, personalized healthcare, and antivirus models.

Cloud Gaming

The gaming industry is one of the most advanced sectors in leveraging the potential of cloud technology. It has revolutionized how game developers work and how gamers access their favorite video games. For example, streaming services such as Google Stadia and Microsoft Xbox have allowed gamers to instantly access sophisticated games without needing a console or powerful PC. Similarly, cloud-based development platforms are creating a faster rate of innovation for developers, offering them more powerful tools to make innovative and exciting content.

Cloud storage has been an essential factor driving the advancements in gaming technology by allowing gamers to easily save files, characters, and progressions while playing on different devices. Cloud computing is further being used to integrate virtual reality with the gaming industry, poised to unlock great opportunities for gamers soon. Thus, it can be seen that cloud technology has undoubtedly enhanced user experience within the gaming industry.

Multi-Cloud Solutions

Multi-cloud solutions represent a powerful advancement in the way businesses manage their data. As the amount of data organizations produce increases, multi-cloud solutions offer an effective, efficient way to store and access this information. Multi-cloud enables companies to store data across multiple cloud provider platforms while affording them flexibility as they scale up or down over time without disrupting their workflow.

It also minimizes costs since businesses don’t have to incur fees with just one provider or manage all their data on-site. Additionally, with these solutions, businesses can obtain strong security capabilities, reassuring customers that their data is reliably protected. There are many advantages for businesses looking for an efficient way to manage their increasing storage requirements, and multi-cloud solutions are proving to be the ideal solution.

Remote and Hybrid working

Cloud advancements have revolutionized the way businesses and working professionals approach remote working. Not only has it enabled transitioning to remote work with greater ease and speed than ever before, but advances in cloud storage have created greater opportunities for collaboration, communication, and monitoring of project progress while working outside of an office setting.

For example, cloud-based programs like Dropbox and Google Drive make it easy to share documents among distributed teams with version control systems, while video conferencing solutions such as Zoom help teams stay connected so that workers can feel the same sense of community and team spirit found in a traditional workplace. Thanks to these new technologies, those who were once confined by location can now tap into distant opportunities, enabling them to capitalize on their skill sets from anywhere in the world.

Benefits of Advancements in Cloud Computing

Conclusion

As businesses worldwide increasingly rely on cloud computing, the need to utilize modern cloud computing services is becoming increasingly critical. The trends in cloud computing are towards increased use of cloud infrastructure and services. Smaller companies can access enterprise-level resources, and larger companies require greater scalability and speed in an increasingly competitive market. Cloud solutions allow businesses of all sizes to operate with improved efficiency, cost savings, and the ability to quickly deploy updated technology across distributed networks – making it essential for any business hoping to succeed in our digital world.

Evolution of Generative Artificial Intelligence for Text (ChatGPT)

Alex Thompson Data and AI March 14, 2023

Many companies and research organizations that are pioneers in AI have been actively contributing to the growth of generative AI content by bringing in & applying different AI models to fine-tune the precision of the output.

Before discussing the applications of generative AI in text and large language models, let’s see how the concept has evolved over the decades.

RNN sequencing

After researchers proposed the seq2seq algorithm, which is a class of Recurrent Neural Networks (RNN), it was later adopted & developed by companies like Google, Facebook, Microsoft, etc., to solve Large Language Problems.

The element-by-element sequencing model revolutionized how machines conversed with humans, yet, it had limitations and drawbacks like grammar mistakes and bad semantic sense.

LSTM

RNN suffered from a problem called Vanishing Gradient. LSTM (Long Short Term Memory) and GRU (Gated Recurring Unit) were introduced to address this issue.

Though in structure, they remain the same, LSTM preserves the context/information present in the initial part of the statement by preventing the issue of Vanishing Gradient. To retain the part of the statement, it introduced cell state and cell gates with layers such as forget gate, input gate, and output gate.

Transformer model

While LSTM was a rock star during its time in the NLP evolution, it had issues such as slow training and lack of contextual awareness due to a one-directional process. Bi-directional LSTM learned the context in forward & backward directions and concatenated them. Still, it was not ahead and back together, and it struggled to perform tasks such as text summarization and Q&A that deal with long sequences. Enter, Transformers. This popular model was introduced with improved training efficiency. Also, the model could parallelly process the sequences, based on which many text training algorithms were developed.

UNILM

Unified language model was developed from a transformer model, BERT – Bi-directional Encoder Representations. In this model, every output element is connected to every input element, and the language co-relation between the words was dynamically calculated. AI content improved with the tuning of algorithms and extensive training.

Text to Text Transfer Transformer, with text as input, generates target text. This is an enhanced language translation model. It had a bi-directional encoder and a left-right decoder pre-trained on a mix of unsupervised and supervised tasks.

BART

Bi-directional & auto regressive transformers, a model structure proposed in 2020 by Facebook. Consider it as a generalization of BERT and GPT. It combines ideas from both the encoder and decoder. It had a bi-directional encoder and a left-to-right decoder.

GPT: Generative Pre-trained Transformer

GPT is the first autoregressive model based on Transformer architecture. Evolved as GPT, GPT2, GPT3, GPT 3.5 (aka GPT 3 Davinci-003) pre-trained model, which was fine-tuned & released to the public as ChatGPT (based on InstructGPT) by OpenAI.

The backbone of the model is Reinforcement Learning from Human Feedback (RLHF). It’s continuously human-trained for text, audio, and video.

This version of GPT converses like a human to a great extent, which is why this bot has a lot of hype. With all the tremendous efforts that went into scaling AI content, companies are striving to make it more human-like.

More Large Language and Generative AI models were built and released by Google (BARD based on Language Model for Dialogue Applications (LaMDA), HuggingFace (BLOOM), and the latest from Meta Research LLaMA, which was open-sourced.

Application of ChatGPT and Generative AI models

With companies expanding their investment in data analytics to use the power of data to derive critical insights, we must discuss AI bots’ role in data analytics.

The applications of Generative AI and ChatGPT are vast. From generating a new text, answering questions conversing like a human, assisting developers with generating code, explaining code, writing newsletters, blogs, social media posts, and articles (This post was not written by ChatGPT ) to sales reports and generating new images and audio, ChatGPT can do it all.

As we read in the earlier paragraph on various applications of Generative AI, different models come into play for the same. We continue to see ChatGPT experiences from people of various backgrounds and industries. How and where can enterprises use ChatGPT?

As you know, ChatGPT is Language Model. Its application is predominantly in “Text” and tasks that require human-like conversation, taking notes in a meeting, composing an email, writing content, and increasing developers’ productivity.

Key challenges in using open-source AI bots for data analysis

Most data analysis projects deal with sensitive data. Large organizations sign agreements on data privacy protection with customers and the government that prevents them from disclosing sensitive information to open-source tools.

That’s why organizations must understand what kind of support the data engineering team looks for from AI bots and ensure no sensitive information is disclosed.

A known risk: AI models have continuously evolved to ensure improved accuracy. This implies that there is definitely room for errors. The open-source conversational bots, even if well-trained to perform certain activities, hold no responsibility for the output it provides. You need the right eye to ensure the AI gets the correct data, understands it, and does what it should.

Responsible governance & corporate policies

Technology is fast evolving and has the entire world working on it such that innovations, new tools, and upgrades are happening in the flash of an eye. It’s so compelling to try new tools for critical tasks. But, every organization must ensure the right policies are in place to responsibly handle booms or sensations like ChatGPT.

Get buy-in for your data modernization initiative

Alex Thompson Data and AI, Modernization March 6, 2023

If you want to convince your top management to get buy-in for your data modernization initiatives, you are in the right place.

Let’s brainstorm on how we can achieve this. When you want to convince someone of your idea to modernize, you must be prepared to answer all the questions the decision-makers may have. Here are some:

What is the scope of data modernization you are talking about?

When you say ‘modernization,’ you could be talking about:

Whatever your goal, it is essential to explain it clearly — the receiving end should know WHAT CHANGE you are trying to make for the betterment of your organization.

What are the problems your business faces with legacy systems?

Frame the problem statement. Since the Stone Age, necessity has been the mother of all inventions. So, build a strong case for why you need to modernize your organization’s data platform.

Data security issues or compliance risks your company faces since your legacy platform is not designed to tackle modern-day problems.
Your database/source might be incompatible with modern applications.
Inability to address customer demands and ever-changing business requirements, which necessitates a modernized data environment.
Troubleshooting legacy systems is not easy as the type of issues that old data setups have are difficult to tackle. Moreover, you may not have readymade solutions from the service provider handling your data.
Another critical reason is finding the right talent pool — getting new-generation developers to work on legacy systems is challenging. Most developers look for a modernized tech stack to work.

Your business may have more biting problems by following traditional data systems & approaches — bring those challenges out on the table!

What benefits would your organization or business reap from data modernization?

Of course, the benefits depend on the nature of your business and what you’re trying to achieve through data modernization.

But, the expected benefits that any organization can count on receiving through data modernization would be:

The enhanced support you get from the modernized platform
Modernized databases and solutions are designed with data privacy, security & performance in mind and ensure higher efficiency
Get on-demand services at reduced costs, as you pay only for what you need. The data market is highly competitive, so solution providers ensure you get cost-effective solutions. You don’t have to pay for additional solutions that your business may not need. Find out which data solution your business needs and whether your solution provider can cater to the needs within your budget. Mention these factors as critical information during your meeting.
You can even leverage more resources or cancel resources for specific data solutions. This kind of elasticity definitely will kindle the interest of your management .
Organizational transformation

Are you talking the language of successful people?

Now, we’re not talking about using a new language. It’s about the universally known language — numbers and statistics to impact how others understand your business proposals.

Thats right! Numbers are critical in decision-making. It will be much better if you have data about how your competitor benefited from data modernization or even how your industry is benefiting. Many research organizations like Gartner and PWC publish free articles that you can use to substantiate your goals.

Also, when you explain your proposal to modernize your data setup, you must explain what the project will mean for your organization regarding numbers.

The best thing to do is to talk about how much you will save costs through data modernization. Any decision maker would need to know about your company’s return on investments (ROI) before they nod to anything new or consider the proposal brought to them.

How do you plan to achieve data modernization?

It’s time to talk about the crux of it.

What data solution are you proposing?
Delivery method – Is data modernization going to be achieved by your own company, or does it have to be outsourced?
Get a full-length program plan if your company will do the data modernization.

For instance, if you are doing a database migration.

Done! Now that you have everything you need to get your top management interested in data modernization, you are all set to go!