ChatGPT-The new paradigm shift

Network Science March 01, 2023

The phenomena!
2022 was a watershed year in evolving the way people interacted with Artificial Intelligence. DALLE-2 captured the imaginations of everyone in the world at a scale which would have made its namesake painter proud. ChatGPT wrote movie scripts, party planning ideas, code and explained millions of concepts to people without having to read 10 websites for understanding. In 4 months, the world found alternatives for Google search, photoshop, Stack Overflow, and script writing. I personally used some of these tools to get inspirations for pitch decks and feature sets for enterprise technologies and occasioned an interesting conversation between Shakespeare and Douglas Adams in the style of their characters.
This excitement goes beyond acceptance of new paradigms of search and creation. Proof of its popularity is evidenced by its incredible rate of adoption which till last count was about 100 Million monthly active users just on ChatGPT. The insanity compounds with the enormous flow of investments from Microsoft in Open AI with an aim to disrupt Google search. To keep pace with the new paradigm, Google has backed Anthropic as its own horse in the race.

Source:https://www.statista.com/chart/29174/time-to-one-million-users/
Shifting paradigms
Though the introduction of Artificial Intelligence on a global stage is not new, it feels a little different from what the past has shown us. In the age of AR/VR, we saw companies focusing bringing AR and VR to the forefront. Similarly, with blockchain and drones, the technology was at the center of the value proposition. However, AI/ML was playing the old sage, hiding its hard work behind an outcome that many believed could have been done with some basic coding and hard work themselves. In engaging with a website chatbot, or outputs from a predictive engine, users often viewed the output of complex analysis as something that can be created with enough time on excel or by searching hard enough. However, moving to generic text and image has changed that. AI’s abilities seem closer, and at times beyond, finished products of professionals. Search is not about narrowing your universe anymore, but finding exactly what you are looking for. Likewise, creation is now focused on bringing fantastical ideas come to life where the limiter is your ideas. This paradigm shift is powerful beyond what we can comprehend today. What seems like joyous tools to complete your job swiftly can open paths of infinite possibilities leading to democratised creation.

The Tomorrow
Today tools such as ChatGPT or Chatsonic are giving you the scaffolding around complete solutions which can be easily configured or edited to your designed results. However, getting more contextual results and providing the ability to ad-hoc dynamically will move the usage of these tools from starting points to being complete solution suits. Social media is already flooded with bundled AI tools for advertisers, marketers, designers and more. In the coming weeks, we will see more specialised uses of AI bundling. This will lead to a complex network of AI’s talking to each other possibly through a single interface. I know this might sound a bit like steps towards Terminator coming back to kill Sarah Conner, but till that happens, this is good news for creation. Such paradigms of AI can expand the scope of who we call creators. Lack of specialised software skills will not equate to a lack of quality. Tomorrow a diligent college student might be able to win an Oscar, or a grandmother set up a sustainable D2C brand. I obviously wax lyrical to a fanciful future, but stranger things have happened.
Even if I don’t think robot from the future is likely, it doesn’t mean there are not concerns regarding this new paradigm. This form of AI will limit growth of millions of medium to even high skilled jobs. There is a growing notion that many jobs will be replaced by skilled people who know how to use AI rather than AI’s on their own. There is truth to the notion, however one does question the value you would assign to expert work. There are other issues such as biased training, questions around ownership, ethical dilemmas, lack of transparency, and privacy concerns, but each of these points deserves an in-depth study on it’s own.

The Conclusion
Artificial Intelligence is here to stay and its adoption will have good and bad consequences. However, arming yourself with the right skills to harness the potential seems like the best course of action, at least in the near term. AI is already disrupting my job by getting me access to specialised information for which I would have to spend a couple of hours studying. However, using it without adding my experience and knowledge will also lead to my downfall. After all, DALL-E 2 can make beautiful paintings in the style of Eduard Munch, but, at least today, it can’t convey the raw inner turmoil behind The Scream. That still needs some ‘human’ editing.

The Scream by Edvard Munch

ChatSonic prompt: Make a painting depicting anxiety and inner turmoil in the style of Edvard Munch

This blog has been contributed by Nikhil Mahen, Deep Tech Consultant at Network Science. You can write to Nikhil at nikhil@networkscience.ai for any queries regarding this blog.

How does Big Data Analytics help in constant innovation?

Network Science February 08, 2023

What are DAM Solutions?

DAM solutions are platforms that help organizations manage, store, and distribute their digital assets such as images, videos, audio files, and documents. These solutions typically include features such as metadata management, search and discovery, access control, and analytics.

What is the Market Size for DAM?

• • Global:
    The DAM solution market size is expected to grow significantly in the coming years. According to a market research report by MarketsandMarkets, the global Digital Asset Management market size was valued at $3.4 billion in 2020 and is expected to reach $7.4 billion by 2025, growing at a compound annual growth rate (CAGR) of 17.8% during the forecast period.
    The increasing adoption of digital technologies, growing need for efficient management of digital assets, and rising demand for cloud-based solutions are some of the key factors driving the growth of the DAM market. Additionally, the increasing need for compliance and security, as well as the growing adoption of artificial intelligence (AI) and machine learning (ML) in DAM solutions, are also expected to boost the market growth.
    The market is segmented into solutions, services, deployment models, organization size, verticals and regions. The solutions segment is further divided into solutions such as digital asset management, video management, and digital rights management. Services segment is divided into professional services and managed services. Deployment models are segmented into on-premises and cloud.
    North America is expected to hold the largest market share, while Asia Pacific is expected to grow at the highest CAGR during the forecast period.
  • India:
    In India, the market for Digital Asset Management (DAM) solutions is growing as more organizations recognize the need for efficient management of their digital assets. Industries such as media and entertainment, advertising, and manufacturing are some of the major adopters of DAM solutions in India. DAM is a very relevant tool for the Pharmaceutical & CPG industry where digital asset creation and management is an extensive requirement. The market is also driven by the increasing use of digital channels for marketing and the growing demand for content creation and distribution in India.
    There are a number of companies that offer DAM solutions in India, including global players such as Tenovos, Adobe, Widen, and Bynder, as well as Indian companies such as InData Labs, PixelVault, and Webkul. These companies offer both on-premise and cloud-based solutions, catering to organizations of different sizes and industries.
    However, the market for DAM solutions in India is still relatively small compared to more developed markets such as North America and Europe. This is likely to change in the future as more organizations in India adopt digital technologies and recognize the need for efficient management of their digital assets.
    With the awareness about DAM still being relatively lower in India, adopting it can give a unique First-Mover advantage to a lot of organisations in this competitive market.
    • Competitive Analysis:
      According to a study by Forrester conducted in early 2022, Aprimo, Adobe, Widen, Bynder emerge as the long-established market leaders for DAM globally. However, the above solutions are not very cost effective and aren’t the first choice in the Indian market.
      There is a classification of other “Strong Performers” which are newer, intuitive solutions driven by AI and ML. These include the likes of Tenovos, OpenText and Sitecore, which have a decent market share and are also easier on the pockets for clients. These are solutions which have proven more acceptable in the Indian Market.

Why Use DAM?
Digital Asset Management (DAM) solutions can provide a variety of benefits in terms of return on investment (ROI), including:

1. Increased efficiency: DAM solutions can help organizations streamline their workflow, reducing the time and effort required to find, retrieve, and use digital assets.
2. Improved collaboration: DAM solutions can make it easier for teams to share and collaborate on digital assets, resulting in more productive work and faster time-to-market.
3. Better asset utilization:DAM solutions can help organizations more effectively use their digital assets, leading to increased revenue and reduced costs.
4. Enhanced security: DAM solutions can provide robust security features that help organizations protect their digital assets from unauthorized access or use.
5. Improved brand consistency: DAM solutions can help organizations maintain consistent branding across all their digital assets, resulting in a more professional and cohesive image.
6. Better tracking and reporting: DAM solutions can provide detailed analytics and reporting on asset usage and performance, which can help organizations make more informed decisions about their digital assets.

Overall, a well-implemented Digital Asset Management (DAM) solution can lead to increased productivity, improved collaboration, reduced costs, and enhanced security, all of which can contribute to a strong ROI.
In our bid of “Changing the World with DeepTech Innovation”, Network Science, is proud to add another solution in our portfolio. Digital Asset Management (DAM) opens up avenues to collaborate with previously unexplored Industry Verticals: Advertising, Media and Entertainment.

This blog has been contributed by Swapnil Bang, Deep Tech Consultant at Network Science. You can write to Swapnil at swapnil@networkscience.ai for any queries regarding this blog.

How does Big Data Analytics help in constant innovation?

Network Science July 25, 2022

The concept of big data has been around for years; most organisations now understand that if they capture all of the data that flows into their businesses (potentially in real time), they can apply analytics and derive significant value from it. Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from diverse, high-volume, high-velocity data sets. These data sets may come from a variety of sourcesoffice365 torrent, including the web, mobile, email, social media, and networked smart devices. They frequently contain data that is generated at a high rate and in a variety of formats, ranging from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files).

Big Data Analytics Processes

By analysing data sets, analytics solutions gain insights and predict outcomes. However, before the data can be successfully analysed, it must first be stored, organised, and cleaned by a series of applications in a step-by-step preparation process:

1. Gather Data: Every organisation’s approach to data collection is unique. With today’s technology, businesses can collect structured and unstructured data from a variety of sources, including cloud storage, mobile apps, in-store IoT sensors, and more. sketchup crack 2018 itaSome data will be stored in data warehouses, where it will be easily accessible by business intelligence tools and solutions. A data lake can be used to store raw or unstructured data that is too diverse or complex for a warehouse.
2. Process Data: Once data has been collected and stored, it must be properly organized in order to produce accurate results on analytical queries, especially when the data is large and unstructured. Data availability is increasing at an exponential rate, making data processing difficult for organizations. Batch processing, which examines large data blocks over time, is one processing option. When the time between collecting and analysing data is long, batch processing comes in handy. Stream processing examines small batches of data at once, reducing the time between collection and analysis and allowing for faster decision-making. Stream processing is more difficult and frequently more expensive.
3. Clean Data: To improve data quality and get stronger results, all data must be formatted correctly, andkms activator any duplicate or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, resulting in flawed insights.
4. Analyse Data: It takes time to convert big data into usable information. Once ready, advanced analytics processes can transform big data into big insights. Big data analysis methods:
   1. Data mining searches large datasets for patterns and relationships by detecting anomalies and forming data clusters.
   2. Predictive analytics makes future predictions based on an organisation’s historical data, identifying upcoming risks and opportunities.
   3. Deep learning mimics human learning patterns by layering algorithms and finding patterns in the most complex and abstract data.

Different types of Big Data Analytics

1. Diagnostic Analytics: Diagnostic analytics is an advanced type of big data analytics that can be used to investigate data and content. Using this type of analytics, you can answer the question, “Why did it happen?” So, by analysing data, you can understand the reasons for certain behaviours and events related to the company you work for, their customers, employees, products, and so on.
2. Descriptive Analytics: Descriptive analytics is one of the most common types of analytics used by businesses to stay current on current trends and operational performance. It is one of the first steps in analysing raw data by performing simple mathematical operations and producing statements about samples and measurements. After identifying trends and insights with descriptive analytics, you can use other types of analytics to learn more about what causes those trends.
3. Prescriptive analytics: Prescriptive analytics uses descriptive and predictive analysis resulcs6 master collection serial number generatorts to find solutions for optimising business practises using various simulations and techniques. It employs data insights to recommend the best course of action for the company.

Why is Big Data Analytics so important?
Big data analytics assists businesses in harnessing their data and utilising it to identify new opportunities. As a result, smarter business decisions, more efficient operations, higher profits, and happier customers follow. Businesses that leverage big data and advanced analytics gain value in a variety of ways, including:

1. Cost-cutting measures: When it comes to storing large amounts of data, big data technologies such as cloud-based analytics can significantly cut costs (for example, a data lake). Furthermore, big data analytics assists businesses in finding more efficient ways to conduct business.
2. Making faster and more accurate decisions: The speed of in-memory analytics, combined with the ability to analyse new data sources, such as streaming data from IoT, allows businesses to analyse information quickly and make informed decisions.
3. New product and service development and marketing: Analytics enables businesses to provide customers with what they want when they want it. With big data analytics, more businesses can develop innovative new products to meet the changing needs of their customers.

Big data analytics can ultimately fuel better and faster decision-making, model and predict future outcomes, and improve business intelligence. A scalable analytics solution can benefit any organisation that works with large amounts of data, which is why many major industries, including retail, entertainment, and healthcare, already use big data to establish strategy, lower costs, and predict customer needs.

Unleashing Machine Learning: The Rationale Behind Smart Predictions

Network Science July 11, 2022

Machine Learning (ML) & Artificial Intelligence (AI) albeit recent technologies, are by no means new. Historically, machines have been used for several tasks and functions that were otherwise deemed as trivial and dirty for humans. Over time, these machines have evolved to take on more complex & sophisticated activities including decision making and strategy formulation. However, as complexities increase, there is a growing need for people to trust in the power of these machines rather than question their abilities.

A recent study by Accenture Labs has revealed that Machine Learning, especially deep learning, is quickly seeing an upsurge in its adoption in workplaces across industries. In healthcare, for instance, hundreds of companies are using Machine Learning algorithms and predictive analytics to reduce drug development time and diagnose ailments from medical images. Similarly, in the transportation sector, self-driving cars using ML are expected to become a norm within the next couple of years, with commercial applications of these automobiles being close behind.

What is Machine Learning?

These intelligent systems take on low-level pattern recognition tasks like image recognition, speech recognition and natural language processing to help companies churn large volumes of data for making specific recommendations. ML allows software systems to provide users with accurate predictions with minimal uncertainty.

The internal algorithms involved in this decision-making process, however, are often not visible to company personnel, making ML systems operate as ostracized “black boxes”. This makes organizations unwilling to allocate core competencies to machines due to higher risks of poor decision making and related costs.

Research indicates that in the upcoming years, machines will be compelled to explain their reasonings and recommendations in a deeper manner. As the next stage of human augmentation by machines, this interaction will enable people to understand and act responsibly. It will work towards creating an effective team between humans and machines.

Source – https://www.statista.com/chart/17966/worldwide-artificial-intelligence-funding/

Machine Learning Synergy

Intelligent systems powered by ML are now here to work alongside their human counterparts. By utilizing smart machines for responsibility, fairness, and transparency, organizations can enforce collaboration & efficiency within their workplaces. These advanced intelligent systems of the future, however, will not replace people. They will complement and support humans in a manner that allow businesses to make smarter, better and more accurate decisions.

There are 3 main market drivers for advanced ML-led systems. First, the growing need for transparency, as required by laws such as the EU’s GDPR, makes it essential for companies to disclose how personal data is being used for selection and other decision-making. Second, a growing need for trust between AI and human beings mandate that systems are able to effectively explain the rationale behind their decision-making. Third, the need for better machine-human synergy. With machines being better at recognizing minute patterns in large volumes of data and people being more efficient at connecting the dots among high-level patterns, the businesses of tomorrow are going to increasingly need both resources working hand-in-hand.

So, how exactly can Machine Learning convey the rationale behind outputs better? Research has identified three different methods to achieve this:

1. 1. Data-level explanation – Through this method, ML-based systems can provide evidence of the modeling and its results using comparisons made with other examples. This allows the system to justify the decision taken around any particular issue or targeted prediction.
   2. Model-level explanation – This approach focuses more on the Machine Learning algorithms. Through this method, the explanation provided makes the logic more understandable to humans by adding a layer of domain knowledge on top. Compared to the other methods, model-level explanation abstracts most from the data through rules or by combining it with semantics.
   3. Hybrid-level explanation – This approach works the best and is most useful if the data being studied is particularly large, complex or packed. The method uses a high level of abstraction by refactoring data at a metadata level. Rather than using the data as an evidence as in the case of other methods, the hybrid-level explanation offers explanation for every feature at a metadata level.

Enhanced ML will allow sophisticated systems to:

1. 1. • Explain the reasoning behind their results and how they arrived at them.
      • Characterize the system’s strengths & weaknesses.
      • Compare their performance & output with those of other intelligent machines.
      • Convey results in a comprehensive manner that showcases the potential of future technologies.
      • Make the decision-making process in businesses smarter.

Why Zero-Code App Development is important?

Network Science June 28, 2022

We are powered by code in our daily lives. Programming is what allows us to do things like check our bank accounts, like our friends’ photos on social media, and shop for new clothes on our favorite e-commerce sites. But what if we told you that you didn’t have to write any code to create such apps? It is entirely possible to accomplish this using Zero-Code/No-Code application development platforms.

So, what is Zero-Code or No-Code? Zero-Code is a technique that allows you to create software, web applications, smartphone apps, and other applications without knowing how to code. You can create your ideal software by dragging and dropping readymade/pre-existing building blocks. What distinguishes no-code is the ability to automate processes, digitize operations, and bundle them into everyday apps without manually writing code.

No-code is simply an abstraction layer that sits on top of code. It translates the fundamentals of code into simple drag-and-drop solutions, allowing creators to build modern apps and websites visually. AppExe, by Mobilous, is an AI-based Rapid Mobile Application Development (RMAD) platform that provides all of the functionality of HTML5, CSS, and Javascript, but you don’t need to know any of these programming languages to get started. Where once only skilled programmers could create apps and launch web applications, no-code development platforms and a wealth of tutorials can get anyone on their way to getting their ideas out there. Being a non-programmer is no longer an issue.

Why is zero-code app development the future of application development?

• • Drag-and-drop interface: The ease of use is one of the primary reasons why no-code development platforms have gained popularity and expanded rapidly. This is primarily due to the fundamental feature of drag and drop. You can simply drag and drop the feature you need to create for your application using this feature. It allows you to complete the task quickly.
  • Data connections: Most no-code application development platforms either come pre-configured with database and server-side software or provide a simple user interface for connecting to your preferred database. A good no-code platform not only assists you in developing user-facing visuals but also in developing data management and processing capabilities that run behind the scenes to assist you in completing your business workflows.
  • User Interface Builder and Visual Modeling: Most no-code platforms include a user interface builder that allows users to quickly assemble preconfigured elements to create a website and application. To create functionality in no-code platforms, simply drag and drop components in a logical sequence. Basically, no coding is required; everything has already been created or visually modelled; all you have to do is drag, drop, and arrange!
  • Integrations: Every day, the world becomes more connected. Most businesses use a variety of applications and seek connectivity in order to improve and automate collaboration and workflow. Most no-code platforms provide a number of integrations with other software, allowing you to connect your data and processes without having to duplicate or perform manual work. API integrations allow you to easily integrate the apps you create with a no-code platform with a variety of web applications such as YouTube, Google Maps, WhatsApp, Slack, Twitter, and others.
  • Reduced cost: Developers are not cheap. No-code solutions allow you to avoid this, removing much of the overhead associated with keeping a skilled team of developers on staff at all times. In the long run, you can build apps faster and cheaper.
  • Easily changeable: The issue with traditional hand coding is that you can’t change functionality or feature at the drop of a hat, especially if you’re coding in a foreign language. You can do it without a code. If something needs to be changed, you simply implement new logic and have your change ready in a matter of hours.

With no-code development, Organisations can create a plethora of internal and external-facing tools without involving IT at all. There is no maintenance burden, no additional IT overhead, just pure productivity and hopefully a bit of fun building it. Businesses that prioritise agility and simplicity over other factors are the best candidates for no-code platforms. They can begin developing new applications in minutes with little or no customization to complete their tasks.