Рубрика: AI

The practical application of generative artificial intelligence has gone from an abstract, future concept to a concrete reality in a matter of mere months. Businesses and organizations large and small are scrambling to figure out if and how AI can help their people be more productive and efficient. For organizations using Microsoft software, the application of AI in a business environment is being led by the Microsoft Copilot platform.

Businesses at the enterprise level are also looking for ways AI can leverage the massive amounts of data generated daily by their organizations in increasingly productive ways. Many believe that such a massive undertaking can only be accomplished by a competent AI platform. Microsoft Copilot, with its integration into Microsoft 365, Azure, Windows and enterprise-wide data streams, is purported to be the AI that unlocks the creative and productive potential of an organization’s people and data.

Jump to:

• What is Microsoft Copilot?
• What are business benefits to using Microsoft Copilot?
• How does Microsoft plan to integrate Copilot into its applications?
• What are potential problems and caveats with Microsoft Copilot?
• What are the alternatives to Microsoft Copilot?
• How much does Microsoft Copilot cost?
• Which businesses should consider Microsoft Copilot, and which should not?
• When will Microsoft Copilot be available?

What is Microsoft Copilot?

Microsoft Copilot is a new AI product that combines the power of large language models with in-house enterprise data generated by the Microsoft Graph and Microsoft 365 applications. Using the power of AI and natural language conversations, users can find better answers to their questions and potentially create content from those answers. Copilot was developed on the ChatGPT platform and announced as an in-development platform at the July 2023 Microsoft Inspire conference.

There are two versions of Copilot: Microsoft 365 Copilot and a more general Microsoft Copilot. It is important to note that Microsoft 365 Copilot is different from the consumer and small business-oriented Copilot platform found in Bing Chat or non-enterprise versions of Microsoft 365. These Copilot LLMs are trained on more generalized aggregate data gathered across the internet and therefore tend to have more generalized results. Microsoft 365 Copilot will be dependent on the data generated by a specific, and only a specific, enterprise.

Microsoft 365 Copilot will be dependent on in-house, enterprise-generated data, while the more general Microsoft Copilot will use aggregate data pulled from the internet.

Both Copilot versions will be embedded in the Microsoft 365 apps including Word, Excel, PowerPoint, Outlook and Teams. This complete Microsoft 365 integration will allow an organization’s workers to be more creative and unlock productivity gains and potentially improve their skills. In addition, Microsoft 365 Copilot will add AI-enabled Business Chat to the productivity suite, which will work across enterprise-specific data like calendars, emails, chats, documents, meetings and contacts to help employees communicate easier and better.

What are business benefits to using Microsoft Copilot?

Assuming Microsoft Copilot works as advertised, an employee could jump-start a project (e.g., email, presentation, report, data visualization) with an AI-generated first draft. With that foundation, the employee can quickly move on to refining and iterating a second draft and then a final draft, cutting at least one step from the process. This should make the employee more efficient and productive, and it could allow for the development of new skills.

For Microsoft 365 Copilot, the key to these productivity gains will be the application of enterprise-specific data to the new project from the start. Using data internally generated by the enterprise from emails, documents, calendars, contacts and so on, presumably the project will be jump-started with limited, and more importantly, pertinent assets.

SEE: Hiring kit: Prompt engineer (TechRepublic Premium)

For example, if an enterprise employee at Ford is creating a presentation that needs to show movement, the AI – if properly trained – should use a Ford-related product rather than a similar product from one of its competitors. An AI trained by data culled from the internet may come to a different conclusion and defeat the purpose of deploying enterprise-specific generative AI.

How does Microsoft plan to integrate Copilot into its applications?

Copilot will be integrated into the fabric of all Microsoft 365 applications. When an employee starts a Word document, reads an email in Outlook, opens an Excel report or updates a PowerPoint presentation, Copilot and its generative AI abilities will be there to assist when called upon.

The specifics of how each Microsoft 365 application will use Copilot are still in development, and many use cases will likely only be discovered when users can actually use the platform. However, Microsoft has outlined some of its basic ideas for Copilot in a business environment.

Below are examples of commands a user might give Microsoft Copilot.

Copilot in Word

• Draft a two-page project proposal based on data gleaned from a Word document (i.e., either the one you are currently working on or one that you specify by name) and an Excel worksheet.
• Make the third paragraph in the current document more concise and change the tone of the document to be more casual.
• Create a one-page draft based on this rough outline.

Copilot in Excel

• Break down this sales data by type and channel and then insert a table.
• Project the impact of a variable change in this data and then generate a chart to help visualize it.
• Model how a change to the growth rate for a variable would impact my gross margin.

Copilot in PowerPoint

• Create a five-slide presentation based on this Word document and include relevant stock photos.
• Consolidate this presentation into a three-slide summary.
• Reformat these three bullets (in a specific PowerPoint presentation) into three columns, each with a picture.

Copilot in Outlook

• Summarize the emails missed while out of the office last week and flag any important items.
• Draft a response thanking the senders of an email and asking for more details about their second and third points.
• Shorten a draft email and make the tone more professional.
• Invite everyone to a lunch-and-learn about new product launches next Thursday at noon. Mention that lunch is provided.

Copilot in Business Chat

• Summarize the chats, emails and documents about the topic being discussed.
• What is the next milestone for a project? What risks were identified? Brainstorm a list of potential mitigations.
• Write a new planning overview in the style of this (specified existing) document that contains the planning timeline from a different document and incorporate the project list in the email from this person (specifying a user’s name).

What are potential problems and caveats with Microsoft Copilot?

All AI platforms are only as good as their training; if Microsoft Copilot is modeled after data that is incomplete, biased, wrong or otherwise corrupt, the suggestions it generates, regardless of who is asking, will be incomplete, biased, wrong or otherwise corrupt. The old adage of garbage in equals garbage out still applies.

For Microsoft 365 Copilot, training will be particularly important because all the data used for that Copilot AI platform will be internally generated and gathered. Enterprises looking to properly apply AI will have to carefully monitor the data Copilot can access. For example, a business may not want brainstorming documents and meeting notes to be part of the data stream; rejected ideas may taint the data stream and propagate throughout the organization.

For general versions of Microsoft Copilot, organizations and users will have to be aware that inherent biases, fads, misguided trends and other transitory events will likely color some of the AI-generated output. Not every passing fancy on the internet should make its way into official organizational documents.

In addition to this potential problem, especially for Microsoft 365 Copilot implementations, is the tendency of some departments to silo their data behind firewalls. New products and services developing under non-disclosure agreements, for example, will often be cut off from the normal organizational data stream. Businesses will have to decide whether hiding data from the AI in such cases is more beneficial than allowing AI access.

The most important caveat for Microsoft Copilot is the platform is still in development. The use cases outlined by Microsoft at the 2023 Inspire conference are the company’s visions of how the platform will work. Once Copilot is released to the public, what the AI platform is capable of, good or bad, will be revealed. Until then, we will be dealing with potential and perhaps some wishful marketing.

What are the alternatives to Microsoft Copilot?

Generative AI is arguably the hottest trend in technology innovation for 2023, so it stands to reason there are many new and in-development AI platforms ready to compete with Microsoft Copilot.

Even though Copilot is based on ChatGPT, the AI chatbot is available as a standalone platform and therefore should be considered a competitor. AI is already incorporated into Microsoft Edge in the form of Bing Chat, and an AI has recently been released for the Google search engine and Chrome. Technology experts have reported that Apple is developing its own AI platform. It seems that AI will be integrated into just about every digital application we use on a computing device.

Major tech companies including Salesforce, Oracle and Adobe are all working on AI platforms. There are also dozens of smaller independent developers working on their own versions of an AI platform. Plus, there are a multitude of AI competitors working on specialized platforms that will bind LLMs and generative AI principles to specific applications. Businesses will likely spend a lot of time wading through AI platform possibilities.

How much does Microsoft Copilot cost?

As revealed during the 2023 Inspire conference, Microsoft 365 Copilot will cost $30/user/month. At first glance, this price point seems expensive, but it is vital to remember this version of the platform is designed for large business enterprises. For a large enterprise with thousands of employees, and assuming the platform delivers what Microsoft promises, that $30 could end up being a bargain. That is a big assumption to make at this early point in development.

The consumer and SMB versions of Microsoft Copilot will likely be priced lower than the enterprise counterpart. Bing Chat, which is also based on ChatGPT, is available now and for free as an integral part of Microsoft Edge. It is also likely that some features restricted or otherwise modified version of Copilot will be available for SMBs too small to have meaningful in-house generated data available to train the AI platform.

The pricing for all the versions of Microsoft Copilot is likely to change as development of the platform continues. With the large number of competitors in the AI platform space, it seems almost certain the cost of these services will change significantly.

Which businesses should consider Microsoft Copilot, and which should not?

At $30/user/month, only certain large enterprises will be able to afford large numbers of employees subscribing to the Microsoft 365 Copilot platform. In addition to the subscription fee, such employers will also have to account for the extra expenses associated with generating, collecting and collating accurate and useful data for the AI and LLMs to train on. This is a major undertaking, and the decision to implement the Copilot platform will take a significant commitment.

Large enterprises will have to decide whether the productivity benefits of using the Microsoft 365 Copilot platform outweigh the initial costs of developing and maintaining the platform – and then paying for it indefinitely.

For individuals and small businesses with little to no LLM-ready data, the consumer level versions of Microsoft Copilot is available for free. Bing Chat is already available in Microsoft Edge for everyone. Some form of low-cost or no-cost version of Copilot will likely be available for certain versions of Microsoft 365.

Only time will tell if the generative AI capabilities of Copilot are worth the time necessary to use them.

When will Microsoft Copilot be available?

As of August 2023, Microsoft Copilot is in the testing phase of development. A limited number of organizations and Microsoft Insiders are testing the AI and providing feedback on what works and what does not work.

There is currently no officially announced release date for any version of Microsoft Copilot.

Visit Microsoft Copilot

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Generative AI, one of the hottest growing technologies, is used by OpenAI's ChatGPT and Google Bard for chat and by image generation systems such as Stable Diffusion and DALL-E. Still, it has certain limitations because these tools require the use of cloud-based data centers with hundreds of GPUs to perform the computing processes needed for every query.

But one day you could run generative AI tasks directly on your mobile device. Or your connected car. Or in your living room, bedroom, and kitchen on smart speakers like Amazon Echo, Google Home, or Apple HomePod.

Also: Your next phone will be able to run generative AI tools (even in Airplane Mode)

MediaTek believes this future is closer than we realize. Today, the Taiwan-based semiconductor company announced that it is working with Meta to port the social giant's Lllama 2 LLM — in combination with the company's latest-generation APUs and NeuroPilot software development platform — to run generative AI tasks on devices without relying on external processing.

Of course, there's a catch: This won't eliminate the data center entirely. Due to the size of LLM datasets (the number of parameters they contain) and the storage system's required performance, you still need a data center, albeit a much smaller one.

For example, Llama 2's "small" dataset is 7 billion parameters, or about 13GB, which is suitable for some rudimentary generative AI functions. However, a much larger version of 72 billion parameters requires a lot more storage proportionally, even using advanced data compression, which is outside the practical capabilities of today's smartphones. Over the next several years, LLMs in development will easily be 10 to 100 times the size of Llama 2 or GPT-4, with storage requirements in the hundreds of gigabytes and higher.

That's hard for a smartphone to store and have enough IOPS for database performance, but certainly not for specially designed cache appliances with fast flash storage and terabytes of RAM. So, for Llama 2, it is possible today to host a device optimized for serving mobile devices in a single rack unit without all the heavy compute. It's not a phone, but it's pretty impressive anyway!

Also: The best AI chatbots of 2023: ChatGPT and alternatives

MediaTek expects Llama 2-based AI applications to become available for smartphones powered by their next-generation flagship SoC, scheduled to hit the market by the end of the year.

For on-device generative AI to access these datasets, mobile carriers would have to rely on low-latency edge networks — small data centers/equipment closets with fast connections to the 5G towers. These data centers would reside directly on the carrier's network, so LLMs running on smartphones would not need to go through many network "hops" before accessing the parameter data.

In addition to running AI workloads on device using specialized processors such as MediaTek's, domain-specific LLMs can be moved closer to the application workload by running in a hybrid fashion with these caching appliances within the miniature datacenter — in a "constrained device edge" scenario.

Also: These are my 5 favorite AI tools for work

So, what are the benefits of using on-device generative AI?

• Reduced latency: Because the data is being processed on the device itself, the response time is reduced significantly, especially if localized cache methodologies are used by frequently accessed parts of the parameter dataset.
• Improved data privacy: By keeping the data on the device, that data (such as a chat conversation or training submitted by the user) isn't transmitted through the data center; only the model data is.
• Improved bandwidth efficiency: Today, generative AI tasks require all data from the user conversation to go back and forth to the data center. With localized processing, a large amount of this occurs on the device.
• Increased operational resiliency: With on-device generation, the system can continue functioning even if the network is disrupted, particularly if the device has a large enough parameter cache.
• Energy efficiency: It doesn't require as many compute-intensive resources at the data center, or as much energy to transmit that data from the device to the data center.

However, achieving these benefits may involve splitting workloads and using other load-balancing techniques to alleviate centralized data center compute costs and network overhead.

In addition to the continued need for a fast-connected edge data center (albeit one with vastly reduced computational and energy requirements), there's another issue: Just how powerful an LLM can you really run on today's hardware? And while there is less concern about on-device data being intercepted across a network, there is the added security risk of sensitive data being penetrated on the local device if it isn't properly managed — as well as the challenge of updating the model data and maintaining data consistency on a large number of distributed edge caching devices.

Also: How edge-to-cloud is driving the next stage of digital transformation

And finally, there is the cost: Who will foot the bill for all these mini edge datacenters? Edge networking is employed today by Edge Service Providers (such as Equinix), which is needed by services such as Netflix and Apple's iTunes, traditionally not mobile network operators such as AT&T, T-Mobile, or Verizon. Generative AI services providers such as OpenAI/Microsoft, Google, and Meta would need to work out similar arrangements.

There are a lot of considerations with on-device generative AI, but it's clear that tech companies are thinking about it. Within five years, your on-device intelligent assistant could be thinking all by itself. Ready for AI in your pocket? It's coming — and far sooner than most people ever expected.

Artificial Intelligence

Data science was a vaguely defined discipline to begin with, but it’s shaped up substantially lately. Execs now yearn to take immediate advantage of generative and other clearly useful (if currently problematic) kinds of AI.

That demand suggests an opportunity for influencers and visionaries in organizations to lobby for each organization to build an AI-ready data foundation, one that supports hybrid AI (knowledge graph + statistical machine learning) as well as existing ML modeling efforts. Otherwise, companies won’t be able to scale their AI efforts.

Role articulation needs for the AI-enabled enterprise

Data scientists are busy enough with statistical machine learning models and doing the data prep needed to create useful models. And data engineers are consumed with creating pipelines and tapping and making accessible the resources the data scientists and others need. The specialists staffing these roles are too focused on their own disciplines to worry about the hows and whys of semantic knowledge graphs and architectural transformation overall.

It’s obvious that other roles need to be created or updated to complement the data scientist and data engineering roles. If as Andrew Ng says, better data beats better algorithms, don’t enterprises need to focus on creating the kind of findable, accessible, interoperable and reusable (FAIR) data most suited to large-scale AI efforts? What about the need for knowledge engineers, architects, ontologists, taxonomists and stewards to establish means of ownership and sharing the disparate kinds of FAIR data that’s best managed and scaled via a knowledge graph?

What’s missing from discussion of AI is a suitable data foundation for enterprise-wide AI. Organizations often just don’t think first about how to scale their AI efforts, or even ponder why a foundation is required. Their blind spot seems to reflect a passive attitude about cloud computing and an assumption that clouds are AI-ready to begin with.

The truth is that public software as a service (SaaS) puts the data interests of cloud providers first. To counter that tendency and the fragmentation that comes with thousands of different SaaS subscriptions, enterprises need to stake out much more of their own data territory. They can do that with the help of data-centric (rather than application-centric architecture) and data architects who can guide real AI-scaling transformation.

Multi-purpose architecture and its flexibility benefits

Think for a moment from a building architect’s point of view and the case of a multi-purpose commercial building. Just as today’s new buildings must often begin as multi-purpose, today’s AI must be multi-use. Otherwise, the inefficiency in having to tear down and rebuild the foundation for each use from scratch will become overwhelming.

The building architect’s challenge today is to envision the design of a building that will be flexible in its uses. The trend lately here in the US and perhaps elsewhere as well is to start with the concept of an urban village and assume that most buildings in the village will be suitable for residential, retail and office purposes at a minimum. Some buildings, of course, will continue to be built for one purpose, but those will be the exception, not the rule.

The AI-ready enterprise will need the same kind of foundational flexibility. With a proper, interoperable data foundation–the kind a good knowledge graph can provide–an enterprise can grow its own data, rules and processes from that foundation to suit its AI needs.

Building owners need more and more flexibility to lease out the space they own. These multi-purpose buildings start as shells, with each floor its own tabula rasa for designing what current needs are. As those needs change, parts of each floor can be redesigned to accommodate different kinds of tenants.

For example, here in San Jose where I live, the downtown buildings need to provide more residential than office space, considering that workforces do quite a bit more work from home now. Residential space is at a premium. Google began with one grandiose office + other purposes plan for its downtown San Jose campus before the pandemic. Now, their plans and presumably the demands from the city planning department are changing.

Architects, of course, work with general contractors and their subcontractors–tradespeople with complementary specialties. Those generals and their trades are critical to the effective and efficient execution of any architect’s plan.

Building a multi-purpose data foundation for hybrid, neurosymbolic AI

Knowledge graphs have been around for over a decade now. The technology is mature and extensible. At this point, it’s hard to find a member of the Fortune 50 that hasn’t built a knowledge graph yet. But what is difficult to find is a large enterprise who’s taken its knowledge graph activity and used that graph as a foundation for its larger AI efforts. Most haven’t taken that mental leap yet.

Montefiore Health, a hospital chain in the New York area, is one exception that may prove the rule. Its Patient-Centered Analytics Learning Machine (PALM) is built on a knowledge graph that Franz, provider of the Allegrograph database, helped to engineer and implement.

The Montefiore knowledge graph brings together many critical, but disparate external and internal sources so that machine learning and advanced analytics methods can be run that benefit from the entire connected whole. The PALM as a result can predict and prevent specific occurrences of sepsis and respiratory failure, for example.

Today, GAI efforts are crying out a sounder, more reliable data foundation, one that brings trustworthiness and certainty to users. The term “neurosymbolic AI”, which the AI centers at the University of South Carolina (headed by Amit Sheth) and Kansas State University (headed by Pascal Hitzler) are advancing, captures the complementary nature of the neural nets (statistical deep learning) and symbolic AI (embodied today in semantic knowledge graphs). All we need is to build awareness of the power of these two technologies together so that there’s more wood behind the AI arrow than mere algorithmic or prompt interface magic.

Big data and artificial intelligence are able to collaborate to help organizations reap a variety of benefits. Since AI requires large amounts of data in order to learn and make decisions, it is able to utilize big data as a source of raw material.

While big data can store data from various sources, AI can further categorize and filter the content. The more data AI has, the more accurate its output will be. Due to this, these two systems work well together and can offer transformative capabilities for companies, leading to significant advancements for a variety of industries.

What Is Big Data?

Big data refers to a large amount of data that is generated at ever-increasing rates, making it very hard to manage. In order for information to be classified as big data, it must contain the three “V’s” of volume, variety, and velocity. This data helps reveal valuable insights that traditional data methods cannot achieve.

The data can be collected through various sources such as comments on social networks, information from apps or electronics, questionnaires, product purchases, electronic check-ins, and more.

There are three types of big data, which are defined as structured, unstructured, and semi-structured. Structured big data is any information that can be stored, accessed and processed in the form of a fixed format. Unstructured data is information with an unknown form that poses multiple challenges for processing. Lastly, semi-structured data is information that can contain both forms of data. An example of semi-structured data is data represented in an XML file.

Defining AI

Artificial intelligence, or AI, involves the creation and implementation of computer systems that are capable of reasoning, logic, and decision making. AI relies on data in order to function properly. This data makes it possible for machines to learn from experiences, adjust, and even perform human-like tasks.

The primary goal of AI is to eliminate any tedious tasks and assist in managing extremely detailed information. Additionally, it has the ability to consume and process massive datasets and develop patterns, aiding it in making decisions for future tasks.

When it comes to classifying artificial intelligence, there are various stages that have been defined. These AI stages focus on reactive machines, limited memory, theory of mind, and self-awareness.

• Reactive machines are those which utilize AI that can only react to certain types of stimuli based on preprogrammed rules. They do not use memory and therefore cannot learn with new data.
• Limited memory AI is the most modern type and can use memory to improve over time by being trained with newer data. This AI is typically part of an artificial neural network.

• Theory of mind doesn’t currently exist, but ongoing research for this stage is underway. It pertains to AI that can emulate a human mind and make decisions similar to a human, such as recognizing and remembering emotions.

• Lastly, self-aware AI, should the stage ever be reached, would feature machines aware of their own existence, giving them true sentience. Machines under this category would be as intellectual and emotional as a human being. Like the theory of mind stage, this AI stage is not currently obtainable.

Benefits of integrating Big Data with AI for your company

While organizations can benefit from big data or AI independently, they can combine both to reap valuable results, such as boosting a business’s performance and efficiency. Additionally, by using AI in conjunction with big data, companies can get advantages like:

• Obtaining valuable insights – Big data and AI allow you to analyze large sets of data very quickly and efficiently. You can save time for other priorities and identify patterns or trends more easily.

• Time-saving automation – Artificial intelligence and big data help by automating time-consuming tasks. They can assist with screening and cleansing data, so that the only thing remaining is quality information.

• Discovery of hidden opportunities – Big data and AI have made it possible to fully analyze extremely large datasets. In the past, you’d miss a lot of opportunities because you wouldn’t be able to dissect so much information.

Any industry can gain value from AI and Big Data

AI and big data can transform industries by allowing them to make data-driven decisions and helping streamline processes. Some fields that can benefit from utilizing these tools include:

• Healthcare
• Finance
• Retail and ecommerce
• Manufacturing
• Transportation and logistics
• Education
• Energy and utilities
• Marketing and advertising
• Agriculture
• Government
• Human resources
• Gaming and entertainment

How these systems complement one another

Some of the ways that big data and AI work with each other include through the following:

• Pattern recognition and prediction capabilities – Certain techniques of AI such as machine learning can process large datasets to identify patterns that may be hard for humans to spot. By continuing to analyze data, it leads to more informed decisions and, ultimately, better results.

• Personalization – Big data allows companies to collect certain information regarding preferences, behaviors, and interactions. AI can then take this data and create personalized experiences, such as product recommendations or content curated to that person.

• Natural language processing (NLP) – Natural language processing is built on big data. With massive amounts of data available, AI models can be trained to understand and generate applications such as virtual assistants and chatbots.

• Fraud detection and security – Combining big data with AI allows you to analyze large datasets to identify any unusual patterns, letting you prevent any fraudulent activity.

• Healthcare and medical applications – With AI’s ability to process big data, it can discover key areas of patient care that may need assistance. Additionally, you can analyze large volumes of medical records, data and clinical studies.

• Supply chain optimization – Big data combined with AI can help in the supply chain field by predicting demand and improving inventory management.

• Natural resource management – Artificial intelligence can examine large amounts of environmental data collected through satellites to improve resource management. This gives organizations the ability to better tackle challenges such as climate change, deforestation, and more.

As you can see, big data working side by side with AI can produce some impressive results. We’re fascinated with these implications and look forward to the changes on the horizon.

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Ally Hosler works with Kent State University’s Ambassador Crawford College of Business & Entrepreneurship.

Modern Data Quality refers to the process of ensuring that data is accurate, reliable, consistent, and up-to-date in today’s data-driven environment. It involves implementing advanced technologies and methodologies to maintain high-quality data that meets the needs of various data-driven applications and analytics.

Importance of Modern Data Quality:

Innovation: Modern data quality drives innovation by providing clean and accurate data that fuels creativity and exploration of new opportunities.

Data-driven Culture: Emphasizing data quality cultivates a data-driven culture where every team member values and utilizes data to drive results.

Risk Mitigation: By identifying and rectifying data issues early on, you can proactively mitigate potential risks and challenges.

Business Growth: High-quality data is a catalyst for business growth, enabling you to identify untapped markets and capitalize on emerging trends.

Customer Trust: When your data is reliable, customers trust your brand more, leading to stronger relationships and increased loyalty.

In summary, modern data quality is indispensable for organizations looking to thrive in a data-centric landscape. It empowers data-driven decision-making, enhances customer experiences, optimizes operations, and lays the foundation for sustainable growth and success.

Innovations in technology are changing the rules when it considers the use of big data and analytics for better growth. Advanced software systems are highly decreasing analytics time, hence offering companies the potential for making quick decisions that will help in boosting revenue, mitigating costs and stimulating growth. This provides a competitive advantage to the organizations that are able to work on a faster pace and target their consumers more efficaciously.

Introduction

The use of Big Data and Analytics has been here for quite a while, however, it was not until recently that Big Data is revolutionizing the business world. A number of organizations are now understanding how they are able to capture the terabytes of data that streams into their businesses and implement analytics for transforming it into actionable and in-depth insights. The advantages of the use of big data and analytics have made it a significant requirement for organizations exploring to harness their business potential. For professionals also, there is an ocean of positive possibilities in Big Data Analytics to consider it for their next career move.

What Is Big Data and Analytics?

‘Big Data’ refers to a highly larger volume of data and data sets that tends to include structured and unstructured data coming in from a number of sources. Furthermore, these datasets are so large that conventional data processing software is not able to capture, handle, or process them. Complicated big data can be applied for addressing business problems that were earlier inaccessible.

Frequently, big data is also characterized by the three Vs. – data entailing bigVariety, coming in bigger Volumes, with greater Velocity. On the other hand, the data can come from publicly accessible sources like social media, the cloud, websites, mobile apps, sensors, and several other devices. Businesses access such data for observing consumer details like buying history, what they looked for or what they watched, their preferences, interests, and so on. Big data analytics uses analytic techniques for examining information, hence, receiving and finding out data like hidden patterns, market trends, correlations, and consumer preferences. Thus, analytics are helping the organizations in making informed business decisions that are leading to productive operations, satisfied consumers, and increased profits.

Use of Big Data and Analytics

Big Businesses all over the globe are integrating Big Data and analytics for achieving big success.

• Amazon, the online e-commerce giant, applies its massive data bank for accessing details such as customer names, payments, addresses, and search histories and integrates them in advertising algorithms and for enhancing customer relations.
• The American Express Company is using big data for analyzing customer patterns.
• Marketing leader Capital One is utilizing big data analysis for making sure of the success of their customer offers.
• Whereas, Netflix is using big data for gaining insight into the viewing patterns of international viewers.
• Brands like Marriott Hotels, McDonald’s, Uber Eats, Starbucks are also constantly integrating big data as part of their core business.

Most Compelling Advantages of the Use of Big Data and Analytics

Businesses, small or big, across industries can leverage from integrating big data effectively. The benefits of big data and analytics involves improved decision-making, bigger innovations, and product price optimization, among others. Let’s take a look at the top benefits closely:

Customer Acquisition and Retention

The digital footprints of customers tends to reveal a lot about their interests, requirements, purchasing behavior, etc. Businesses are using big data for observing consumer patterns and then customize their products and services as per to the specific customer needs. This goes a long way for ensuring customer satisfaction, loyalty, and finally, a considerable increase in sales.

Furthermore, Amazon has utilized this big data advantage by providing the eventual customized shopping experience, wherein recommendation pop up based on previous purchases as well as products that other customers have recently bought, exploring patterns, and other aspects.

Focused and Targeted Promotions

The use of Big Data and Analytics are allowing businesses for delivering personalized products to their targeted audience—no more spending big amounts on promotional campaigns that are not delivering anything. With big data, organizations are able to analyze customer patterns by monitoring their online shopping and point-of-sale transactions. These in-depth insights are then implemented for design-focused and targeted campaigns that will allow brands to live up to customer expectations and structure a robust brand loyalty.

Potential Risks Identification

Businesses are operating in high-risk environments, so they need a strong effective risk management solutions for addressing issues. Big data is playing a crucial role in making effective risk management processes and plans.

Big data analytics and tools quickly mitigate risks by optimizing complicated decisions for unexpected events and capable threats.

Innovate

The insights you attain by implementing big data analytics are the solutions to innovation. The use of Big data and analytics are allowing you to update current products/services while innovating new ones. The huge volume of data gathered helps businesses to identify what suits their customer base. Information on what others think of your products/services will be helping in product development.

The insights can also be incorporated for twisting business strategies, improving marketing techniques, and optimizing customer service, employee productivity.

Complex Supplier Networks

Companies that are using big data provide supplier networks or B2B communities with greater precision and insights. Suppliers are able to integrate big data analytics for evading constraints they tend to typically encounter. Furthermore, Big data is allowing suppliers for using greater levels of contextual intelligence that is vital for success.

Cost optimization

One of the most powerful gain that big data tools like Hadoop and Spark are offering involves considerable cost benefits for processing, storing, and analyzing large volumes of data. The cost mitigation benefit of big data is appropriately presented through an example from the logistics industry.

Most often, the cost of returns is 1.5 times higher than normal shipping costs. Thus they can take appropriate actions to reduce product-return losses.

Improve Efficiency

Big data tools are improving functional efficiency—your interaction with customers and their quantitative feedback will be helping in collecting huge amounts of valuable customer information. Analytics can then take out meaningful patterns hidden within the data for creating tailored products. The tools are able to automate routine processes and tasks, therefore, freeing up valuable time for employees, which they can use it for performing tasks that requires cognitive skills.

Conclusion

In spite of the numerous benefits of big data and analytics, there are yet several untapped opportunities in the data world that are yet to be explored. As businesses are look forward to exploit the power of big data, there is a greater demand for professionals with data analytical skills who can increase the organization operation as well as boost their careers.

AI21 Labs conducted a social experiment this spring where more than 2 million participants engaged in more than 15 million conversations through its website. At the end of each chat, a participant had to guess whether their conversation partner was a human or an AI bot. Nearly one-third guessed wrong.

As ChatGPT and other AI chatbots become more popular, so have the questions about whether such AI tools can be as intelligent as humans, whether the content these tools generate can pass for human creations, and whether AI threatens people's jobs.

Also: 4 things Claude AI can do that ChatGPT can't

AI21 Labs found inspiration for the "Human or Not?" experiment from Alan Turing's evaluation of a machine's ability to exhibit a level of intelligence indistinguishable from that of a human.

This type of experiment would come to be known as a Turing Test based on the 1950 observation by the mathematician, "I believe that in 50 years' time, it will be possible to make computers play the imitation game so well that an average interrogator will have no more than 70% chance of making the right identification after 5 minutes of questioning."

Results of the Human or Not experiment support Turing's prediction: Overall, the experiment's participants guessed correctly 68% of the time. When paired with an AI chatbot, participants guessed correctly only about 60% of the time. When the conversation partner was another human, they guessed correctly 73% of the time.

Though this wasn't a perfect Turing Test, AI21 Labs' Human or Not experiment showed how AI models can mimic human conversation convincingly enough to deceive people. This challenges the assumptions we have about AI limitations and could have implications for AI ethics.

Also: 40% of workers will have to reskill in the next three years due to AI, says IBM study

The experiment found that human participants used different strategies to try to spot the AI bots, like asking personal questions, inquiring about current events, and assessing the level of politeness in the responses.

On the other hand, the authors found that bots confused players with human-like behaviors, like using slang, making typos, being rude in their responses, and showing awareness of the context of the game.

"We created 'Human or Not' with the goal of enabling the general public, researchers, and policymakers to further understand the state of AI in early 2023," according to Amos Meron, creative product lead at AI21 Labs at the time of the experiment. One objective, he added, was "not looking at AI just as a productivity tool, but as future members of our online world, in a time when people are questioning how AI should be implemented in our futures."

Also: The new Turing test: Are you human?

Having used it myself while it was available, I was paired with humans each time and guessed correctly each time. The answer seemed clear to me because my conversation partners would use internet slang (idk, for example), refused to answer questions, or didn't know the answers.

Players tried to confuse other players by imitating AI chatbots, using terms like "as an AI language model," but this was often done imperfectly, and human participants on the other end were able to see through the attempts.

In the midst of the Fourth Industrial Revolution, generative AI emerges as a beacon of transformative potential. While AI’s capabilities in automation, recommendation, and prediction have been widely acknowledged, its generative functions have opened new horizons for businesses globally. This article seeks to shed light on the benefits of generative AI, elucidating how they’re altering the very fabric of business operations.

Understanding generative AI

At its core, generative AI can be visualized as an advanced digital artist. Drawing from existing data, it can produce entirely new data instances that maintain the essence of the original. From synthesizing music that emulates classical composers to creating artwork reminiscent of renowned artists, generative AI’s prowess transcends sectors and industries.

But how does it operate? Relying on sophisticated algorithms and neural networks, such as Generative Adversarial Networks (GANs), this AI model is created by mimicking, learning, and innovating. Its potential applications in business are only now beginning to be fully grasped.

The 4 top Generative AI benefits for business

1. Enhanced product design and innovation

Generative AI is revolutionizing the very core of product design and innovation. Traditional product design often involves laborious brainstorming sessions, multiple iterations, and significant investment in terms of time and resources. With generative AI, businesses can now dramatically accelerate this process.

For instance, in sectors like the automotive or aerospace industry, where design intricacies are paramount, generative AI can be used to come up with multiple design variations that meet specific functional requirements while optimizing for parameters like weight, aerodynamics, and material usage. Companies like Airbus have already started to harness the power of generative design to create lighter and more efficient plane parts.

Beyond physical products, generative AI is also making waves in the digital domain. Think of software interfaces or website designs that adapt in real-time based on user interactions, creating a fluid and ever-evolving user experience.

2. Personalized customer experiences

The age of one-size-fits-all is long past. Today’s consumers seek experiences tailored to their unique needs and preferences. Generative AI offers businesses the unparalleled capability to craft hyper-personalized experiences, going far beyond simple product recommendations.

Consider the realm of E-commerce. Generative AI can be used to tailor every facet of the shopping experience. From generating personalized product descriptions, and dynamically creating virtual store layouts based on individual user preferences, to even designing custom apparel or accessories tailored to a user’s taste and size. The experience becomes less about browsing and more about personal discovery.

Beyond shopping, think of entertainment platforms, where generative AI can create custom music playlists or even generate new music tracks tailored to the mood and preferences of the listener, offering a deeply personal entertainment experience.

3. Efficient content creation

In a digital landscape inundated with content, standing out requires not just quality but also speed and adaptability. Generative AI’s ability to craft content ranges from the written word to visual graphics and even video.

News agencies, for instance, can deploy AI to draft preliminary articles, especially for data-heavy topics like sports scores or financial reports. These drafts can then be refined by human journalists, ensuring timely and accurate news delivery.

In the world of marketing, generative AI can produce myriad ad variations, catering to diverse demographics, and optimizing based on real-time feedback. For film and animation studios, imagine generating basic animation sequences or backgrounds, drastically reducing production timelines.

Moreover, for businesses relying heavily on social media, generative AI can be a game-changer. Crafting personalized ad copies, generating visuals tailored to audience preferences, and even automating responses to user interactions can lead to a more engaged and loyal audience base.

4. Risk management and simulation

Business decisions, whether they concern investments, product launches, or operational changes, come with inherent risks. Generative AI acts as a crystal ball, simulating countless scenarios, and offering a near-real glimpse into potential outcomes.

In finance, investment firms can utilize generative AI to model myriad economic scenarios, foreseeing potential market shifts and adjusting strategies proactively. Similarly, in sectors like agriculture, generative models can simulate various weather patterns, helping farmers predict crop yields and make informed decisions about planting and harvesting.

For urban planners and architects, generative AI can model and simulate the impact of different designs on traffic flow, environmental sustainability, and energy consumption, ensuring the creation of functional and efficient urban spaces.

Conclusion

The digital era has been marked by continuous disruption, and the benefits of generative AI stand as a testament to this transformative journey. These benefits are not merely incremental improvements but paradigm shifts, redefining how businesses conceive, operate, and deliver. As AI technology continues its evolutionary trajectory, the onus is on businesses to adapt, integrate, and champion this generative frontier.

Are you enthralled by the expansive potential of generative AI? The future beckons businesses with a promise of innovation, efficiency, and differentiation. Reach out to us today to embark on this transformative journey, leveraging the full spectrum of generative AI benefits, and ensuring your business remains at the cutting edge of technological advancement.