WhatsTheBigData

Big Data Quotes of the Week: December 1, 2012

Posted on December 1, 2012 by GilPress

“Let us cultivate the mathematical sciences with ardor, without wanting to extend them beyond their domain; and let us not imagine that one can attack history with formulas, nor give sanction to morality through theories of algebra or the integral calculus”–Augustin-Louis Cauchy, 1821, quoted by Matthew Jones, Columbia University

“…the common language of business is not going to be Chinese or Spanish. It’s going to be math”–Michael Rhodin, IBM

“The future is going to be owned by people who are comfortable in the quant world but have deep business knowledge”–Christine Poon, Max M. Fisher College of Business, Ohio State

“[One false promise that some proponents of Big Data hold out is that somehow vast oceans of digital data can be sifted for nuggets of pure enterprise gold.] It is not going to happen magically. The software only finds correlations, not causations. In order to find causal relationships you have to do work. If you take any sufficiently large data sets, you are going to find correlations. You need a human in the loop to work out which are important”–Stephen Sorkin, Splunk

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2017 Gartner Hype Cycle for Emerging Technologies: AI, AR/VR, Digital Platforms

Posted on August 16, 2017 by GilPress

Gartner: The emerging technologies on the Gartner Inc. Hype Cycle for Emerging Technologies, 2017 reveal three distinct megatrends that will enable businesses to survive and thrive in the digital economy over the next five to 10 years.

Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms are the trends that will provide unrivaled intelligence, create profoundly new experiences and offer platforms that allow organizations to connect with new business ecosystems.

6 Highlights of a New Survey on Big Data Analytics

Posted on September 13, 2015 by GilPress

A new survey of 316 executives from large global companies, conducted by Forbes Insights and sponsored by Teradata in partnership with McKinsey, provides a fresh look at the state of big data analytics implementations. Here are the highlights.

The hype gone, big data is alive and doing well

About 90% of organizations report medium to high levels of investment in big data analytics, and about a third call their investments “very significant.” Most important, about two-thirds of respondents report that big data and analytics initiatives have had a significant, measurable impact on revenues.

59% of the executives surveyed consider big data and analytics either a top five issue or the single most important way to achieve a competitive advantage. This attitude is slightly more prevalent in financial services and much more prevalent in Asia-Pacific, where 41% of executives (compared to the survey average of 21%) consider big data and analytics the single most important way for companies to gain a competitive advantage.

The right organizational culture is key to big data success

No matter how many times you say “data-driven,” decisions are still not based on data. Sounds familiar? 51% of executives said that adapting and refining a data-driven strategy is the single biggest cultural barrier and 47% reported putting big data learning into action as an operational challenge. 43% cited fostering a culture that rewards use of data and valuing creativity and experimentation with data as key challenges.

Companies that don’t get the data-driven culture right tend to fall behind their peers. 47% of executives surveyed do not think that their companies’ big data and analytics capabilities are above par or best of breed. And the survey found that the more the respondents know about big data and analytics, the less likely they are to judge the organization as above average or best of breed. For example, among data scientists, only 8% call their organizations best of breed and 10% think they are above average.

Big data is top of mind when the CEO loves data

If you take big data analytics seriously, you get results. 51% of organizations where big data is viewed as the single most important way to gain competitive advantage are led by CEOs who personally focus on big data initiatives. In organizations where big data is viewed as a top-five issue that gets significant time and attention from top leadership, the sponsor is typically one level below top leadership. Finally, companies that have established data and analytics positions at the CxO level are more likely to have above average data analytics capabilities.

Going from the right attitude to the right action is a long big data journey

Even if you have top leadership sponsorship and the right culture, getting data to drive action and strategy is a challenge. 48% of executives surveyed regard making fact-based business decisions based on data as a key strategic challenge, and 43% cite developing a corporate strategy as a significant hurdle. Other obstacles to realizing the benefits of big data analytics are focusing resources to get the most insights from data (43%) and viewing data as a valuable asset (41%).

There’s gold in them thar brontobyte data mountains

The survey found that big data is driving opportunities for innovation in three key areas: creating new business models (54%); discovering new product offers (52%); and monetizing data to external companies (40%). To pursue these opportunities, companies that are gaining the most traction are looking beyond transactional data—exploring a wide variety of many data types.

The most-cited was location data (used to identify an electronic device’s physical location), collected by over half of the respondents, followed by text data (unstructured data like email messages, slides, Word documents, and instant messages). Social media is tracked and its unstructured data collected by 43% of companies surveyed and about a third finds golden nuggets in images, weblogs, videos, sensor data and speech files.

Big data miners still very much wanted

Realizing the business and innovation opportunities hidden in the mountains of data requires the right set of skills and experiences. 46% of the executives surveyed, however, reported that hiring the talent that can recognize innovations in data is a challenge.

Originally published on Forbes.com

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Graduate Programs in Big Data Analytics/Data Science

Posted on April 12, 2012 by GilPress

Updated list here

Bentley University

M.S. in Marketing Analytics

DePaul University

M.S. in Predictive Analytics

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2 New Surveys About the Market for Data Scientists

Posted on May 17, 2015 by GilPress

Two new surveys tell us a lot about both the supply and demand sides of the hot market for data scientists, “the sexiest job of the 21^st Century.”

On the demand side—the challenges of recruiting, training, and integrating data scientists—we have the MIT Sloan Management Review and SAS fifth annual survey of 2,719 business executives, managers and analytics professionals worldwide. On the supply side—the talent available and what salaries it commands—we have the second annual Burtch Works Study, surveying 371 data scientists in the U.S. (see also the video presentation at the end of this post).

The median salary of a junior level data scientist is $91,000, but those managing a team of ten or more data scientists earn base salaries of well over $250,000, according to Burtch Works. Supply is still tight and top managers enjoyed over the last year an eight percent increase in base salary and median bonuses over $56,000. When changing jobs, data scientists see a 16 percent increase in their median base salary.

Who are these data scientists that are so much in demand? The vast majority have at least a master’s degree and probably a Ph.D., and one in three are foreign-born. But with a younger generation of data scientists, freshly minted from more than 100 graduate programs worldwide, the median years of experience dropped from 9 in 2014 to 6 in 2015.

As data science is increasingly adopted by all companies in all industries, the proportion of data scientists employed by startups—the firms that have dominated the application of big data analytics— declined from 29 percent in 2014 to 14 percent in 2015.

It is the mainstreaming of data science and the specific challenges of acquiring and benefiting from this still-scarce talent pool that is the focus of the MIT Sloan Management Review survey. Four in ten (43%) companies report their lack of appropriate analytical skills as a key challenge but only one in five organizations has changed its approach to attracting and retaining analytics talent.

As a result of the scarcity of data scientists, 63 percent of the companies surveyed are providing formal or on-the-job training in-house. “One big plus of developing analytics skills among current employees,” says the report, “is that they already know the business.” These companies are also doing more to train existing managers to become more analytical (49%) and train their new data scientists to better understand their business (34%). Still, half of the survey respondents cited turning analytical insights into business actions as one of their top analytics challenges.

To better manage these challenges, the study recommends giving preference to people with analytical skills when hiring and promoting, developing analytical skills through formal in-house training, and integrating new talent with more traditional data workers.

“Infusing new analytics talent without proper support and guidance can alienate traditional data workers and undermine everyone’s contributions,” says the report. Yet only 27% of companies report that they successfully integrate new analytics talent with more traditional data workers. So even after managing to find (and pay for) the data science talent, there is no guarantee for the desired results, either because of the lack of understanding of the business by the new recruits, resistance from current employees engaged in data preparation and analysis, or failure to translate new insights into meaningful action.

Many companies have responded to these challenges by creating new roles and responsibilities and devising new organizational structures. The report points out that the range of analytics skills, roles and titles within organizations has broadened in recent years. What’s more, new executive roles, such as chief data officers, chief analytics officers and chief medical information officers, have emerged to ensure that analytical insights can be applied to strategic business issues.

Whether the work is centralized or decentralized, data science and analytics should be perceived and managed by companies as a professional function with its own clear career path and well-defined roles. Tom Davenport asked in a recent essay: “When was the last time you saw a job posting for a ‘light quant’ or an ‘analytical translator’? But almost every organization would be more successful with analytics and big data if it employed some of these folks.”

Davenport defines a “light quant” as someone who knows something about analytical and data management methods, and a lot about specific business problems, and can connect the two. An “analytical translator” is someone who is extremely skilled at communicating the results of quantitative analyses.

Data science is a team sport that requires the right blending of people with different skills, expertise, and experiences. Data science itself is an emerging discipline, drawing people with diverse educational backgrounds and work experiences. Typical of the requirements for a graduate degree is what we find in a recent announcement from the University of Wisconsin’s first system-wide online master’s degree in data science: “The Master of Science in Data Science program is intended for students with a bachelor’s degree in math, statistics, analytics, computer science, or marketing; or three to five years of professional experience as a business intelligence analyst, data analyst, financial analyst, information technology analyst, database administrator, computer programmer, statistician, or other related position.”

As with any team sport, there are stars that are paid more than the average player. According to Glassdoor (HT: Illinois Institute of Technology Master of Data Science program), the average salary for data scientists is a bit more than what Burtch Works reported, at over $118,000 per year. (By the way, Glassdoor reports the average salary for statistician is $75,000 and $92,000 for a senior statistician).

It’s possible that the Glassdoor numbers include more of what Burtch Works calls “elite data scientists.” Do we know who is in the elite of top data science players? The closest we get to identify the MVP of data science is the Kaggle ranking of the data scientists participating in its competitions. Currently, Owen Zhang is number one. Zhang says on his profile that “the answer is 42” and his bio section tells us that he is “trying to find the right question to ask.” He lists his skills as “Excessive Effort, Luck, and Other People’s Code.”

Zhang is currently the Chief Product Officer at DataRobot, a startup helping other data scientists build better predictive models in the cloud. He is also yet another example of how experience and skills still matter today more than formal data science education. His educational background? Master of Applied Science in Electrical Engineering from the University of Toronto.

This Burtch Works webinar provides highlights from the 40+ pages of compensation and demographic data in the report, which is available for free download here: http://goo.gl/RQX1xd

[youtube https://www.youtube.com/watch?v=aEkpVr8Q6oI?rel=0]

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Top Skills and Backgrounds of Data Scientists on LinkedIn

Posted on November 8, 2015 by GilPress

A new study of LinkedIn profiles by RJMetrics has found that the number of data scientists has doubled over the last 4 years . This reflects the increasing demand for sophisticated data analysis skills, combining computer programming with statistics, and the growth in the popularity of the term “data science” both in job openings and the words people use to describe their work on LinkedIn. At least 52% of all current 11,400 data scientists on LinkedIn have added that title to their profiles within the past 4 years.

Cumulative Number of Data Scientists Over Time_RJMetrics

In the chart above, the cumulative number of data scientists in any given year corresponds to the number of present-day data scientists who started their first job that year. We can safely assume that those who started their first jobs between 1995 and 2009 were not called then “data scientists,” but the data shows the cumulative growth in the number of professionals who have this title today.

Here are the other highlights of the study:

The high-tech industry (LinkedIn classification: Information Technology and Services industry, Internet and Computer Software industries) employs 44.9% of the professionals identified on LinkedIn as data scientists, followed by education (8.3%, probably employed mostly by universities), Banking and Financial Services (7.2%), and Marketing and Advertising (5.2%).

The top ten companies employing data scientists are Microsoft, Facebook, IBM, GlaxoSmithKline, Booz Allen Hamilton, Nielsen, GE, Apple, LinkedIn, and Teradata. Note that Google is not at the top ten, possibly because the data science Googlers on LinkedIn adhere to the title Google bestows on them: quantitative analyst.

Both Microsoft and Facebook, according to RJMetrics’ analysis, appear to be on a hiring spree, accelerating their data scientist recruiting during the 2014 calendar year by at least 151% and 39%, respectively, when compared to 2013. But given the scarcity of experienced data scientists, it’s a revolving door, with Microsoft also losing the largest number of data scientists over that period.

So how do you become one of these unicorn data scientists, commanding annual salaries of $200,000 plus? The study provides fresh data on the skills and background of data scientists.

RJMetrics analyzed 254,000 skill records of the data scientists on LinkedIn and ranked each skill by the number of people listing it on their profile. In addition to the catch-all categories of “data analysis,” “data mining,” and “analytics,” the top skills are R, Python, machine learning, statistics, SQL, MATLAB, Java, statistical modeling, and C++. Hadoop (20.9%) is at the bottom of the top 20, as a specific skill, behind SAS (22.78%).

Top 20 Skills of A Data Scientist_RJMetrics

An analysis of skills by job levels revealed that chief data scientists appear to be less technical on average: Only 27% and 26% listed Python and R, respectively, compared to 52% and 53% of junior data scientists, along with 38% and 43% of senior practitioners. Those at higher level jobs may not need to emphasize their technical skills or may not need them in positions where management experience and knowledge of a business domain are valued more than technical proficiency.

Over 79% of data scientists listing their education have earned a graduate degree, with 38% of all data scientists who had an education record earning a PhD, and close to 42% listing a Master’s degree as the highest degree attained.

Computer Science is the dominant field of study among data scientists, followed by business administration/management, statistics, mathematics, and physics. Only 4.6% of data scientists list “machine learning/data science” as their graduate degree, a number that will probably increase in coming years due to the proliferation of new Master in Data Science programs, supplanting the older Master in Analytics programs.

Top 20 Backgrounds of Data Scientists with a Graduate Degree_RJMetrics

Note that RJMetrics included in their sample only data scientists associated with specific companies, assuming that those listing “data scientist” in their profile without an association with an actual company may only have aspirations about a career in data science, but not actual experience. They analyzed 60,200 records of professional experiences, 27,700 records of education, and 254,600 records of skills, and information about 6,200 unique companies that employed self-identified data scientists as of June 1, 2015.

For other recent studies of the skills and salaries of data scientists see here and here.

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Data is Eating the World: A New Economy

Posted on May 11, 2017 by GilPress

The Economist:

Data are to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and—crucially—new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data…

The problem [with personal data] is the opposite to that with corporate data: people give personal data away too readily in return for “free” services. The terms of trade have become the norm almost by accident, says Glen Weyl, an economist at Microsoft Research. After the dotcom bubble burst in the early 2000s, firms badly needed a way to make money. Gathering data for targeted advertising was the quickest fix. Only recently have they realised that data could be turned into any number of AI services.

Whether this makes the trade of data for free services an unfair exchange largely depends on the source of the value of the these services: the data or the algorithms that crunch them? Data, argues Hal Varian, Google’s chief economist, exhibit “decreasing returns to scale”, meaning that each additional piece of data is somewhat less valuable and at some point collecting more does not add anything. What matters more, he says, is the quality of the algorithms that crunch the data and the talent a firm has hired to develop them. Google’s success “is about recipes, not ingredients.”

That may have been true in the early days of online search but seems wrong in the brave new world of AI. Algorithms are increasingly self-teaching—the more and the fresher data they are fed, the better. And marginal returns from data may actually go up as applications multiply, says Mr Weyl.

Big Data and Data Science Events September-December 2012

Posted on September 16, 2012 by GilPress

Big Data and Data Science Events

September – December 2012

Last updated September 16, 2012

TDWI World Conference Sep 16–21, Boston

Predictive Analytics World–Government September 17-18, Washington DC

*** To get a 15% off of the 2 Day and Combo passes, use this code: WTBDBP12 ***

An Introduction to Machine Learning for Hackers: O’Reilly Strata Webcast September 18, 10am PT

Government Big Data Conference, September 18-19, Arlington, VA

Big Data World Europe September 19-20, London

Sixth IEEE International Conference on Semantic Computing September 19-21, Palermo, Italy

GigaOM Mobilize September 20-21, San Francisco

Sports Analytics Innovation Summit, September 20-21, San Francisco

Data 2.0 Conference & Expo September 21, San Francisco

Data 2.0 Labs: 2012 City-Wide Data Festival September 22-27, San Francisco

Data Analytics 2012 September 23-28, Barcelona, Spain

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases September 24-28, Bristol, UK

The Business Value of Big Data, September 27, Temple University, Philadelphia

London DataDive September 28, London

Predictive Analytics World September 30-October 4, Boston

*** To get a 15% off of the 2 Day and Combo passes, use this code: WTBDBP12 ***

Marketing Optimization Summit September 30-October 4, Boston Continue reading →

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21 Generative AI Examples and its Applications Across Industries

Posted on October 14, 2024 by GilPress

Generative AI, also known as Gen AI, is a subfield of AI that focuses on generating new content. In recent years, it has experienced a rapid surge in popularity and applications across various industries. From code generation to Image-to-image conversion, generative AI has truly transformed the way we work.

In this guide, we will list the top 21 generative AI applications and use cases across industries.

21 Generative AI Examples and Use Cases Across Industries

1. Language Translation

Generative AI is increasingly utilized by businesses and individuals for real-time and accurate translations across multiple languages. These AI models, trained on massive datasets of text, can understand the nuances of different languages and generate human-quality translations that are natural-sounding and contextually relevant. AI-driven translations are becoming increasingly common among people. Most businesses and companies are utilizing generative AI to translate their documents, websites, customer communications, and more. People also utilize this technology for casual conversations, traveling, or learning foreign languages.

2. Chatbot performance improvement

While Chatbots are one of the most popular AI applications, generative AI technology helps enhance and improve chatbots’ capabilities, making them more useful for users. Here’s how generative AI is currently being utilized for enhancing chatbot performance:

Natural language understanding: Generative AI models have significantly improved AI chatbots’ natural language understanding (NLU). Training AI models on an extensive amount of text information has helped the technology learn language patterns, content, and nuances. This results in chatbots having a better understanding of users’ input and generating a personalized response.
Managing open-ended prompts: Most traditional rule-based chatbots find it difficult to handle unfamiliar topics or open-ended queries raised by users. However, implementing generative AI helps chatbots better handle user inputs, even on topics that the platform is unfamiliar with.
User profiling: Another benefit of generative AI implementation is facilitating chatbots’ creation of user profiles. By utilizing generative AI, chatbots can analyze past conversations to better understand users’ likes, preferences, and tone and establish a user profile based on them. This helps chatbots generate user-based responses and offer a personalized chatting experience.

3. Code generation

Programmers and software developers are utilizing generative AI to produce code. Generative AI offers an automated approach to code creation that helps advance coding tasks efficiently, eliminating manual coding effort requirements. This breakthrough helps simplify the code generation process not just for coding experts but also for non-technical individuals. Additionally, Generative AI is being utilized across multiple platforms to automatically update and maintain coding.

4. Content creation

One of the most popular use cases of generative AI is content creation. People across various industries utilize generative AI applications to generate unique and eye-catching content as they are extremely helpful in creating various types of content such as blogs, marketing copies, articles, social media captions, and more. Generative AI applications such as ChatGPT, can help speed up the content creation process by generating excellent content ideas, content outlines, quotes, etc.

5. Image generation

Generative AI tools have the ability to generate stunning AI images effortlessly using text descriptions. This has completely simplified and sped up the process of image generation allowing users to create images comfortably in a cost-effective manner. AI image-generating tools can create images in a variety of different styles, themes, backgrounds, etc. Most users also access image generators to edit or enhance their existing images by changing their size, removing unwanted objects, adding color, style, and more. These image generators are utilized across various industries for multiple purposes such as marketing, content creation, graphic design, photography, and much more.

6. Automate testing

Generative AI-driven applications can enhance automated testing processes and save software developers time as it’s a time-consuming task. Generative AI is utilized to develop diverse and realistic test data. It can create a wide range of test cases such as edge cases and anomalies which can help detect any potential defects in applications. Developers can create new test cases based on their specifications, requirements, or existing test data, enhancing code coverage.

7. Code completion

Generative AI has enhanced coding efficiency by offering smart coding suggestions and auto-completion capabilities. IDEs (integrated development environments) can harness generative AI models to predict future code lines that a developer might write next, based on the current context, programming language, and coding style of the developer. This predictive capability helps speed up the code completion process by suggesting useful code snippets to the developer. It also helps minimize errors, especially for repetitive or boilerplate code. Apart from this, generative AI can also offer real-time insights into best practices, suggest alternative approaches, and fix any potential bugs or other issues.

8. Collaborative coding

Another impactful use case of generative AI is collaborating coding which plays a crucial role in enhancing the efficiency of software development processes. By incorporating Generative AI into collaborative coding it can generate useful code snippets suggestions based on the context and requirements of the project which helps developers in generating code by speeding up the development time. It can even analyze and provide suggestions on your existing code to enhance its performance.

9. Debugging code

Generative AI also has the capability to provide assistance with debugging. Generative AI applications can analyze code to identify any potential issues such as performance bottlenecks, syntax errors, and logical inconsistencies. This way it can enhance the efficiency and effectiveness of the software by resolving any defects. It can also predict the potential of any error based on historical data and code patterns. Thus, generative AI helps speed up the entire debugging process by automating the process generating valuable insights, and fixing any potential errors.

10. Image-to-image conversion

Image-to-image conversion is another popular use case of generative AI applications. It involves transforming one image into another by changing various aspects of the images such as style, color, shape, and more to generate your desired outcome. It also contains feature extraction using which you can eliminate various features from your existing images such as edges, texture, etc, and generate a brand new image based on the transformed features. Various artists and designers use image-to-image conversion to generate unique artistic images and explore their imagination by trying out different styles, colors, textures, and more. Apart from this, photographers also utilize this technology to enhance or modify their existing photographs by removing an object, changing the background, enhancing image quality, and more.

11. Text-to-Speech Generator

Generative AI’s other popular use case is text-to-speech generation through which businesses or creators can transform their texts into audio. By combining user data with generative AI, it can produce high-realistic and expressive speeches that are widely utilized for commercial purposes including marketing, podcasting, advertising, content creation, education, and more. Audio files produced through this technique are widely utilized as educational material for blind or visually impaired students.

12. Summarization

Generative AI can quickly process vast quantities of text and generate a summary by accurately capturing all the important details and main points of the document. Writers, students, and researchers can utilize these generative AI tools to summarize large text content to identify essential details, key trends, and insights. It can even produce summaries tailored to specific needs such as providing an overview or focusing on a particular detail. These tools can help students summarize lengthy lectures and text chapters and help them speed up the learning process. Generative AI can even summarize documents or large texts into different languages, making them accessible to a wider audience.

13. Video generation

Another widely implemented generative AI use case is Video generation. Generative AI applications have simplified the video creation process, allowing individuals to generate high-resolution video content without any actors, cameras, or microphones. By utilizing generative AI models, applications can automate the video creation process and create stunning AI videos from scratch using text descriptions. You need to simply add some texts describing the kind of video you wish to generate and generative AI will instantly process your request and transform your texts into captivating videos efficiently. In addition, generative AI can also perform various tedious tasks such as adding special effects, composition of the video, animations, editing video snippets, and more.

14. Writer

One of the most popular use cases of Generative AI is producing content. AI chatbots such as ChatGPT are utilized for creating multiple types of text content such as blog posts, email campaigns, stories, poems, articles, and more. Generative AI tools also support writers in brainstorming ideas based on writers’ existing work or prompts. It assists in providing feedback on writers’ work helping in identifying areas that require changes or any improvement. Writers also tend to utilize such tools to help with grammar, style, and tone ensuring the generated content is well-polished without any mistakes.

15. Sales and Marketing

Generative AI plays a crucial role in assisting marketing campaigns by enhancing hyper-personalized communication across various channels such as emails, SMS, and social media to both potential and existing customers. Generative AI offers valuable analytics and insights into customer behavior, helping teams improve performance. Most marketing teams are utilizing this technology to gain essential data about their consumers, enabling them to better understand their audience and create content that truly connects with the audience and fulfills their requirements causing a rise in sales. In addition, Generative AI also helps with audience segmentation and identifying important leads, to improve the effectiveness of their marketing strategies.

16. Project management and operations

Generative AI tools also provide exceptional support to project managers by automating various tasks. Some of the benefits of incorporating generative AI into operations include automatic task and subtask generation, predicting timelines and requirements based on previous project data, taking essential roles, and predicting any potential risk. Generative AI can help project managers generate instant summaries of important business documents quickly. This helps save time and enables project managers to focus on more essential and complex duties rather than repetitive management tasks.

17. Product development

Generative AI is being increasingly utilized by product designers to generate unique design concepts. This technology assists designers in brainstorming ideas, suggesting improvements, and helping them explore new possibilities, making the product development process smoother and more efficient. It also helps designers in structural optimizations, which ensure the products are strong and durable with minimal material usage, leading to cost reduction.

18. Customer service

Generative AI is also considered highly useful in customer service. By applying advanced AI technology, it can handle a variety of customer service tasks, such as generating human-like responses, responding to users’ queries, transcribing customer calls or messages, suggesting relevant solutions, and more. The best part about implementing generative AI in customer service is that it offers 24/7 support by developing appropriate responses and enhancing customer service operations’ efficiently.

19. Fraud detection and risk management

Generative AI can generate vast amounts of synthetic data that mimic real-world patterns and play a significant role in improving the training of fraud detection models. It can scan large amounts of data and detect anomalies or deviations, which can be beneficial in identifying any potentially fraudulent or suspicious activity as it continuously monitors data streams. By utilizing synthetic data, it ensures the protection of data. This way, organizations and businesses can protect sensitive and private customer information while still developing effective fraud detection systems.

20. Medical Image Synthesis

Generative AI is also creating a significant impact in the healthcare industry as it helps in medical imaging, especially for generating synthetic MRI images. Producing high-quality images through synthetic MRI image generation can help in diagnosis and treatment planning and make the process more efficient. Apart from this, generative AI also plays a crucial role in synthesizing CT scan images as these AI-generative images can be beneficial for medical professionals to identify any anomalies and abnormalities with more accuracy. Similarly, in X-ray diagnostics, generative AI is utilized to enhance the overall image quality to offer a clear image of the X-ray, so medical professionals can make more accurate assessments.

Bottom Line

In conclusion, generative AI truly transforms the workforce across various industries through its innovation and efficiency. From content creation to code completion, generative AI is driving innovation at an excellent speed. Above, we have mentioned 21 generative AI applications and use cases through which we have explored the capabilities of Gen AI and how it’s being utilized by professionals across various industries to enhance their workforce efficiently.

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History of Artificial Intelligence (AI) 1921- 2024

Posted on October 14, 2024 by GilPress

Artificial intelligence has integrated into our daily lives, from using virtual assistants like Siri to accessing self-driving cars. It is everywhere. But did you know the concept of AI is not new? Instead, the journey of AI goes way back to ancient times, a period you would not have imagined. The term “artificial intelligence” was introduced in 1956 during a workshop.

In this article, we will closely examine the history of artificial intelligence (AI), tracing its development from its early foundations in the 1900s to the remarkable advancements it has achieved in recent years.

What is Artificial Intelligence?

Artificial intelligence (AI) is a computer science technology that creates intelligent agents or systems that can replicate human intelligence, decision-making, and problem-solving abilities. Applications or devices equipped with AI can identify objects, understand and respond to human language, and even learn from new information by improving their performance and experience over time. Today, AI is utilized in various areas such as healthcare, finance, customer service, manufacturing, transport, and more.

The History of Artificial Intelligence

Artificial intelligence has a rich history that goes back thousands of years to ancient myths and philosophical musings. Although “artificial intelligence” wasn’t coined until 1956, inventors made mechanical devices known as “automatons,” which moved independently without human involvement. The word “automatons” means “acting of one’s own will.” Some of the earliest records of an automaton include the “mechanical monk” created in the 16th century, the still-functional “Silver Swan” constructed in 1773, and more.

Groundwork for AI:

The groundwork for AI was laid through a series of significant developments and discoveries over the years. In the early 1900s, there was a massive buzz about “Artificial humans.”

The buzz was so strong that scientists began to question whether it was possible to create an artificial brain. Various creators made simplified versions of robots that could perform simple tasks.

Some of the notable dates during this time are as follows:

1921: Karel ?apek, a Czech playwright, released a science fiction play, “R.U.R.” (Rossum’s Universal Robots), in 1921, which introduced the word “robot” into the English language. He used the term “robots” for artificial people created to serve humans.

1929: Makoto Nishimura, a Japanese professor, created the first-ever Japanese robot, known as “Gakutensoku.”

1949: Edmund Berkeley, a computer scientist, published a book called “Giant Brains, or Machines That Think. ” In it, Berkeley compared early computers to human brains, exploring the potential of machines to perform tasks traditionally associated with human intelligence.

Birth of AI: 1950-1956

The period from 1950 to 1956 is considered a prominent period in the history of AI. During this period, the term “artificial intelligence” was introduced, along with several groundbreaking developments in the field.

1950: In 1950, Alan Turning, who is often considered the inventor of AI, published a landmark paper titled “Computing Machinery and Intelligence,” which proposed a test called the “Turing Test.” This test was introduced by Turning to determine whether a machine is capable of exhibiting intelligent behavior indistinguishable from a human.

1952: Arthur Samuel, a computer scientist, created a checkers program, the first-ever program to learn the game independently. The program could also improve its performance over time by playing it against itself and analyzing its outcomes.

1956: The Dartmouth workshop took place in 1956 and considered the founding event of artificial intelligence as a field. John McCarthy and Marvin Minsky organized this workshop with the support of two senior scientists from IBM, Nathan Rochester and Claude Shannon. In this workshop, John McCarthy introduced the term “Artificial Intelligence” for the first time. This workshop was when AI first gained its name and mission, which is considered AI’s birth.

AI maturation: 1957-1979

The late 1950s to 1960s was a period of creation in AI. From programming languages that are relevant to this day to books and films that explore the idea and objective of robots, AI became a widespread idea instantly. The 1970s also played a significant role in the development of AI, with The American Association of Artificial Intelligence (AAAI) being founded in 1979. However, there was a lot of struggle for AI research since the government reduced its interest in funding AI research.

Some of the notable dates during this period are as follows:

1958: John McCarthy created LISP, which stands for List Processing, in 1958; this was the first high-level programming language designed specifically for artificial intelligence research.

1959: Arthur Samuel coined the term “machine learning” while giving a speech on teaching machines to play chess better than humans who programmed them.

1961: James Slagle developed SAINT (Symbolic Automatic INTegrator), a heuristic program that solved symbolic integration problems in freshman calculus.

1965: Joshua Lederberg and Edward Feigenbaum created the first “expert system” in 1965. The Expert system was a form of AI specially programmed to replicate or copy the thinking and decision-making abilities of human experts.

1966: Joseph Weizenbaum built the first “chatterbot,” which was later shortened to “chatbot. ” This bot utilized natural language processing (NLP) to communicate with humans.

1968: Alexey Ivakhnenko, a soviet mathematician, released “Group Method of Data Handling” in the journal “Avtomatika,” which carried an entirely new approach to artificial intelligence,e which is known as “Deep Learning” in today’s date.

1973: The British government declined support and funding for AI research in 1973 after applied mathematician James Lighthill provided a special report on the strides, which were apparently not as impressive as the scientists had promised.

1979: In 1961, James L. Adams created the Stanford cart, a remotely controlled, TV-equipped mobile robot that became one of the first-ever examples of an autonomous vehicle. In 1979, the Stanford cart successfully navigated a room full of chairs without any human interference.

1979: The American Association of Artificial Intelligence (AAAI) was founded in 1979 and is today known as the Association for the Advancement of Artificial Intelligence (AAAI). This organization plays a significant role in promoting research, education, and public understanding of artificial intelligence.

AI boom: 1980-1987

Most of the 1980s showcased a period of excellent growth and interest in AI, labeled as the “AI bloom.” The massive increase in AI came from breakthroughs in AI research and additional funding from the government to support researchers. During this period, deep learning techniques and the use of expert systems also became broadly popular.

1980: The first American Association of Artificial Intelligence (AAAI) conference was held at Stanford University in 1980. It was also named the first Nation Conference on Artificial Intelligence (AAAI-80). This conference is considered one of the significant milestones in developing AI as a field, as it provided a unique platform for researchers and experts to showcase their ideas and works.

1980: XCON (Expert Configurer) was one of the first expert systems to enter the commercial market. It was developed by Carnegie Mellon University to assist in the configuration of computer systems. XCON helped streamline the ordering process and reduced errors by automatically choosing components based on customer specifications.

1981: The Japanese government launched the Fifth Generation Computer Systems Project to develop computers with capabilities such as human-level reasoning, problem-solving, and natural language understanding. The government funded the project around $850 million (which is more than $2 billion dollars today).

1984: The American Association for Artificial Intelligence (AAAI) warned about the arrival of “AI Winter.” This term refers to a decrease in funding and interest in AI research, which made the entire process more difficult.

1985: AARON, an autonomous drawing program capable of creating original drawings and paintings without human involvement, was demonstrated in 1985 at the American Association for Artificial Intelligence (AAAI) conference. This demonstration helped showcase AI’s true potential in generating unique artworks and paintings and its growing capabilities in creative domains.

1986: Ernst Dickmann, along with his team at Bundeswehr University of Munich, developed and demonstrated the first driverless car or robot car in 1986, which was known as “Stanley.” This robot car could drive autonomously up to 55 mph on roads without other obstacles or human drivers.

1987: Alactrious Inc. launched Alacrity, the first commercial strategy managerial advisory system. Alacrity was a complex expert system with more than 3,000 rules that could offer strategic advice to managers. After the commercial launch of Alacrity, a significant step was taken in the application of AI to business decision-making.

AI winter: 1987-1993

As predicted by the American Association for Artificial Intelligence (AAAI), AI Winter did occur in the late 1980s and early 1990s. The first AI Winter took place in the 1970s when AI became a subject of critique and witnessed several financial setbacks. The term AI Winter refers to a period of low consumer, public, and private interest in artificial intelligence, resulting in reduced research funding and interest. By then, government and private investors had lost interest in AI and halted financing due to the high costs and seemingly low returns. The primary reason behind the occurrence of this AI Winter was because of inevitable setbacks in the expert systems and machine market.

Some of the key factors which contributed to the AI Winter are:

The End of the Fifth Generation Project: The Japanese project launched by the government in the early 1980s to develop advanced computers capable of performing translation, conversing in human language, and expressing reasoning on a human level came to an end. Despite the ambitious goal, the project failed to meet its objectives, which led to a loss of confidence in AI research.
Cutbacks in Strategic Computing Initiatives: The Government reduced its funding for AI research as it shifted its priorities to other areas of spending.
Slowdown in the Deployment of Expert Systems: Although expert systems started well and saw early success, their momentum lasted only a short time. The limitations became quite clear: they were not utilized in commercial applications as widely as anticipated.

Some of the notable dates during AI Winter are as follows:

1987: The market for specialized LISP-based hardware crumbled in 1987 due to the availability of cheaper and more accessible computers that could run LISP software, including those offered by Apple and IBM.

1988: Another notable event during this timeline was the invention of Jabberwacky, a chatbot designed by Rollo Carpenter to provide interesting and entertaining conversations to humans.

AI agents: 1993-2011

Regardless of the shortage in funding during the AI winter, the early 90s introduced some impressive strides forward in AI research, including IBM’s Deep Blue, which created a record by beating the reigning world champion chess player. This era also introduced an autonomous vacuum robot, Roomba, into their everyday life.

Some of the notable dates during this era are as follows:

1997: IBM’s Deep Blue, a chess-playing expert system, created a record when it defeated the world chess champion, Gary Kasparov, in a six-game match. This victory was considered a significant milestone in the history of AI, demonstrating the excellent progress made in computer systems with its complex problem-solving and strategic thinking.

1997: Windows released its speech recognition software in June 1997, developed by Dragon Systems.

2000: Kismet is an expressive robot head developed by Professor Cynthia Breazeal. It was designed to stimulate human emotions through facial expressions, including eye movements, eyebrow changes, mouth movements, and ear positioning.

2002: iRobot introduced Roomba in September 2002, an autonomous vacuum designed for cleaning floors. The success of Roomba has helped popularize the concept of household vacuum robots, which is popular among people today.

2003: NASA successfully landed two rovers (Spirit and Opportunity) on Mars. The rovers could navigate the Martian surface autonomously, collecting information and exploring the surface of the planet’s geology without any human intervention.

2006: In the mid-2000s, several social media platforms, such as Twitter and Facebook, and streaming services like Netflix had begun utilizing artificial intelligence in their operations and advertising. Platforms were utilizing AI algorithms to personalize user content recommendations, optimize advertising targeting, and improve the overall user experience. These platforms paved the way for the widespread adoption of AI in numerous sectors.

2010: Microsoft released the Kinect for the Xbox 360, the first gaming hardware specifically designed to track body movement using motion-sensing technology and translate them into game commands.

2011: IBM’s Watson, a natural language processing (NLP) system programmed to answer questions, won Jeopardy against two former champions in a televised match. Watson’s ability to understand and process natural language and an extensive knowledge base allowed the system to outsmart and defeat human opponents.

2011: Apple released Siri, the first popular virtual assistant that could be activated using voice commands. This helped spread the concept of voice-activated assistants.

Artificial General Intelligence: 2012-present

That brings us to the most advanced and developed era of artificial intelligence up to the present day. This era witnessed the introduction of virtual assistants, search engines, chatbots, and more. Chatbots such as ChatGPt were being utilized on a large scale by people worldwide to generate human-like texts such as emails, stories, code, musical pieces, and much more. OpenAI also introduced DALL-E, an AI model that can develop AI images using text prompts.

2012: Jeff Dean and Andrew Ng, two researchers from Google, trained neural networks to demonstrate their capabilities. They trained neural networks to recognize cats from unlabeled images without background information.

2015: In 2015, some of the most prominent figures worldwide, including Elon Musk, Stephen Hawking, and Steve Wozniak (along with 3000 others), signed an open letter urging a ban on the development and usage of autonomous weapons systems in the world’s government. The letter expressed concerns regarding the ethical implications of such weapons and the potential of them falling into the wrong hands and causing danger. This letter helped raise awareness regarding the issue.

2016: A humanoid robot named Sophia was created by Hanson Robotics in 2016 with a remarkable human-like appearance and the ability to replicate human emotions. Sophia became the first “robot citizen” and was granted citizenship in Saudi Arabia. Its ability to engage in human-like conversations and respond to queries made her a notable figure in robotics and AI.

2017: Facebook researchers programmed two AI chatbots that were specifically designed to learn how to negotiate with each other. However, as the chatbots interacted, they developed their language, departing from the English language initially programmed for use. This raised concerns regarding the potential of AI systems as they could build their language entirely autonomously, which could be problematic for humans to understand or control.

2018: The Chinese tech group Alibaba’s language-processing AI system surpassed human performance on the Stanford Reading Comprehension Dataset (SQuAD), creating a benchmark for machine reading comprehension.

2019: Google’s AlphaStar AI system reached Grandmaster level in the complex real-time strategy video game StarCraft 2. Unlike other games, StarCraft 2 is significant because it requires strategic thinking, planning, and adaptability, skills that are often considered challenging for AI systems.

2020: OpenAI introduced GPT-3, a language model capable of generating human-quality text, including articles, code, scripts, musical pieces, emails, letters, etc. Although it’s not the first of its kind, GPT-3 was the first language model capable of generating content similar to those created by humans.

2021: OpenAI launched DALL-E, a unique AI model that can generate high-quality AI images from text descriptions. DALL-E’s ability to understand and process visual content through texts represented a significant step forward in AI’s understanding of the visual world.

2023: OpenAI created a multimodal large language model GPT-4 capable of processing and generating text and images. This multimodal capability allows GPT-4 to perform a broader range of tasks, such as answering questions about pictures or creating original images based on textual descriptions.

Who Invented AI?

There isn’t any one single inventor of AI; instead, multiple individuals play a crucial role in laying the foundation of AI. Alan Turing proposed the famous “Turing test,” a method that helped determine whether a machine can think like a human. However, John McCarthy is often coined as the person who invented the term AI “artificial intelligence” in 1956.

First Artificial Intelligence Robot

Shakey is the first ever AI-based mobile robot created in 1970 by the Stanford Research Institute (SRI International). It was one of the first robots to demonstrate the ability to plan and execute tasks in a real-world environment. Shakey could perceive its surroundings by utilizing sensors and performing various tasks such as opening doors, pushing blocks, and navigating a room.

When Did AI Become Popular

AI has gradually become popular over several decades, with the development of various expert systems, increasing capabilities, and practical applications playing a significant role in this rise.

1950s to 1960s: During the initial surge, various AI programs such as ELIZA and the Dartmouth Summer Research Project on Artificial Intelligence played a crucial role in the development of AI.

The 1990s: AI gained popularity during the 90s with advances in neural networks and machine learning. Some notable milestones during this timeline are IBM’s Deep Blue, which created a record by beating the reigning world champion chess player and releasing speech recognition software.

2000s: AI started gaining massive recognition in the 2000s as computational power, data availability, and machine learning improved. Various social media platforms and streaming services like Netflix also began utilizing artificial intelligence to personalize content recommendations, helping pave the way for the widespread adoption of AI across various sectors.

The 2010s: Several breakthroughs for artificial intelligence occurred in the 2010s, especially with the development of neural networks, which led to advancement in AI, enabling numerous tasks such as natural language processing, self-driving cars, and image recognition.

2020s: Various workforces and applications are today integrating AI into their lives. From virtual assistants and chatbots to autonomous vehicles, the demand for AI is increasing daily.

What does the future hold?

Now that we have learned about the history of artificial intelligence (AI), the most obvious next question in everyone’s mind is: what comes next for AI?

Well, we can’t precisely predict the future. Still, many experts and professionals have stated that AI systems are also expected to become more sophisticated and capable of understanding complex concepts and learning from diverse data sources. The adoption of AI is also likely to occur among businesses of all sizes, bringing excellent changes in the workforce as automation eliminates and generates jobs in equal measure, more robotics, autonomous vehicles, etc, leading to higher efficiency, productivity, and cost-saving.

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