Whats the Big Data

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]

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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Alternatives to BikiniOff

Posted on January 23, 2024 by Kevin Pocock

BikiniOff is an excellent clothes-removing platform through which you can effortlessly convert your bikini images into nude images by removing clothes using Artificial intelligence. BikiniOff can transform your images into various categories such as Lingerie, Nude, Business style, Sports, and much more.

This tool utilizes advanced AI technology and neural networks to remove clothing from your images and generate desired results. In this article, we will mention the top alternatives to BikiniOff that you can access to transform your images into different categories using simple steps. So, let’s get started.

What is Bikini Off Bot?

BikiniOff Bot is a software that utilizes advanced Artificial Intelligence technology to remove and transform clothes from your images into different categories. BikiniOff allows users to transform their images into lingerie, sports, business, nude, and more styles. This bot uses a neural network to transform your clothed images into nude images and turn any photo to nude version of the person.

This platform works best with images of women in bikinis or limited clothes. However, it’s important to use this tool responsibly and avoid creating images of individuals in BikiniOff without their consent. You must have an individual’s proper permission and authorization when using this tool to avoid violating privacy and consent.

Alternatives to BikinOff

BikinOff bot is an ai cloth remover technology that allows users to transform their bikini images into nude images by removing their clothes using AI technology. Regardless of its capabilities, this technology often raises ethical and privacy questions therefore, users are always advised to avoid using images of individuals without their consent. Here are some of the best alternatives to BikiniOff:

1. Nudify.online

Nudify.online is an excellent alternative to BikinOff as this platform can remove clothes from your uploaded images using deep learning algorithms and advanced AI technology. This tool offers an intuitive interface through which users can easily transform their clothed images into nude images in just a few clicks. You can log in to this platform using your Google account, Discord account, or email.

To use this platform, users must start by uploading their images on Nudify.online. Next, you need to paint over the areas using a virtual brush to specify the areas you wish to undress. Once done, click on the “Generate” option and the AI tool will instantly transform your image into a nude image.

Features:

Users can transform their clothed images into different types such as Lingerie, Barbie, Bikini, Anime, Nude, and more.
This tool offers a variety of image resolution options, such as Standard, High, and ultra-high quality.
It is a versatile platform that offers a variety of editing options.

Pricing:

Basic Plan	Standard Plan	Pro Plan
$5.49/month	$16.99/month	$37.99/month

2. Promptchan AI

Promptchan AI is an advanced AI platform that allows users to remove clothes from their existing images and create a Nude image. It utilizes AI technology and machine learning algorithms to ensure more accurate clothes removal from an image. With this tool, users can effortlessly generate content in various styles such as Hyperrealistic, Cinematic, Anime, Real-looking girl, and more using text instructions.

Promptchan AI is a versatile tool. It allows users to create uncensored images and explicit videos without any restrictions. Overall, Promptchan AI is an excellent alternative to BikiniOff, and its user-friendly interface makes it easy for beginners and professionals to access the platform.

Features:

Promptchan AI contains many style options for creating images and videos, such as Cinematic, Anime, Real, and Art.
It contains an “Explore” section through which users can browse various uncensored AI images and gain inspiration.
Machine learning algorithms in this platform ensure clothes removal with higher accuracy and realistic results.

Pricing:

The paid plans of Promptchan AI start at $5.99/month.

3. SoulGen AI

SoulGen AI is an impressive AI platform that allows users to transform their clothed images into nude images and generate captivating visuals of real and anime girls. This tool also contains excellent edit options through which users can remove objects and perform several changes to their original images.

To remove clothes from your existing images on Soulgen AI, you must start by navigating to the “Edit Image” option. After this, you need to upload your image online and choose the areas you wish to undress in your uploaded image. Users can also specify further details by providing a text prompt. Once done, click the “Generate” option, and your image will be ready.

Features:

Good customization options are available.
Users can generate high-quality AI Girl and Real Girl on SoulGen AI.
This tool contains advanced editing options through which users easily add or remove elements from their images.

Pricing:

Soulgen AI’s one-month plan is $9.99, while the yearly plan is $69.99.

4. DreamGF AI

DreamGF AI is an AI-driven platform allowing users to create their desired AI virtual girlfriend and engage in fun and exciting conversations. This tool utilizes advanced AI algorithms to create virtual partners based on users’ preferences. DreamGF AI allows users to customize the overall appearance of their virtual partner along with personality, hobbies, and various other traits to provide a personalized experience.

Users can interact with their virtual partner on this platform and receive AI images of their virtual companion through requests and receive voice messages through its chat feature.

Features:

This platform contains excellent customization options that allow users to modify and adjust a virtual companion’s personality, appearance, hobbies, and more.
An intuitive interface through which users can easily generate unique AI images and virtual girlfriend.

Pricing:

DreamGF AI premium plans begin at $9.99/month.

5. Nubee.AI

Nubee.AI is a unique Telegram Bikinioff bot that can easily transform your clothed image into a nude image using AI technology. To use this platform, users must install a Telegram app on their device and then continue by joining the Nubee.AI telegram channel. Once you have joined you need to confirm your age and accept terms and service.

Next, you can upload your image on the chat interface and easily transform your image into different categories such as Nude, Bikini, Lingerie, Sport, and more. Overall, Nubee.AI is a great alternative to BikiniOff, as it contains a simple interface through which you can easily convert your existing images into different categories by following simple steps.

Features:

This tool lets Users convert their existing images into categories such as Bikini, Nude, Sport, and much more.
A simple interface doesn’t require any technical expertise.
Professional grade image enhancement.
One category selection costs 1 credit on this platform.

Pricing:

10 CR – $6
20 CR – $11.4
50 CR – $27
100 CR – $49.8
200 CR – $90
500 CR – $225

The Pros and Cons of BikiniOff

High resolution and instant image generation are two of BikiniOff Bot’s major pros. This tool helps save users time and instantly removes clothes from an individual’s image while maintaining the original quality of the image.

However, one of BikiniOff’s major cons is its high prices. Each image generation requires a certain amount of credits, which can be expensive for some users. Furthermore, since BikiniOff can transform your images into nude images, it often raises ethical and privacy concerns as well.

Conclusion

By exploring the alternatives to BikiniOff, you can discover a diverse range of image transformation tools to effortlessly convert your images into various categories. Above, we have mentioned some of the top alternatives to BikiniOff that offer a variety of advanced features and capabilities to generate images in different categories.

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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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Most Popular 17 Generative AI Tools: Boost Your Creativity

Posted on October 14, 2024 by GilPress

Generative AI has revolutionized the way we generate content from visually stunning images to captivating text. Whether you’re a content creator, artist, writer, developer, or software programmer today, there is a generative AI tool available to enhance your creativity, automate tasks, and improve your workflow. However, with so many options available, it can be difficult to find a suitable generative AI tool.

In this article, we will explore the top 24 generative AI tools that can efficiently boost your creativity.

Most Popular 17 Generative AI Tools and Platforms in 2024

We have listed down the top 24 Generative AI tools that can help enhance your creativity and streamline your workflow efficiently:

1. GitHub Copilot

GitHub Copilot is a generative AI code completion tool created by OpenAI and GitHub. It is a versatile tool that supports multiple programming languages allowing developers or software programmers to work on various projects. GitHub Copilot provides an intuitive interface that understands natural language prompts and generates code snippets based on text descriptions. Copilot is utilized by developers on a large scale for its ability to generate useful code suggestions which helps developers in generating codes efficiently without any errors.

Key Features:

Copilot offers excellent code suggestions based on your specific requirements.
It supports a wide range of programming languages such as Python, Java, TypeScript, C++, and more.
Copilot can integrate with popular integrated development environments (IDEs) such as JetBrains IDEs, Neovim, and Visual Studio Code.

Pricing:

Team: $4 per user/month. This plan includes access to GitHub Codespace, protected branches, draft pull requests, code owners, and more.
Enterprise: $21 per user/month. It includes everything in the team plan along with user provisioning through SCIM, repository rules, audit log API, GitHub connect, and more.

2. ChatGPT

ChatGPT is a popular AI chatbot developed by OpenAI. This generative AI tool is designed to interact in a conversational manner, allowing you to ask your queries and doubts and get data about any topic in a natural language format. You can also utilize ChatGPT to translate text content into another language. This chatbot offers a simple and user-friendly interface that can be easily accessed by everyone including newbies. ChatGPT is one of the most popular AI chatbots worldwide.

Key Features:

ChatGPT can generate a wide range of text content such as blog posts, articles, essays, poems, scripts, musical pieces, and more.
It offers excellent summarization capabilities and transforms lengthy text documents into concise summaries at a quick speed.
ChatGPT offers excellent language translation capabilities through which you can efficiently translate your text content into multiple languages.

Pricing:

Plus: This is a perfect plan for those individuals seeking to amplify their productivity. Available for $20/month, you receive early access to new features, access to GPT-4, GPT-4o, GPT-4o mini, DALL·E image generation, generate and use custom GPTs, and more.
Team: This plan is especially for fast-moving teams and organizations. It is available at a price of $25 per user/month billed annually or $30 per user/month billed monthly. This plan includes everything available in the plus plan along with Unlimited access to GPT-4o mini and higher message limits on GPT-4 and GPT-4o, as well as tools like DALL·E, web browsing, data analysis, and more.
Enterprise: Contact sales to create a custom plan for yourself. This plan is ideal for global companies and includes everything in the team plan.

3. Claude

Developed by Anthropic, an AI company, Claude is an AI chatbot and large language model released in March 2023. This platform can be utilized for multiple purposes such as writing, researching, coding, question answering, and problem solving. Claude lets you generate content in multiple text formats such as articles, essays, blog posts, etc. This chatbot can also fulfill your creative requirements and generate musical pieces, unique storylines, poems, and more. In addition, Claude is built with advanced security features to ensure you have a safe and secure experience. There are multiple Claude models offered by Anthropic such as Claude 1, Claude 2, and Claude Instant. The latest creation of Anthropic “Claude 3” was released in March 2024 which has the capability to analyze images.

Key Features:

Claude can generate a wide range of text content, including articles, scripts, letters, emails, essays, poems, and more.
This tool offers excellent language translation capabilities, through which it can accurately convert your content into another language.
It can also summarize large text content or documents quickly.
Claude can also be utilized for question-answering purposes and it can provide detailed and informative answers to all your queries.

Pricing:

Pro plan: $20/month. This is a perfect plan for beginners. Some of the features included are the use of Claude 3 Opus and Haiku, higher usage limits versus Free, Creating Projects to work with Claude around a set of documents, code, or files, and Priority bandwidth and availability.
Team Plan: $25/month. This plan includes everything in pro. It also offers higher usage limits versus Pro, Share, and Discover chats from teammates, and central billing and administration.
Enterprise: Contact sales to create a custom plan for your team or business.

4. DALL-E

Developed by OpenAI, DALL-E is a generative AI technology that can generate stunning images using text prompts. This tool utilizes deep learning models and GPT-3 language models to create images. On DALL-E 2 you generate visually stunning AI artworks by combining unique art styles and concepts to generate your desired outcome. DALL-E is considered one of the most popular text-to-image generators in the market. The images generated by DALL-E are quite detailed and can be utilized for the fulfillment of multiple tasks such as designing products, creating unique artworks for social media, illustrating stories, and more.

Key Features:

DALL-E offers a diverse range of style options ranging from photorealistic to abstract.
On DALL-E you can combine various styles, concepts, and themes to generate your desired output.
It offers excellent image editing tools. You can modify your images by providing a text prompt specifying the changes you wish to see in them.
It offers a simple and user-friendly interface.

5. Gemini AI

Gemini is an AI-powered assistant that assists with multiple tasks, such as writing, creating pitch materials, generating campaign briefs, project plans, and more. This tool contains multimodal functionality, which means it can understand and process both text and image inputs. One of the stand-out qualities of Gemini is its advanced reasoning and planning; it can handle complex or difficult queries raised by individuals.

Key Features:

Gemini is capable of handling different types of content such as text, images, and audio.
This tool contains enhanced problem-solving capabilities and it can help provide useful solutions and insights by analyzing complex datasets.
Gemini produces quick responses to all your queries and inputs making it a suitable real-time application for various purposes such as chatbots, customer service, and more.
This tool caters to a global audience as it supports a wide range of languages, making it suitable for users with different native languages and cultural backgrounds.

6. Jasper AI

Jasper AI is an innovative AI writing tool that helps you produce high-quality writing content efficiently. This tool is specially designed to assist content creators and marketers in generating various types of writing content such as blog posts, emails, social media captions, product descriptions, and more. This tool also assists you in optimizing your content for search engines which is beneficial to enhance its visibility and engagement. Unlike other tools, Jasper AI allows you to translate your content in different languages making your content accessible to a wider audience without any language barrier.

Key Features:

This tool can generate various types of content such as social media captions, blog posts, product descriptions, email marketing campaigns, and more.
Jasper AI offers good customization options through which you can generate text outputs tailored to your specific needs and requirements.
You can translate your content into different languages via this platform.
It can optimize your content for search engines by enhancing its visibility and reach.

Pricing:

Creator Plan: Available for $39/month, it contains powerful AI features such as 1 user seat, 1 brand voice, access to Jasper chat, etc., through which you can create and improve your content everywhere you work online.
Pro Plan: $59/month, this plan offers various advanced AI features such as 3 brand voices, 10 knowledge assets, 3 instant campaigns, and more. With this plan, you can create content for multiple brands & collaborate on campaigns.
Business Plan: Custom pricing is available. This plan offers personalized AI features with additional control, security, team training, and tech support.

7. Synthesia

Synthesia is an AI video generator that lets you generate studio-quality videos featuring realistic-looking avatars. This AI-based platform can transform your text inputs into stunning AI videos in just a few minutes. Synthesia is a versatile tool that is often utilized for marketing videos, corporate training, language learning, and other purposes. This platform supports multiple languages, enabling you to create videos that cater to a global audience.

Features:

Synthesia offers 200+ free video templates for different requirements such as marketing, internal communication, education, and more.
This tool offers 230+ AI Avatars to choose from.
Turn your texts into high-quality voiceovers in over 140 languages and cater to global audiences effortlessly.
On Synthesia, you can customize the appearance of AI Avatars by choosing their clothing, hairstyle, facial features, etc.

Pricing:

Starter Plan: At $29/month, this plan is perfect for small businesses, content creators, or casual users. Its features include 1 editor, 125+ AI avatars, 3 personal avatars, and 10 minutes of video/month.
Creator Plan: At $89/month, this plan is ideal for professional video makers and businesses that want to deliver a message through high-quality videos. Its features include 1 editor, 180+ AI avatars, 5 personal avatars, and 30 minutes of video/month.
Custom Plan: Connect with Sythesia’s team and generate a custom plan for yourself by customizing the number of editors and guests. This plan features include 230+ AI avatars, unlimited personal avatars, and unlimited minutes of video.

8. AlphaCode

Alphacode is an AI platform designed to assist programmers in generating high-quality codes. This system is developed by DeepMind, a subsidiary of Alphabet Inc. It can generate optimized code solutions in multiple programming languages such as Java, Python, and C++. This platform utilizes machine learning algorithms to identify codes and also learn from the examples of codes to enhance its performance. AlphaCode is an excellent AI platform that can cater to all your coding requirements and help software programmers and developers boost coding productivity and minimize errors.

Features:

AlphaCode can help you in the code generation process, it can create code in multiple programming languages such as Python, Java, and C++.
AlphaCode is a flexible AI tool that can adapt to different coding tasks and programming languages effortlessly.

9. Cohere

Cohere is a top AI platform for enterprises that offers industry-leading large language models (LLMs) RAG capabilities. It helps businesses explore, generate, search, and act upon information intuitively without a language barrier between humans and machines. Cohere’s AI tools are utilized widely across multiple functions, such as writing product descriptions, blog posts, articles, and marketing copy with scalable, affordable generative AI tools. It also helps extract concise, accurate summaries of articles, emails, and documents.

Key Features:

Cohere offers excellent language models through which you can generate multiple text content such as summaries, translations, descriptions, and more.
This tool offers a text completion feature that can provide excellent suggestions for completing sentences and paragraphs.
It offers easy integration into applications using a simple API.
Cohere offers good privacy and security and ensures your data is well-protected without any security breach concerns.

10. GPT-4

GPT-4 is a multimodal model, developed by OpenAI. It’s the fourth generation in the GPT series of foundation models. You can access GPT-4 through ChatGPT Plus, OpenAI’s API, or via Microsoft copilot for free. Since it’s a multimodal model, this means that GPT-4 is capable of using multiple modalities of data such as texts, images, and audio. GPT-4 is both creative and collaborative, and it can create, edit, and iterate with you on both technical and creative writing tasks, including writing screenplays, composing musical pieces, etc. GPT-4 is considered the most advanced system of OpenAI that can produce useful responses to your inputs while maintaining your safety. It can solve tough and difficult problems with greater accuracy thanks to its excellent problem-solving capabilities.

Key Features:

GPT-4 is a versatile tool and it can process and generate both text and image content.
It can generate various types of content such as writing captions, generating code, answering questions, translating texts, and more.
GPT-4 is available for access through ChatGPT Plus, OpenAI’s API, and Microsoft Copilot.

Pricing:

Model	Pricing
gpt-4o	$5.00 / 1M input tokens$15.00 / 1M output tokens
gpt-4o-2024-08-06	$2.50 / 1M input tokens$10.00 / 1M output tokens
gpt-4o-2024-05-13	$5.00 / 1M input tokens$15.00 / 1M output tokens

11. Character AI

Character AI is an AI chatbot platform that lets you engage in personalized conversations with AI characters. This tool offers a diverse range of AI character options in various categories such as Anime, Games, Books, History, Politics, and more. Character AI even allows you to generate a personalized AI character based on individual preferences. This tool not only lets you engage in interactive conversations but also assists you in performing various tasks such as writing a story, brainstorming ideas, receiving book recommendations, and more.

Key Features:

Character AI offers an excellent range of AI character options from various categories such as fictional, anime, historical figures, and more.
This tool offers personalized conversations as AI characters are capable of learning from your interactions and generating responses that are tailored to your style and preferences.
Create a personalized character for yourself and customize various aspects, such as voice, tone, avatar, personality, etc, for an immersive chatting experience.
Character AI can be utilized for multiple purposes such as learning a new language, writing a story, brainstorming ideas, practicing interviews, etc.

Pricing:

Character AI offers a free plan while its premium plans begin at $9.99/month.

12. Midjourney

Midjourney is a powerful generative AI program that lets you create stunning images from text prompts. This is an excellent AI tool for artists, designers, and art enthusiasts as it allows you to explore creativity and generate visually stunning images based on your preferences and likes. You can create stunning art pieces in multiple styles by simply inputting your text descriptions, and the AI will bring your imagination to life within a few seconds. This tool generates four images at once in a single grid. You can even further refine your images by adjusting the style, aspect ratio, detailing level, etc. Some of the most popular use cases of Midjourney include artistic creations, marketing, e-commerce, graphic design, and more.

Key Features:

This tool offers excellent creative control. You can generate your desired AI image outcome by customizing various aspects of your image, such as size, aspect ratio, image resolution, and more.
It offers multiple artistic style options such as surreal, abstract, realistic, etc.
Midjourney offers a simple interface using which you can explore all your creative side and generate unique AI images without any technical knowledge.

Pricing:

Type of Plan	Monthly subscription cost	Annual subscription cost
Basic Plan	$10	$96($8 / month)
Standard Plan	$30	$288($24 / month)
Pro Plan	$60	$576($48 / month)
Mega Plan	$120	$1152($96 / month)

13. Wordtune

Wordtune is a free AI writing assistant designed to enhance your writing style with more clarity and authenticity. This platform is specially designed to aid writers, students, and professionals in every step of their writing process. Wordtune assists you in improving your writing by rephrasing your sentences, correcting grammar or spelling mistakes, suggesting better word options, and more. With this tool, you can switch between casual and formal tones or lengthen or shorten your sentences with the click of a button and keep your messaging on point. Apart from this, wordtune can also summarize your documents, webpages, documents, and even YouTube videos.

Key Features:

It contains a rewrite feature that can instantly paraphrase emails, articles, messages, and more. It can even change the tone of your content and switch it from casual to formal based on your requirements.
It offers a grammar checker that helps identify and correct any mistakes, incorrect spellings, or errors found in your content.
It offers word choice suggestions for your content to improve your writing style and make your content impactful.
On Wordtune, you can rewrite entire paragraphs of your content to improve its structure and enhance its overall effectiveness.

Pricing:

Advanced: $13.99/month or $6.99/month billed annually, this plan is perfect for users looking to perfect their writing for limited daily use. It offers 30 Rewrites and AI suggestions, 15 AI summarizations, unlimited Spelling corrections, Grammar checks, and AI recommendations.
Unlimited: $19.99/month or $9.99/month billed annually; this plan is ideal for those looking to write confidently anywhere, anytime. It offers unlimited Rewrites & AI suggestions, AI summarizations, Spelling corrections, Grammar checks, Vocabulary enhancements, Clarity improvements, Fluency increases, and Premium support.
Business: This plan is perfect for teams looking to showcase their professional side. It offers all the features, along with Business support, SAML SSO, and Centralized billing.

14. ChatPDF

ChatPDF is another powerful generative AI tool that lets you interact with PDF documents and files in a conversational manner. This tool is designed for students, researchers, and professionals. This platform utilizes advanced natural language processing to understand the questions raised and provide accurate and relevant responses and information from the texts within the PDF. With this tool, you can receive all the essential information required for your research efficiently from your scientific papers, academic articles, and books. The best part about ChatPDF is that it accepts PDFs in any language and is available for chat in any language.

Key Features:

ChatPDF offers a conversational interface through which you can ask all your queries in natural language and receive accurate responses based on the content of your PDF document.
ChatPDF helps save your time and effort by instantly searching for important information through PDFs.
It offers multilingual support, allowing it to accept PDF documents in any language and even respond to your questions in any language.
Generate folders to organize your documents and chat with multiple PDFs in one single conversation.

15. Chatsonic

Chatsonic is a conversational AI chatbot that offers real-time web search, PDF, image, and website engagement. This generative AI platform offers factually accurate information in less than 5 seconds, along with citations. On Chatsonic, you can research any topic or subject, chat with any document, generate captivating AI images, and summarize a webpage. The best part about Chatsonic is that it can access and process all the latest news and current event information from the web, ensuring the responses generated are up-to-date.

Key Features:

Chatsonic contains an in-house state-of-the-art web search that delivers up-to-date factual responses with excellent accuracy.
Chat with PDFs by dragging and dropping documents or links into Chatsonic. You can drop different kinds of documents on chatsonic, such as PDFs, Word documents, web pages, or blog articles, to generate instant summaries and insights.
Chatsonic offers an image-generating feature through which you can transform your ideas into captivating AI images.
This tool offers multilingual support, allowing you to communicate and create content in multiple languages, such as English, Chinese, Japanese, French, and more.

Pricing:

Chatsonic plans for individuals & freelancers:

Chatsonic: $12/month, an ideal plan for individuals who just want AI chat like ChatGPT.
Individual: $16/month, essential plan for freelancers and content writers.
Standard: $79/month, this plan is suitable for solo marketers and small teams focusing on foundational content and SEO practices.

Chatsonic plans for professional & teams:

Standard: $79/month, this plan is perfect for solo marketers and small teams focusing on foundational content and SEO practices.
Professional: $199/month, this plan is suitable for SEO specialists and content professionals aiming to enhance strategy and execution.
Advanced: $399/month, this plan is ideal for agencies and in-house marketing teams seeking advanced SEO insights and content dominance.

16. Compose AI

Compose AI is a free Chrome extension that helps improve your writing using AI. It’s designed to write emails, create documents, and chat faster by auto-completing your sentences every time you type. Compose AI can help complete your sentences by suggesting useful words and phrases that perfectly align with your sentence and help enhance your writing flow. It also provides grammar and style suggestions pointing out any grammatical errors or spelling mistakes in your content. Overall, Compose AI is a beneficial tool for professional writers, students, or someone who wants to improve their writing skills.

Key Features:

Compose AI helps you write different types of content such as blog posts, marketing copy, paragraphs, sentences, headlines, topical information, and more.
It provides an excellent sentence completion service by suggesting words or phrases to complete your sentences, improving the flow of your writing, and saving you time and effort.
Compose AI can draft emails for you and generate professional responses to emails with a single click.
It includes a “Rephrase” feature that can rewrite your sentences to enhance clarity and ensure they appear professional and well-written.

Pricing:

Premium Plan: $14.99/month or $9.99/month billed $119.88/year. It offers 25,000 words per month, 50 easy email replies, unlimited advanced autocomplete, and more.
Unlimited Plan: $44.99/month or $29.99/month billed $359.88/year. It offers unlimited AI-generated texts, unlimited easy email replies, advanced autocomplete, early access to new features, and more.

17. Vertex AI

Vertex AI is an innovative machine learning platform that lets you generate and utilize various AI applications. This tool combines data science, ML engineering workflows, and data engineering all together. Vertex AI is designed for data scientists and machine learning (ML) engineers allowing them to build, train, deploy, and manage ML models. Vertex AI offers a variety of options for model training and deployment such as AutoML, Custom training, Model Garden, and Generative AI.

Key Features:

Vertex AI allows you to train and deploy custom ML models by using various frameworks such as XGBoost, TensorFlow, and PyTorch.
Its AutoML can process multiple tasks such as image classification, tabular data prediction, and natural language processing.
It contains a model monitoring feature through which you can track the model performance or identify any issues.
Generative AI also lets you create and customize large language models (LLMs) for various applications based on your requirements.

Which is the best generative AI Tool?

The best generative AI tools are ChatGPT, DALL-E, GitHub Copilot, AlphaCode, Gemini, Compose AI, ChatPDF, Midjourney, Amazon SageMaker, and more.

What are the free generative AI Tools?

Some of the free generative AI tools are ChatGPT, Gemini, ChatPDF, Character AI, Claude, Compose AI, and more.

Bottom Line

There is no doubt that generative AI has reshaped the creative landscape globally with its excellent content creation capabilities. Above, we have listed the top 24 generative AI tools tailored to different creative fields, such as image generation, content creation, translation, programming, and more. These tools can help enhance your creativity and automate tasks by offering a perfect combination of inspiration and productivity.

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The 7 Cs of the Cloud: A Big Data Taxonomy

Posted on July 17, 2011 by GilPress

What’s the new landscape of the information business as Cloud Computing and the Big Data Wave usher in the Age of Big Cloud Data (ABCD)?

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

London DataDive September 28, London

What is Bikini Off Bot?

Alternatives to BikinOff

1. Nudify.online

Features:

Pricing:

2. Promptchan AI

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3. SoulGen AI

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4. DreamGF AI

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5. Nubee.AI

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The Pros and Cons of BikiniOff

Conclusion

21 Generative AI Examples and Use Cases Across Industries

1. Language Translation

2. Chatbot performance improvement

3. Code generation

4. Content creation

5. Image generation

6. Automate testing

7. Code completion

8. Collaborative coding

9. Debugging code

10. Image-to-image conversion

11. Text-to-Speech Generator

12. Summarization

13. Video generation

14. Writer

15. Sales and Marketing

16. Project management and operations

17. Product development

18. Customer service

19. Fraud detection and risk management

20. Medical Image Synthesis

Bottom Line

What is Artificial Intelligence?

The History of Artificial Intelligence

Groundwork for AI:

Birth of AI: 1950-1956

AI maturation: 1957-1979

AI boom: 1980-1987

AI winter: 1987-1993

AI agents: 1993-2011

Artificial General Intelligence: 2012-present

Who Invented AI?

First Artificial Intelligence Robot

When Did AI Become Popular

What does the future hold?

Most Popular 17 Generative AI Tools and Platforms in 2024

1. GitHub Copilot

2. ChatGPT

3. Claude

4. DALL-E

5. Gemini AI

6. Jasper AI

7. Synthesia

8. AlphaCode

9. Cohere

10. GPT-4

11. Character AI

12. Midjourney

13. Wordtune

14. ChatPDF

15. Chatsonic

16. Compose AI

17. Vertex AI

Which is the best generative AI Tool?

What are the free generative AI Tools?

Bottom Line

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