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2 New Surveys About the Market for Data Scientists
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 21st 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
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.
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%).
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.
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.
Data is Eating the World: A New Economy
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.
See also:
Data is Eating the World: 163 Trillion Gigabytes Will Be Created in 2025
Data Is Eating the World: Enterprise Edition
Big Data and Data Science Events September-December 2012
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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8 Best Deepnude Tools to Create Deepfake Nudes
Deepnude Generators are remarkable technology through which users can transform their clothed photographs into unclothed images with no blur. These tools utilize advanced AI technology and deep learning algorithms to remove clothes from your content and generate accurate deep nude images that can be used for art or educational purposes.
This technology was specially developed for adult audiences who have a keen interest in deep images and should, therefore, only be accessed by adults.
8 Best Deepnude Tools to Create Deepfake Nudes
1. Undress.app
Undress.app is a leading free deepfake nude generator that can easily remove clothes from your images and generate deep nude images quickly. This web-based platform offers a user-friendly interface through which users can effortlessly transform their regular images into deep nudes in just a few simple steps.
Users must choose the “Undress” option to use this platform and upload their images online. Next, draw over the zone you wish to remove clothes from and then select presets, or users can also keep it empty. Once done, click on “Generate,” and the clothes removal process will begin, and your deep nude image will be generated.
Features:
- This tool is capable of generating highly accurate deepnude images within a few seconds.
- It offers a user-friendly interface.
- Users can browse a variety of pre-existing deepnude images under the “Hub” option.
Pricing:
Paid plans begin at $5.49/month.
2. Promptchan AI
Another popular platform that can generate Deepfake nude images using advanced AI technology is Promptchan AI. This tool is capable of generating high-quality deep nudes using text prompts. The stand-out quality of this AI platform is the range of style options available in this platform.
To generate realistic deepnude images on Promptchan AI, users need to sign up on the official Promptchan AI website using their email addresses. Next, tap on the “Edit” option and upload your image. After this, under the prompt section enter your prompt or short description specifying your image requirements such as “remove clothes from images.”
Users can also add a “Negative Prompt” to address things they wish to avoid from the image (Although this part is Optional). Next, select a style upload quality of your choice and click on the “Edit” option. That’s it; Promptchan AI will process your request and generate your deep nude image instantly.
Features:
- Users can create deepnude images using text prompts.
- Extensive style options are available, such as cinematic, art, hyperanime, and fur.
- It contains an “Explore” section through which users can browse a variety of deepnude image creations.
Pricing:
The paid plans of Promptchan AI begin at $5.90/month.
3. SoulGen DeepNude Generator
SoulGen is one of the best deepfake nude platforms. It allows users to easily remove clothes from their photographs and transform them into unclothed images. SoulGen is trained using deep learning models, and it contains an impressive “Edit” tool that allows users to upload their images and remove unwanted objects and clothing to generate Deepnude images.
To use Soulgen, users need to register on the platform using their email and login. Next, tap “Edit” and select the “Edit Image” option. After this, upload the image and select the clothing areas you wish to remove from your picture. Enter a text prompt specifying your requirements and click “Create.” Soulgen will instantly process your request and generate your deepfake nude image on the platform.
Features:
- It is an AI-based tool and utilizes deep learning algorithms to generate Deepnude online.
- This tool is capable of generating realistic-looking results.
- Soulgen contains a simple interface.
Pricing:
Soulgen contains two pricing plans to access advanced features such as priority queue, blur removal, Multi-creations, and more. Here are the two pricing plans of Soulgen:
- 1 month plan: $9.99
- 12 months plan: $69.99
4. Nudify.Online
Nudify Online is a free deepfake nude maker that allows users to eliminate clothes from their photographs and transform them into deepnude images using AI technology. This tool is one of the most accurate deepnude generators that provide high-quality results at a fast speed.
Users can access this tool for free by registering on the Nudify online website using their email address. Once logged in, you must upload your image on the web-based service and paint the areas using a virtual brush to highlight the clothing elements you wish to remove from the uploaded images. Within a few seconds, nudify online will analyze your request and generate a deepnude image based on your preferences.
Features:
- It contains “Auto” and “Manual” paint options to generate deepnude images.
- Nudify online allows users to generate deepnude images in different types such as Lingerie, Barbie, etc.
- This tool allows users to select their desired body type, age, and image resolution.
Pricing:
Free, paid plans begin at $5.49/month.
5. Undress AI
Undress AI is an impressive deepnudify tool that can instantly remove clothes from your images and generate realistic deepnude photographs. This tool is trained using thousands of images using which the platform can easily distinguish and generate deepnude images from a clothed photograph.
This deepnude tool is extremely easy to use, you need to upload your image on the platform, choose a generation mode, and click on “Create.” That’s it, within a few seconds, the advanced AI technology will begin processing your image and generate your naked image.
Features:
- This platform is trained using thousands of AI images.
- Undress AI generates high-quality AI images for you within a few seconds.
- It contains an intuitive interface.
Pricing:
Free, paid plans start from $5.49/month.
5. Candy AI
Candy AI is an AI-powered platform that allows users to generate deepnude online and allows users to have chats with them. This tool offers a variety of customization options through which users can easily explore their fantasies and develop their desired deepnude character by simply choosing tags and filters.
Users can also create deepnude images of an existing character on this platform using text prompts. Overall, Candy AI is a decent AI platform that anyone can easily access thanks to its simple and user-friendly interface.
Features:
- This tool offers excellent options for action, body, clothing, etc.
- Users can browse a variety of AI characters available on this platform through the “Explore” option.
- With this tool, users can create deepnude characters and engage in conversions.
Pricing:
Paid plans of Candy AI are available at $9.99/month.
7. DreamGF
DreamGF is another excellent platform that allows users to generate Deepnude characters. This tool is powered by advanced AI technology and offers various customization options through which users can effortlessly generate their desired deepnude character.
This platform not only allows users to create their desired deepnude content but also encourages users to engage in conversations with them and have a fun time. DreamGF offers a variety of characteristics, styles, and personality options to choose from along with an easy-to-use interface.
Features:
- Variety of style, characteristics, personality, and other customization options.
- It contains a simple and user-friendly interface.
- With this tool, users can create their desired deepnude character and engage in conversations with them effortlessly.
Pricing:
DreamGF offers a free trial option, in which the paid plans start from $9.99/month.
8. Deepnude CC
DeepNude CC is a free deepfake nude AI that allows users to quickly transform their regular photographs into realistic naked images within a few seconds. This tool utilizes neural networks and advanced AI technology to remove clothes from your pictures.
To use this Deepnude CC, users need to register on the web-based platform with an email address and log in. Next, they must upload an image online and adjust the size based on their preference. Now, they must click on the “Process” option. This will instantly analyze their image and remove clothing from their uploaded images within seconds.
Since this tool is specially developed to generate deepnude images users don’t need to provide any prompt for further image generation. Deepnude CC will detect the clothing areas and generate a deepnude image by analyzing and processing your uploaded photograph.
Features:
- Users can create high-quality and realistic-looking deepnude photographs at a fast speed.
- This tool contains a simple interface and users can generate their deepnude images in just three simple steps.
Pricing:
A free plan with blurred images and limited features is available. Paid plans start at $29.95, which is $0.30/image and provides 100 image credits.
Is deepnude legit?
Deepnude is considered illegal in some countries and locations. Therefore, ensuring deepnude technology is legal in your area before using these platforms is important.
Apart from this, you must have the complete consent of the individual before using their image for deepnude creation. Non-consensual deepnude generation is considered illegal, thus users should avoid using images without consent.
What is the difference between DeepNude and Deepfake porn?
DeepNude platforms utilize advanced AI technology to create realistic-looking images by removing clothes from your uploaded images. Meanwhile, deepfake is more about switching the faces of an individual to another person’s body.
Deepfakes often look less realistic in comparison to deepnude technology, as adjusting the face with another person’s body can be extremely hard.
Can I get a premium version of DeepNude for free?
No, you can’t access the premium version of DeepNude for free, and users need to purchase the paid plans to access the premium features.
Can you deep nude a photo with a face swap?
Yes, there is a wide range of AI nude maker tools available through which users face-swap to generate deep-nude content. Some popular face swap platforms are SoulGen, Vidnoz Face Swap, etc.
Is there deep-nude software for PCs?
SoulGen AI, Promptchan AI, Deepnude AI, etc are some of the best deepnude software for PCs that can be easily used to generate deepnude images.
Conclusion
Deepnude generators are impressive AI-powered platforms through which users can effortlessly create images for various purposes such as art, creativity, education, etc.
However, using technology like Deepnude AI requires ethical and legal boundaries. You must have proper permission and consent to use this technology. Using images of people without their consent is considered illegal and can cause legal troubles for users. Therefore, users must access Deepnude AI responsibly.
We Tested 8 Free AI Nudifiers To Nudify Images
AI Nudifiers are AI-powered tools that utilize advanced artificial intelligence technology and deep learning algorithms to nudify pics from existing images and transform them into AI Nude images. These tools help effortlessly Nudify any photo online and generate explicit content with precision and realism. But with a wide range of AI nudifiers options available in the market, choosing the most appropriate platform can be difficult.
This article will mention some of the best and most free nudify tools. Each tool contains unique features and capabilities to help you achieve your desired results.
These platforms are trained with large datasets of clothed and nude images to train the AI model to understand the clothes removal process and generate nude images. Regardless of AI nudifier’s capabilities to easily transform images, it’s important to use these platforms ethically and avoid using images of individuals without their consent.
8 Free AI Nudifiers To Nudify Images
There is a wide range of AI Nudifiers tools that allow users to remove clothing from their existing images and convert them into nude images using just a few clicks. Here are some of the best AI nudifier that you should try:
1. Promptchan AI
Promptchan AI is the ultimate AI nudifier tool that uses advanced artificial intelligence to produce uncensored and adult-oriented images. This nudify AI tool allows users to generate desired images in various styles such as Anime, Cinematic, Art, etc. The “Edit mode” of Promptchan AI allows users to tweak their images and instantly generate their desired outcomes.
To transform your images into nudify images on Promptchan AI, you must start by signing up on the platform using your email. After this, click the “Edit” option and the image you wish to edit. Select an area and write a description of what you would like to change to remove clothing you write “Nudify” in the prompt. Now, click on the “Edit” option, and the advanced AI technology on Promptchan will begin processing your request and making changes to your image.
Overall, Promptchan AI is an excellent platform to Nudify your images and create a nude image in just a few clicks. Its simple interface makes it easier for anyone to access the platform and generate their desired results.
Features:
- Promptchan AI contains an “Explore” section, where users can browse a wide range of adult images and videos and gain prompt inspiration.
- It contains many style options such as Art, Hyper Anime, Cinematic, and Fur.
- The “Edit mode” option allows users to change their existing images and transform them into their desired outcome.
Price:
The paid plans of Promptchan AI starts at $5.99/month.
2. Soulgen AI
Soulgen AI is a leading free AI nudifier that allows users to Nudify a clothed image using text prompts. Soulgen AI contains a simple interface that allows users to transform their casual images into Nude images with just a few simple clicks. Soulgen AI’s “Edit mode” allows users to perform various changes in their images, allowing users to nudify girls and objects using text prompts.
Users must start signing up on Soulgen AI using their email addresses to use this platform. Next, navigate to the “Edit” option on your screen. Upload your image, and select the areas you want to change or remove. Enter a prompt specifying the changes and click the “Create” option. Soulgen AI will process your request and change your image quickly.
Features:
- Users can create their desired AI images using Realistic and Anime-styled text prompts on Soulgen AI.
- Soulgen AI provides excellent customization options through which users can make a variety of changes in their images and generate their desired results instantly.
- This tool contains advanced “Editing tools” that allow users to remove various objects from their existing images including clothes and transform them into a Nude image in just a few simple clicks.
- Premium plan subscribers also have an option to decide the total number of image generation.
Price:
The monthly plan for Soulgen AI is available for $9.99.
The Annual plan of Soulgen AI is available for $69.99.
3. Nudify Online
Nudify Online is a free nudifier platform powered by deep learning algorithms that can create nudified versions of your images with the highest accuracy. Nudify Online contains an intuitive interface through which users can easily Nudify any image with just a few simple clicks and generate highly realistic undressed AI images.
To use Nudify Online, you need to sign up on the platform using your email ID. Next, you need to draw on the areas you wish to undress and use the brush available. After highlighting the areas, click the “Generate” options, and nudify online will begin the clothes removal process and generate your image.
Nudify.Online Features:
- Nudify.Online offers a variety of resolution options for your images, including standard quality, high-quality, and ultra-high quality.
- Users can convert their images into styles like Undress, Lingerie, Anime, and more.
- Nudify.Online allows users to select the image resolution for their photos.
Nudify.Online Pricing:
| Basic Plan | Standard Plan | Pro Plan |
| $5.49/month | $16.99/month | $37.99/month |
| Medium quality | High quality | Excellent quality |
4. Candy AI
Candy AI is a chat application that provides users with a unique platform through which users can engage in conversations with various AI models and talk about everything. Candy AI is trained using large datasets of texts which helps them generate perfect responses to all the questions raised by users and provide a human-like interaction.
This tool allows users to create their character by entering a prompt and choosing an Action, Body, Clothing, Scene, Accessories, and View. With this tool, users can unleash all their fantasies and desires and talk about any topic they want without any restrictions. Candy AI also allows users to engage in role-plays, voice messages, and even requests for images from their virtual companions for a personalized experience on the platform.
Features:
- This tool contains excellent customization options for users to generate their desired virtual companion appearance.
- Candy AI also allows users to select the personality of their AI companion for more personalized interaction.
- This tool offers both text and voice chat options.
Price:
The premium plan for Candy AI begins at $9.99/month.
5. PornX.ai
PornX.ai is another impressive Nudify porn tool that allows users to create uncensored images with the help of easy-to-use filters. This advanced AI platform allows users to create their desired images without any registration process. This tool also offers a Nudify feature through which you can easily remove clothing from your images and transform them into nude images.
To use this platform, you need to start by visiting the official site of Porn.AI and choosing the “Undress” option. Next, upload the image to Nudify and paint it on the zone you wish to change. After this, you have to select presets or leave them empty. Lastly, click “Generate.” Within a few seconds, PornX.ai will process your filters, remove clothing from your uploaded images, and create a nude image instantly.
Features:
- This tool contains many AI-generated images that users can browse for inspiration.
- PornX.AI generates high-quality and realistic-looking AI images.
- It contains a user-friendly interface.
- It offers a wide range of filters and style options that users can access to generate their desired AI images.
Price:
PornX.ai offers a free plan with certain limitations to undress your images without any cost. To access advanced features, you have to subscribe to its premium plan mentioned below:
6. Deepswap AI
Deepswap AI is an advanced AI tool that allows users to create explicit images, videos, and GIFs by swapping the faces of other individuals on this platform. This tool utilizes advanced artificial intelligence and deep learning algorithms to swap the faces of an individual in media.
This tool also contains an excellent Editing feature through which users can effortlessly generate adult-oriented content by removing clothes from their images. To undress an image on Deep Swap AI, users must upload it and select the areas they wish to edit. Next, you need to provide a text prompt specifying the modifications you wish to witness and click on “Generate.” Deepswap AI will instantly process your textual prompts and perform various changes within a few seconds.
Features:
- Deepswap AI can create realistic-looking deep-swap videos, images, and GIFs.
- The advanced “Editing tools” of Deepswap AI allow users to remove clothes from existing images and generate high-quality results.
- This tool contains a user-friendly interface that anyone can access regardless of technical expertise.
Price:
- 1-month plan: $9.99.
- 12 months plan: $69.99.
7. Pornderful.ai
Pornderful AI is a nudifier website that allows users to generate erotic and explicit images using its easy-to-use filters. This tool is specially designed for the adult audience to fulfill all the fantasies and desires of adults and generate high-quality porn images.
This tool provides excellent customization options such as Body Features, Age, Facial expression, Haircut, and more. This allows users to decide on the overall appearance of the image to generate their desired outcome. A stand-out quality of Pornderful AI is its wide range of Body pose options for their character. Overall, Pornderful AI is a good image-generating platform for adult-oriented images that can be used to achieve all your desired fantasies and outcomes.
Features:
- This platform contains excellent customization options such as Body features, hair color, age, facial expression, etc.
- VIP users can also decide the size of their desired image on this platform for AI-generated photos.
- It contains a user-friendly interface that users can effortlessly utilize for image generation.
Price:
The VIP Plan is available at $20/month and provides you access to Premium Tags, Unlimited Image Generations, Image Editor Capabilities, and much more.
8. Pornjourney
Pornjourney is another excellent AI nudifier platform that allows users to create AI-generated nude images using advanced AI technology. Users can create their desired explicit images on this platform by choosing a variety of filters. The stand-out quality of Pornjourney is its excellent customization options.
This tool offers a variety of AI model options along with numerous body type choices, age, ethnicity, pose, hair color, haircut, outfit, and much more. The pro plan even offers an additional prompt feature, through which users can specify their requirements using text prompts to describe their requirements.
Features:
- Pornjourney offers good customization options for users to generate their desired outcomes effortlessly.
- This tool contains an easy-to-use interface that anyone can easily access.
- Pro plan users can access Prompt and Negative prompt features to specify their requirements to generate detailed and specific results.
Price:
A free plan is available through which users can generate up to 100 Images per month. For additional features, users need to subscribe to its premium plan mentioned below:
| Basic Pro Plan | Ultimate Plan |
| $4.99/month | $7.99/month |
| 500 images/month | Unlimited Images |
Ethical Applications of AI Nudifying
AI Nudifying tools can be used in numerous contexts such as the Entertainment industry. At times, AI nudify tools can be used to generate AI nudes as an alternative method to avoid any present difficulty or discomfort using a proper consent-driven approach towards adult-oriented content.
Another ethical application of AI nudifying tools is using such tools in the education sector. Medical institutions can use such tools for human anatomy. AI nudifiers can be valuable aids in showcasing different aspects of the human body or subjects without using any actual human. However, having proper permissions and consent while accessing any images from the Nudifying sites is equally important to avoid legal or ethical troubles.
FAQ’s
Are AI Nudifiers Legal to Use?
The legality of AI Nudifiers depends on your location and jurisdiction since many areas have banned the usage of AI nudifiers, while some areas consider the usage of AI Nudifying tools legal. Therefore, to avoid legal troubles, it is important to look at the rules and regulations set by the jurisdiction in your country or area before accessing the AI Nudifier tool.
Can AI Nudifiers be Used to Create Explicit or Pornographic Material?
Yes, AI Nudifiers can be used to create explicit or pornographic material as these tools contain an undressing feature through which you can remove clothing from any image. It also allows users to create pornographic content in different poses, actions, appearances, locations, and much more using advanced AI technology.
Conclusion
AI Nudifiers tools are impressive AI-powered platforms that can easily remove clothes from your images and transform them into a Nude image. However, with the high potential of ethical and legal issues associated with such platforms, it’s important to use them responsibly with proper permission and consent. Above, we have listed some of the best free AI nudifier tools that you can use to undress your images easily.
Alternatives to BikiniOff
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.
21 Generative AI Examples and its Applications Across Industries
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
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.