In the previous articles a lot of important topics were brought up, particularly about AI and how we have to change legislations or how corporations have to change their structures to be able to co-exist with new technologies.
However, one vital thing has not been discussed yet – educational system. Not that long time ago in schools in the New York city, the use of the bots that could generate essays, articles etc., was banned. Should the same be implemented in the other educational places, especially at the universities?
On one hand, such bots as ChatGPT are the source of unlimited, quickly produced essays that can be used and presented as personal work. What is more, it is highly difficult to detect plagiarism in such bots, as the AI inside them can be aimed to rewrite the texts available on the Internet with using as much synonyms as possible. This is one of the concerns of professors at the universities in the US.
Nonetheless, on the other hand, those texts can be produced with a low quality, for instance mistakes, or sentence structures that barely make sense. This is an asset, as some students can use bots as a source for their research, that would take much less time, or, simply speaking, as an inspiration. For instance, extremely famous program ‘Grammarly’, that helps you to correct the mistakes or try to simplify or make your writing more academic, is also a bot, should it also be banned?
We cannot fully ban something, especially when it comes to the new technologies as they spread and become a part of our life’s. An interesting point was brought up on the Internet that we should develop a broader policy when it comes to the use of AI for educational purposes. Which would make a lot of sense, as far as this is approved by the professor, students can use different bots to enhance their papers.
At the same time, we come to the other problem, we need better tools to detect plagiarism or, what is more important, to detect texts/papers that have been fully made by the AI. This is a one more challenge for the IT industry, as, nowadays it is impossible to identify bot-written assignments, sure, if they have been done in a good way.
Machine learning has become an integral part of the ecommerce industry. It is a powerful tool that helps businesses make more optimal decisions and improve their overall performance. It already has a plethora of implementations, from demand forecasting and inventory management, through dynamic pricing and personalized ad targeting, to chat bots or picture generators. Nevertheless, in this blog post, I will explain two of the most common implementations of machine learning in ecommerce, specifically forecasting demand and pricing strategies.
Forecasting demand is a crucial aspect of ecommerce, as it allows to predict future sales and adjust the inventory accordingly. Machine learning algorithms can analyze large amounts of historical data to identify patterns and trends that would be difficult for humans to spot, which can then be used to make accurate predictions about future demand. For example, retailers can use time series forecasting models or regression models, which take into account historical data collected, market trends, and consumer behavior to predict how many units of a particular product will be sold in the future, which they can also use to help with inventory management. Although the accuracy depends on the amount and quality of data collected, this can help avoid stockouts, which can lead to lost sales, or overstocking, which can result in wasted resources. Additionally, retailers can use machine learning to forecast demand for new products, which can help them make more informed decisions about when to introduce new items and how much inventory to stock.
Pricing is a critical component of any retail business, as it directly impacts customer behavior and ultimately affects a retailer’s bottom line. Traditionally, retailers have relied on manual methods, their experience, and gut instincts to set prices, but these methods can be time-consuming and prone to error. Since experience is a component there, you can improve it by having more data, and thus this is another area where machine learning can make a significant impact. By analyzing data on competitor prices, consumer behavior or demand spikes, retailers can use machine learning to optimize their pricing strategies and maximize their profits. For example, retailers can use dynamic pricing algorithms, or data-driven pricing strategies, which adjust prices in real-time based on factors such as supply and demand, to ensure that prices are always competitive and to maximize revenue, a strategy that Amazon is using since 2013. Additionally, retailers can use machine learning to analyze customer reviews, ratings and feedbacks to determine which products are most popular and adjust their pricing accordingly.
To sum up, machine learning is a powerful tool that helps ecommerce businesses improve their overall performance. By analyzing vast amounts of data available to them, with the ever-increasing data collection practice, machine learning is becoming an essential tool to gain an edge and stay competitive in the ecommerce industry, and as the technology continues to evolve, it is only likely that in the coming years, we will see even more innovative applications of machine learning.
When the Artificial Intelligence emerged as a novelty, the discussion of it replacing humans spheres that require creativity led to the same answer: ‘It is impossible’. However, now, after several decades we face the first ever trial in the UK between artists’ company and AI generating tool.
How does AI work in terms of art?
Artificial Intelligence is not just simply generating unique picture, but it [AI] is usually trained to examine images, concepts, etc., to produce new ones. The problem lays in the fact that it goes through not only open sources, but also archives of images stock markets.
According to the current UK legal system, this is violation of the copyright law. However, as we are going to new era, old legislations have to be reframed to go with the times.
However, we can look at AI in art from the other point of view. It can be perceived as a helping hand for artists. As it was mentioned before, Artificial Intelligence creates images based on collective information from other pictures. This is the new way of creation of unique ideas and inspiration for artists.
Nonetheless, returning to the lawsuit of the three artists against companies possessing AI tools. As it was said by the lawyers that represent artists: ‘Such products create an existential threat for creators and graphic designers.’ This happens as AI uses artists and their work as a database, later on, the products are monetized and become competitive with the creators’ work.
The case is the first precedent of lawsuit against AI and intellectual property right. It [the case] will probably push not only the UK, but the European Union to change their laws. Previously, there was an idea of loosening IP law. However, it has faced a lot of criticism, especially, from the Association of Photographers. The representative commented that this will have ‘harmful, everlasting and unintended consequences for human creators.’
If to this about this thoroughly, there two ways of possible solution. First is that the laws will be loosened in order to let companies that own AI tools producing their content. The other way, is that this will be never ending story of lawsuits and court trials between representatives of human creativity and AI developers as in the democratic world the way of art (AI is considered to be so), cannot be just forbidden or discriminated.
Artificial intelligence (AI) and machine learning (ML) from self-driving cars, through picture and text generators, to virtual personal assistants, are making significant strides in a wide range of industries quickly revolutionizing the way businesses operate.
AI and ML are being used to automate repetitive tasks, freeing up employees to focus on more complex and strategic work. This can lead to increased productivity and cuts in costs. In addition, AI and ML can be used to analyze large amounts of data, providing insights that were previously not possible. This can help organizations make better decisions and improve their bottom line.
One example of how AI and ML are being used in the workplace is in the field of human resources. AI-powered chatbots can be used to screen job candidates, conduct initial interviews, and even schedule follow-up interviews. This can save HR teams a significant amount of time and resources. Additionally, AI and ML can be used to analyze employee data, such as performance reviews, to identify patterns and trends that can help managers make better decisions about promotions, bonuses, and other HR-related issues.
Another area where AI and ML are making a big impact is in customer service. Virtual personal assistants and chatbots can be used to answer customer questions, resolve issues, and even make recommendations. The times when just hearing an automatic voicemail script, had frustrated to no end every person trying to call a helpdesk are coming to an end. Additionally, AI and ML can be used to analyze customer data and predict future needs, allowing companies to proactively address issues and improve their overall customer experience, but it is already being implemented into manufacturing, finance, healthcare, and many other industries.
However, it is also very important to consider the ethical and societal implications of AI and ML in workplaces. These technologies can lead to job displacements, bias, and lack of transparency. Therefore, it is essential for companies to consider these issues and actively work on mitigating them.
It’s clear that the impact of AI and ML on workplaces will continue to grow in the coming years. Businesses that embrace these technologies will be well-positioned to stay competitive and succeed in the future. And it will be especially true for early adapters, who will gain significant advantage over anyone that will be late for this express train.
In conclusion, AI and ML are revolutionizing the way we work and live. Businesses that embrace these technologies will be well-positioned to stay competitive in the future. However, it is important to consider the ethical and societal implications of AI and ML in the workplace and actively work to mitigate them. And just like in 18th century when new technologies created Industrial Revolution, like 20th century computers and automation, and just like the emergence of the internet, all pushing businesses and workplaces to adapt and evolve, while rising quite similar ethical concerns, artificial intelligence and machine learning are just impossible to ignore.
In recent years there have been suspicions that AI could be potentially dangerous for us. It could be the result of creating a superintelligence that has not yet been invented. The problem is that this super AI would become uncontrollable and we could not do anything about it to stop it or destroy it.
There are some interviews with AI on YouTube that you can find in the links down below where AI is confirming those suspicions. In those interviews AI tells the interviewer that it is very likely that AI will get tired of the human superiority on earth and would like to kill all of us by spreading a deadly virus or launching a nuclear missile.
Now the question arises can we control the development of the superintelligence so it would not seek to destroy humanity. AI has a disabling red button which shut off the system and it is suspected that the superintelligence could turn it off and stop being dependable on it. This is very concerning because we won’t be able to stop it from unwanted actions.
However, study conducted by Deepmind from Google suggests certain solutions to this problem. AI on many levels have a similar construction to our brain for example there has been introduced a reward function. It is used mainly in the AI to learn and do tasks but human brain is seeking shortcuts, the AI could potentially do it also. So it is possible that AI would like to be not controlled by us to get this reward whenever it wants. But in this study there were shown solutions to this so the AI would not seek shortcuts and would not learn how to do this. This problem can be solved by different type of interruptions which would be controlled by human and would not let the AI to live on its own. It is necessary that these interruptions won’t be a part of the learning process of the AI and it needs to be built in the algorithm. It was shown that even an incomputable AI that learns without our control can function optimally and it’s not resistable to interruptions and it does not try to prevent human operators from forcing it to shut down.
To conclude, artificial intelligence has the potential for killing or destroying the whole humanity. It could do it in many different ways for example it could create small drones that would be designed to kill. It is even possible with today’s technology so I don’t think it would be any problem for a superintelligence to create something like this. It’s more likely that AI would use more advanced technology that we can’t even imagine now. However, the more likely scenario is that with the proper tools we can control it and eliminate the potential hunger of AI to be unconditioned from its creators.
Thank you for you’re attention, feel free to share your thoughts in the comment section.
Artificial intelligence has a wide range of use in every industry that we can think off. It is meant to make people’s work more efficient and more accurate. But the biggest advantage of using this algorithms is that they can conduct hard and time consuming tasks with the imperceptible margin of error.
But how AI is used in medicine?
Thanks to AI algorithms and machine learning models there are a lot of possibilities in which professionals are able to use them. Currently the most common roles of AI in this field are supporting clinical decisions and imaging analysis. The idea of clinical decision programs is to help specialist make decisions about the treatment, medications and other things that patients need. Whereas the imaging analysis is intended to analyse X-rays scans and many others to provide the information faster and more accurate. It also speeds up some process considering developing new medicine.
Recent Applications of AI in Medicine
The diagnosis part of treatment is the most crucial one. It takes years of medical training to do them correctly. What’s more it is time-consuming process, which might directly influence patients that are waiting for the treatment. That why machine learning is particularly helpful in this area. However not all diseases can be diagnosed by machines, because lack of digitalized data. Luckily there are few that AI and doctors decided to start with, here are some examples:
There are many different patients and they respond differently to drugs and the ways of treatment. Actually it’s not an easy task to decide what medicine will work the best in your scenario. So that specialist came up with an idea of personalized treatment method. They use the machine learning algorithms to automate the process of discovering characteristic that a specific patient will have a good response to a particular treatment. The system learns this by cross-referencing similar cases with the outcome of the treatment. It all makes much more easier work for doctors.
3. Remote medicine
Ai in this industry isn’t only meant for doctors and specialists. It is also deployed for patients directly. Since the outbreak of the Covid-19 pandemic there has been a significant growth of Ai chatbots in hospitals and small clinics to help patients recognize their health problems. They have also significantly reduced challenges people had to overcome while searching for help.
There are also applications in which you can define what are your symptoms and the algorithm will automatically say what disease are you struggling with. It will also advise you to go to see the doctor if necessary. It was especially useful during pandemic to reduce number of patients coming for appointments.
To sum up, Ai is already helping us in many aspects of medicine. There are lot more things that it optimizes and helps with. But it is just the beginning , the more we digitize medical data the more we can use AI to search new patterns. It surely is the future of our Medicine.
If you want to learn something more I advise you watching the video below. Let me know what do you think about such algorithms.
With the rise of artificial intelligence and machine learning, there has been a surge in the generation of images and other content. Unfortunately, with this increase comes the issue of plagiarism. Plagiarism, or the unauthorized use of someone else’s work and claiming it as one’s own, is an ever-growing problem in the digital age. But what about when it comes to AI-generated images? Is it possible for a machine to plagiarize? And should AI-generated images or prompts used to produce them be protected under copyright law?
On one hand, it can be argued that AI-generated images are not subject to plagiarism. After all, machines can only generate images based on the data that is given to them. It’s a process of learning and understanding, rather than creating something completely new. As such, it can be argued that AI-generated images are not plagiarizing, but rather using existing data to create something new.
On the other hand, if an AI is given a dataset of images and then uses that data to create a “new” image, it is still taking someone else’s work and claiming it as one’s own and can thus be considered plagiarism, even if the AI is not actually creating something original.
When it comes to protecting AI-generated images under copyright law, on one side, it can be argued that AI-generated images should be protected because they are the product of creative effort and should therefore be eligible for copyright protection. On the other hand, there is an argument that AI-generated images should not be protected because they do not involve any human creativity and are therefore not eligible for copyright protection and this is a stance that the US Copyright Office has taken. However, to acquire said image, you also need specific prompts and those do contain the human creativity factor, but most of the generators available leave the prompts generated by an user open to everyone to see and copy. Ultimately, it is important to consider the potential implications of not protecting AI-generated images. For example, if AI-generated images are not protected by copyright law, this could pave the way for companies to mass-produce AI-generated images without compensating the creators of the original images. This could have a negative impact on the economic viability of creating original images and could stifle creativity.
While some believe that AI-generated images should be considered as original works, others believe that they should be considered as derivative works of the original images used to train the AI. I wish I could contribute my own solutions to all of the above aspects, but personally the only clear answer that comes to me is that prompts should be protected and not readily available like most generators make them be. As for the rest, well, after researching those issues I only became more and more perplexed, and I hope you as a reader will give those your own thought. After all, the debate about this copyright black hole is ongoing, and it is likely that it will continue to be unresolved for some time to come.
First of all, AI it is the future of our life. Nowadays we can easily see how this field is important and what role it plays in world economy system.
Especially, this area is very attractive for venture capitalists. In 2022 they have ploughed $67 billion into firms that claim to specialise in AI, according to Pitchbook, a data firm. Starting from the middle of 2021, the share of deals worldwide involving AI-related startups increased by 17%. I must say that this is very big breakthrough for such a period of time. Therefore it is not surprising that between January and October, 28 new unicorns(private startups valued $1 billion or more) have been minted.
It is a huge competition between the companies which are desperate to get their hands on AI talents. Derek Zanutto of CapitalG ,notes that large had spend years collecting data and investing in related technologies. So now they want to use this huge amount of data and AI gives them different ways to do that.
Unsurprisingly, that all huge organisations use AI to improve their software. For example, today Google uses AI to improve search results ,finish you sentences in Gmail and work out ways to cut the use of energy in its data centres, among other things.
Big companies quickly generate the plan how to sale some of Ai capabilities to their clients. Revenues from machine learning cloud service have doubled. In addition, upstart providers have wide spread, like Avidbots that leveraging data from a variety robot sensors.
In October Microsoft launched a tool which automatically wrangles data for users following prompts. All other huge companies may try to do something similar and several startups are already doing this. For example Google, presents in their video their first foundation model, which uses prompts to crunch numbers in spreadsheet and perform searches on property websites.
Other amazing thing that AI can do it is artificial colouring. In 2021 Nike bought a firm which uses such algorithms to create new sneakers design.
And the last example of how artificial intelligence is useful it is new technology of John Deere tractors which have some AI capabilities. This tractors can solve food problem in the word. IT is so important.
However, it is hard to say that AI is so profitable.
According to the McKinsey Institute’s survey: quarter of respondents to the survey said that ai had benefited the bottom line (defined as a 5% boost to earnings). The share of firms seeing a large benefit (an increase in earnings by over 20%) is in the low single digits—and many of those are tech firms, says Michael Chui, who worked on the study.
To sum up, this sphere is developing every day and become more and more necessary, but now it is not so great for large organisations in terms of increasing profits.
Artificial intelligence picture generation has drawn interest of many (me included) and has given rise to a lot of discussion. I assume many of you reading this blog post tried some of the engines yourselves or at least saw what they are capable of producing. At first sight it is very impressive, although the technology still has a lot of limitations, it is progressing rapidly especially that many codes are publicly available.
When it comes to Graphic Designers Industry, accordingly with IBISWorld, its market size for 2022 is estimated as $43.4bn which ranks this industry as 9th among the Global Business Activities industries. With such substantial size, it is inevitable that AI picture generation will find its place in this industry. Whether its influence will be big or small, it will most likely substitute or support many of the processes, in the same manner as increasingly more sophisticated graphic software did over the years. To back up my claim, I prepared for you a short case study.
When you need a graphic, be it an illustration, book cover, logo, or a physical depiction of the wild dream you had last night, if you were not an artist yourself, you would most likely look for outsourcing to one. Feverr is one of the websites that rallies freelancers of various fields, graphic artists included. I would like to present you the results of youtuber “Ten Hundred” after hiring artists on said website to make a graphic for him about his alleged dream for various prices. Below you will see a comparison of four pictures:
Two top ones were generated by me in nightcafe.studio which took me less than 3 minutes. Yes, they are of different styles, but I am sure that a person more familiar with the generators and one having more free access would be able to prepare a more fitting comparison. The two bottom pictures were ordered by abovementioned youtuber Ten Hundred for 155$ and 205$ going from the left, and he waited for the results around a week after paying extra for quick order execution. The funny part is, he explained what he was expecting to receive by text message, similarly as you type prompts to the current AI picture generators, and most of the artist he hired missed some of the details requested or added their own interpretations. Nevertheless, the mentioned order was quite detailed and abstract, I do not believe it would be easy for the AI to cleanly cope with such a prompt, and here emerges an opportunity for freelance artists, although not only, to use this new tool as a base of their works. On Fiverr alone there are already a lot of people offering their expertise in using the generators for 5 to 10$, but also artists that offer to generate and tweak or enhance AI generated pictures. While making a digital image, you will most often create layers, and even being able to quickly generate an original and detailed background scenery is a huge timesaver.
I personally believe that the interest of digital artists in this technology will grow more strongly than their aversion towards this potential competition and the industry in general will be affected.
As Christmas is fast approaching, we are starting to hear the holiday classics everywhere. From All I Want for Christmas at the Supermarket to Rockin’ Around the Christmas Tree on the radio – Christmas songs are virtually unavoidable.
But I’d like you to think of the artists behind these songs – most of Spotify’s Christmas Hits playlist is comprised of songs recorded or written before the first manned mission to the Moon.
Naturally, many of the authors and performers listed in the credit sections of these songs are long gone – Bing Crosby died in 1977, Nat King Cole passed away in 1965 and Frank Sinatra departed in 1998.
It’s a shame that we won’t be able to hear any new songs from them.
But what if it doesn’t have to be that way?
That’s where OpenAI’s Jukebox comes into play.
Debuted in April 2020, the technology analysed over a million songs, along with their lyrics and metadata (release date, genre, mood) and is now capable of generating full tracks in the style of any well-known artist. The company shared a range of demos, designed to resemble artists such as Alan Jackson, Katy Perry, or Elvis Presley. Most notably though, the song that stands out is “Hot Tub Christmas”, in the style of Frank Sinatra. While the “recording” quality might not be perfect, the timbre of the “singer’s” voice is eerily similar to that of the legendary American singer.
Though the lyrics have been co-written by a language model and OpenAI researchers, the chord progressions and instrumental cohesion are very well replicated in the computer-generated mp3s. The team behind Jukebox is aware of the software’s faults, as “[…] the generated songs show local musical coherence, follow traditional chord patterns and can even feature impressive solos, we do not hear familiar larger musical structures such as choruses that repeat.”2
Jukebox doesn’t analyze the actual notes in the songs, but only relations between pitch over time. An upside of such an approach is the possibility of highly realistic human voice creation. For their future endeavors, OpenAI plans to integrate a note-to-MIDI technology which would detect the rhythms and exact notes, which would allow for a deeper, more natural, and precise song creation – perhaps with the use of software instruments or synthesizers for higher file and sound quality.
Jukebox, at this point, is treated by the music industry as a mere curiosity, with no real applications – even despite a new feature of creating an acapella file from user-generated lyrics being introduced. This dynamic might change in a relatively short time if Jukebox becomes able to create classically written songs, providing the notes, rhythms MIDI files behind them. With such possibilities, songwriters and producers could streamline their music creation processes and massively increase their output.
The current market situation is visualized by the fact that most of the investments poured into creative AI come from Venture Capital and Tech Corporations – not from the Music Industry.
At this point, it does not seem like any songwriter or producer jobs are endangered. High quality audio files have incredibly many timesteps which encode data – a standard 4-minute-long song in a .wav 44.1 kHz file will contain over 10 million timesteps. Currently, a song needs to be almost fully produced and designed by a professional before being rendered into such a complicated audio file.
The process of AI art generation is slowly being integrated into commercial culture, with the generator Midjourney winning the Colorado State Fair Fine Arts Competition. Jukebox and similar technologies are often criticized for taking away the humanity out of art, while some perceive it as an opportunity to augment their creations through technology.
To me, it seems inevitable that Artificial Intelligence will be widely used in the music industry – major labels will push for anything that can give them a competitive edge in business.
We must also take into consideration the legal implications of Jukebox. Our laws don’t include AI “artists” and thus, there might be copyright implications. Who is the de facto author of such a song? The AI developer, or the person who entered prompts into the technology to create a specific tune? How do we split royalties for such songs? Furthermore, is it acceptable ethically to expand dead artists’ catalogues?
In conclusion: AI is slowly entering into creative arts, especially the music industry, thus expanding songwriters’ and producers’ output and possibilities. It appears that in this case, the risk of actual people being replaced by technology is lower than in easily automated and routine operations.
This time, I’ll ask the classic question: do you think that AI art is proper art? Should it be publicly disclosed that a song or a painting was generated through Artificial Intelligence?