Tag Archives: AI

How TaskRabbit and Handy are making it easier to get things done?

Reading Time: 3 minutes

What’s “sharing economy”?

It can be described as an economic model in which goods and resources are shared by individuals and groups in a collaborative way such that physical assets become services. The sharing economy’s growth has been facilitated through advances in big data and online platforms…

The sharing economy is one of the most rapidly growing market phenomena in history. Since 2010, investors have contributed over $23 billion in venture capital funding to start-ups that are using a share-based business model. As many of the share-based firms are private, it is difficult to know the exact size of the sharing economy.

The sharing economy involves short-term peer-to-peer transactions to share use of idle assets and services or to facilitate collaboration. The sharing economy often involves some type of online platform that connects buyers and seller.

One of prominent examples of sharing economy platforms in the home services sector are TaskRabbit and Handy.

TaskRabbit: The On-Demand Convenience

TaskRabbit is an odd-job service that operates in over 61 U.S. cities and connects users, called Taskers, to paying gigs. Taskers set their own rates and may get tips. Popular jobs with higher earning potential include handyman-type tasks, moving and cleaning, according to the company.

To be eligible, you must be 18 or older, have a Social Security number, checking account, credit card and smartphone, and pass background and ID checks.

You need to use the Tasker mobile app to create an account and go through the verification process. You’ll have to provide basic information about yourself, upload a profile photo, set up direct deposit, set pay rates and state your level of experience for your task categories.

For each service you provide, you’ll also have to add a “quick pitch” detailing why people should pick your services (more on that later). To help you maintain positive reviews and ratings, TaskRabbit recommends limiting those services to ones you “can perform at a high-quality level.”

If TaskRabbit approves your application, you’ll be charged a nonrefundable $25 registration fee. And that’s the only bill you’ll foot as a Tasker. All other fees come from clients.

Handy: Expertise at Your Fingertips.

Have you been putting off a collection of household tasks you just can’t seem to get to? Handy’s easy-to-use platform can get them scheduled, paid for, and finished in no time—sometimes even the same day!!!

For smaller household tasks such as furniture assembly, cleaning, and TV mounting, Handy is an excellent platform. Its streamlined one-step process of booking a professional and getting a quote is convenient for those who need last-minute services, as well as for clients who are simply too busy to invest more time in researching who to hire for the job.

SPECS

  • Service area: Select cities across the U.S.
  • Services categories: Cleaning, furniture assembly, general handyman services, electrical, plumbing, moving, home improvement projects, and more
  • Scheduling: Online, mobile app
  • Cancellation policy: Free cancellation up until 24 hours before appointment; $25 fee for cancellation 2 to 24 hours before appointment; full cost for cancellations within 2 hours of appointment

PROS

  • Simple and streamlined one-step booking process
  • Vetted and screened professionals
  • Special deals available through popular retailers such as Costco, Crate & Barrel, Wayfair, Walmart, and Mr. Clean

CONS

  • Relatively short complaint-filing window of 72 hours after the service was provided
  • Insurance information for hired individuals not readily available
  • Reports of quotes being significantly lower than actual cost incurred.

The Sharing Economy Revolutionizing Home Services!

Overall Impact

TaskRabbit and Handy have revolutionized the home services industry by addressing several key challenges faced by traditional service providers:

  • Accessibility: These platforms make it easier for customers to find and book services, eliminating the hassle of searching for and vetting individual providers.
  • Convenience: Online booking, real-time tracking, and secure payment options provide a seamless user experience for customers.
  • Reliability: TaskRabbit and Handy’s screening processes ensure that customers are matched with qualified and trustworthy professionals.
  • Transparency: Clear pricing structures and customer reviews foster trust and confidence in the services offered.

As a result of these factors, TaskRabbit and Handy have significantly expanded the reach of home services, making them more accessible and appealing to a broader range of consumers. The sharing economy approach has also led to increased competition and innovation in the industry, driving improvements in service quality, customer satisfaction, and overall efficiency.

In conclusion, TaskRabbit and Handy have played a transformative role in the home services industry, offering customers greater convenience, reliability, and transparency. The sharing economy model has revolutionized the way people access and manage home services, paving the way for a more efficient and customer-centric industry.

Isn’t that great? Let me know what do you think about those business models in the comments 🙂

Sources:

https://g.co/bard/share/ed7f9410c995

https://corporatefinanceinstitute.com/resources/economics/sharing-economy/

https://www.investopedia.com/terms/s/sharing-economy.asp

https://www.nerdwallet.com/article/finance/getting-started-taskrabbit

https://www.bobvila.com/articles/handy-review/

Tagged , ,

AI’s Promising Debut in Auditing – has AI made it to the Big Four ???

Reading Time: 4 minutes

Artificial intelligence is rapidly transforming the consulting industry, and the Big Four firms – Deloitte, EY, KPMG, and PwC – are at the forefront of this change. These firms are investing heavily in AI to develop new services, improve the efficiency of their operations, and gain a competitive edge.

AI’s Journey to Big Four Consulting Firms

Are the Big Four Still 'the Best'?

I think it is important to firstly give you a little introduction of how AI was used before by the Big Four. AI’s journey to the Big Four consulting firms has been a gradual one, marked by both skepticism and cautious adoption. The initial hesitation stemmed from concerns about the potential for AI to replace human consultants, the lack of proven applications in the consulting industry, and the ethical implications of using AI to make decisions.

However, as AI technology matured and its benefits became more apparent, the Big Four firms began to embrace its potential. In 2016, PwC launched its first AI innovation center, and the other Big Four firms soon followed suit. Since then, AI has become an integral part of the Big Four’s operations, with applications ranging from data analysis and automation to client relationship management and risk assessment.

EY’s Pioneering Use of AI for Audit Fraud Detection

EY has been at the forefront of using AI for audit fraud detection recently.

EY Helix – Audit technology

When Big Four accounting firm EY tried out an artificial intelligence system Helix GLAD, it was able to detect fraudulent journal entries in a dataset of real-world financial data. According to Kath Barrow, EY’s UK and Ireland assurance managing partner, the new system detected suspicious activity at two of the first 10 companies checked. The clients subsequently confirmed that both cases had been frauds.

This early success illustrates why some in the industry believe AI has great potential to improve audit quality and reduce workloads. The ability of AI powered systems to ingest and analyse vast quantities of data could, they hope, provide a powerful new tool for alerting auditors to signs of wrongdoing and other problems.

Yet many auditors disagree sharply about how far they can rely on a technology that has not yet been widely tested and is often poorly understood. Some audit firms are sceptical that AI systems can be fed enough high quality information to detect the multiple different potential forms of fraud reliably. There are also some concerns about data privacy, if auditors are using confidential client information to develop AI.

  • “Frauds are . . . unique and each is perpetrated in a slightly different way,” Stephens said. “By nature they are designed to circumvent safeguards through novel uses of technology or exploiting new weaknesses, and AI doesn’t play well there right now.” – Simon Stephens, AI lead for audit and assurance at the UK business of Deloitte, another of the Big Four audit firms, pointed out that frauds were relatively rare and tended to differ from each other. That would mean there were not necessarily tell-tale patterns for AI systems to pick up.
  • KPMG UK, another Big Four auditor, echoed the concerns of Stephens at Deloitte. “Fraud by its nature is unpredictable and therefore using known fraud cases to train machine learning models is challenging,” KPMG said.
  • “AI can automate some of the more mundane, repeatable tasks and allows our auditors to focus on the areas of greatest risk,” – Stephens acknowledged that the technology had its uses in auditing. But he saw a far more limited role for it. As Deloitte currently restricts use of AI to less complex tasks, providing clear instructions on what kinds of anomalies to look for in company accounts.

So Why Only EY Uses AI for Finding Audit Frauds

There are a few reasons why EY is the only Big Four firm that has publicly announced its use of AI for audit fraud detection. First, EY is a leader in the audit and assurance space, and it has a strong track record of innovation. This gives EY the credibility and expertise to develop and implement AI-powered audit tools.

Second, EY has made a significant investment in AI, both in terms of financial resources and human capital. This investment has allowed EY to develop Helix GLAD and other AI-powered audit tools that are at the forefront of the industry.

Third, EY has a strong culture of innovation, and it is willing to take risks on new technologies. This culture has fostered an environment where EY’s auditors are comfortable using AI to detect fraud.

As AI technology continues to mature, it is likely that other Big Four firms will adopt AI-powered audit tools. However, EY is currently the leader in this area, and it is well-positioned to maintain its lead in the years to come.

What is to expect (my opinion)

AI is transforming the whole world right now, and of course the consulting industry will not be an exception, and the Big Four firms will be at the forefront of this change. Even though now almost all consulting firms (except for EY) are very sceptical about the AI usage, I think AI implementation in consulting industry will be for the better, as it will automize work, for sure, and also find some problems/issues that we as people are not able to indicate, as we all can make mistakes.

Of course as were said below there many negative points too, but looking at the consulting industry, what is the one of the most important things after the result itself? – Right, audit fraud detection and this is exactly what AI can help us with. What I am trying to say is that even though there some risks that should be considered, when AI can help so much we should not avoid it, but improve and implement it, which is exactly what EY did. I think EY made the first step, and all the other consulting companies will soon follow

What is your opinion?

Sources(reference):

  1. https://www.ft.com/content/b18961f1-c65c-433b-8dd4-05196fa0e40a ( post about EY usage of AI for auditing frauds )
  2. https://www.forbes.com/sites/adelynzhou/2017/11/14/ey-deloitte-and-pwc-embrace-artificial-intelligence-for-tax-and-accounting/?sh=3948df8b3498 ( from Forbes about all Big Four usage of AI )
  3. https://www.ey.com/en_gl/audit/technology/helix ( more about Helix GLAD technology from EY )
  4. https://www.ey.com/en_gl/news/2023/09/ey-announces-launch-of-artificial-intelligence-platform-ey-ai-following-us-1-4b-investment#:~:text=EY.ai leverages leading-edge,by a robust AI ecosystem. ( new AI technology launched recently by EY)
  5. https://web.archive.org/web/20210815025449id_/https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1007&context=amcis2020 ( impact of AI on auditing )

AI generators used:

  1. Google Bard (key words: AI, Big Four, consulting, auditing frauds, EY)

Tagged , , ,

Can AI wipe out real art?

Reading Time: 4 minutes
What Is an AI Art Generator? Features, Benefits and More

AI art production is a controversial topic that has sparked debates among artists, critics, and the general public. Some see AI as a powerful tool that can enhance human creativity and generate novel and original works of art. Others view AI as a threat that can undermine the value and meaning of human art and creativity. In this article, I will examine some of the arguments for and against AI art production, and offer my own perspective on this issue.

One of the main arguments in favor of AI art production is that it can expand the possibilities of artistic expression and exploration. AI can create images, music, text, and other forms of art that humans may not be able to imagine or produce on their own. AI can also learn from large datasets of existing art and generate new variations, combinations, and styles that can inspire human artists. For example, Dall-E 2, an AI image generator developed by OpenAI, can produce realistic and surreal images based on any text prompt, such as “a sea otter in the style of Girl with a Pearl Earring” or “Gollum from The Lord of the Rings feasting on a slice of watermelon” 1. Some of these images can be considered as artistic and creative, and may even evoke emotions and meanings in the viewers.

Imagine AI Art Generator Reigns Supreme In Outshining Its Competitors

Another argument in favor of AI art production is that it can democratize the access and participation in art and culture. AI can lower the barriers of entry and cost for creating and consuming art, and allow more people to express themselves and enjoy art. AI can also enable collaboration and interaction between human and machine artists, and foster new forms of art and culture. For instance, Midjourney, an AI art platform, allows users to create and share AI-generated images using text prompts, and also edit, remix, and comment on other users’ creations 2. Midjourney claims that its mission is to “empower anyone to create and explore art” and that it is “building a community of creators who are passionate about AI and art” 2.

However, not everyone is enthusiastic about AI art production. Some of the main arguments against it are that it can diminish the quality and authenticity of art and creativity. AI can produce art that is superficial, derivative, and lacking in originality and intention. AI can also copy and exploit the work of human artists without their consent and recognition, and violate their intellectual property rights. For example, some AI art generators, such as Deep Dream Generator and Stable Diffusion, rely on databases of already existing art and text to create images from prompts 3. These databases may contain pirated or licensed images that belong to other artists, and the AI may not properly credit or compensate them. Some human artists, such as children’s illustrators, have expressed their concerns and frustrations about the legality and ethics of AI art generators, and launched an online campaign called #NotoAIArt 3.

Artists: AI Image Generators Can Make Copycat Images in Seconds

Another argument against AI art production is that it can devalue and replace the role and skill of human artists and creatives. AI can generate art faster, cheaper, and more efficiently than humans, and may outperform and outsmart them in some tasks and domains. AI can also automate and standardize the process and outcome of art production, and reduce the need and demand for human art and creativity. For example, some AI tools, such as GPT-3, Imagen Video, and Lensa, can generate text, video, and audio content that can be used for various purposes, such as journalism, education, entertainment, and marketing 4. Some critics have predicted that AI will eventually eliminate creative jobs, undermine human creativity, and erode the cultural and social value of art 4.

My own view on AI art production is that it is neither a blessing nor a curse, but rather a challenge and an opportunity for human art and creativity. I think that AI can be a useful and powerful tool that can augment and complement human art and creativity, but not replace or surpass it. I think that AI can create art that is impressive and interesting, but not meaningful and expressive. I think that AI can learn from and collaborate with human artists, but not imitate or compete with them. I think that AI can democratize and diversify art and culture, but not trivialize or homogenize them.

Therefore, I think that the key to AI art production is not to reject or embrace it, but to regulate and integrate it. I think that we need to establish clear and fair rules and standards for the use and development of AI art tools, and protect the rights and interests of human artists and consumers. I think that we need to educate and empower human artists and creatives to use AI art tools effectively and responsibly, and enhance their skills and talents. I think that we need to appreciate and celebrate the diversity and uniqueness of human and machine art, and foster a culture of mutual respect and collaboration. I think that we need to recognize and embrace the potential and limitations of AI art production, and explore its implications and possibilities for the future of art and creativity.

source:

https://www.theguardian.com/technology/2022/nov/12/when-ai-can-make-art-what-does-it-mean-for-creativity-dall-e-midjourney

https://www.techradar.com/features/best-ai-art-generators-compared

https://picsart.com/ai-art-generator

If it wasn’t created by a human artist, is it still art?

https://www.newscientist.com/article/2266240-ai-art-critic-can-predict-which-emotions-a-painting-will-evoke/

GOOGLE BARD AI

Tagged , ,

Dangerous, unpredictable ocean: Is AI the key to unlock and discover the mysterious ocean’s depths?

Reading Time: 4 minutes
Our planet, Earth, is covered 70% by a large area of salt water which we call ocean. The total surface of all oceans in our planet are equal around 361 million kilometers per square.
Based on classification created by International Hydrographic Organization, we distinguish 5 oceans:
1.    The Pacific Ocean – the biggest ocean which occupies around 30% of the whole Earth.  Also, in the western part of the Pacific is located the Marian Trench that for now is known as the deepest place on our planet. The maximum measured depth is 10984 meters under sea level.
2.    The Atlantic Ocean – the second ocean when it comes to surface in the world. This ocean covers 20% of our planet with the biggest trench equal 9219 meters under sea level.
3.    The Indian Ocean – the third-largest ocean that covers 19,8% of the water on Earth’s surface. The deepest trench of this ocean is the Sunda Trench with a maximum depth of 7290 meters under sea level.
4.    The South Ocean – the fourth-largest ocean placed in the south part of our planet. Its deepest part is South Sandwich Trench with the deepest point at level of 8266 under sea.
5.    The Arctic Ocean – the smallest and shallowest ocean on Earth. It is situated around the Arctic. The deepest point of this ocean is the bottom of the region Amundsen around 4650 meters under the sea.
Oceans are badly big that people have discovered only a little part of them. Based on data less than 20% of the world’s ocean floor has been mapped in detail. Of course, humans are still working to create more information about the ocean floor but due to the hard conditions which are there it is a very slow process. However here with help comes AI that is bringing bright future to the exploration of the oceans because it will increase frequency of ocean expeditions and improve their efficiency. So, it is finally time for us to get more information about our planet?

1.    Autonomous Seacraft.

First, AI can be used to automate submersibles that could work on their own. This action will give the opportunity to explore and map the seafloor for twenty-four hours per day. Without any doubt the area of exploration will grow immediately. Also, AI-powered submarines would decrease the risks of human lives from ocean expedition.

On the other hand, building such a vehicle is a risky project due to the condition that is in the deepest part of our oceans. Even the hardest elements which we know for today will not survive undamaged for long time on 8000 meters level under the sea level because of big pressure and corrosiveness of salt water. Unfortunately, today’s building material is going to be destroyed against ocean nature.

2.    Ocean Data Analysis.

Using AI algorithms in oceans exploration can efficiently process and analyze large volumes of precise oceanographic data, including data from satellites, buoys, and underwater sensors.  This capability allows for the rapid identification of patterns, anomalies, and trends in oceanographic variables such as temperature, salinity, and currents. Predicting the ocean’s condition will allow us to prepare properly for the ocean expedition.

However, relying too much on AI ocean analysis can be very misleading because data in the ocean is changing rapidly. Due to these algorithms can be inefficient and can generate wrong output.

3.   Precision Mapping of Ocean Floor.

AI is revolutionizing the process of creating underwaters map thanks to advanced imaging technologies and machine learning algorithms. Sonars powered by this AI technology which are put under the water, are finding underwater resources, identifying potential hazards, and searching for helpful data that is really needed for ocean explorations. Moreover, such gear can measure the depth in unknown parts of oceans.

It is worth saying that very remote environments on large depth under the sea can cause errors in the process of mapping ocean data. Because of the lack of light, it is hard for AI to analyze data which is surrounding it.

4.   Image and Video Analysis of Ocean.

AI-powered image recognition and computer vision can aid in the analysis of underwater imagery and videos. This is particularly useful for identifying marine species, studying underwater ecosystems, and monitoring the health of coral reefs. It is worth to add here that thanks to usage of image and video analysis we will be able to discover and describe new ocean species which have not been discovered before.

Although there are a lot of similar species in the ocean which cannot be correctly identified by AI. Hard conditions in the ocean have influence on bad quality of image and videos taken there. It would create errors or lack in the collected data which will extend the time of working with such ocean data.

Conclusion: AI is the new future of ocean explorations.

To sum up, AI is going to revolutionize ocean exploration. More ocean data will be gathered by it and new categories of submarines will be created. It will allow us to get more information and statistics from our oceans. Moreover, we are going to find new marine species and there is high probability that by using AI we will find new resources in the ocean that will give us power.

Disadvantages of using AI will be too much reliance on AI, data errors and of course costs which are produced by very hard and unpredictable ocean environment.

My opinion about this topic.

My thought about AI technology used in ocean exploration is that we must use it because we are standing in front of huge discovery of unknown places and species in our world. Nevertheless, we should remember that AI is not going to measure for us all mysterious about our water. We as humans need to use it in the correct way to get access to new data about the ocean.

I am looking forward to your comments about this interesting topic that without any doubt will bring us important facts and new information about our oceans.

Sources:

  • https://pl.wikipedia.org/wiki/Wikipedia:Strona_g%C5%82%C3%B3wna

Articles:

  • https://www.aljazeera.com/features/2023/9/14/titan-implosion-is-ai-the-future-of-deep-sea-exploration
  • https://annualreport.mbari.org/2022/story/unlocking-the-power-of-ai-for-ocean-exploration

AI Engine:

  • https://deepai.org/chat
Tagged ,

Unmasking PhysicsX: A Reality Check on AI in Engineering Simulations

Reading Time: 2 minutes

TechCrunch recently shed light on PhysicsX, a London-based AI startup securing $32 million in funding as it steps out of stealth mode. Co-founded by two theoretical physicists, the company claims to possess an AI platform set to transform engineering simulations, particularly in industries like automotive, aerospace, and materials science manufacturing. Despite the narrative portraying groundbreaking innovation, it’s essential to scrutinize whether PhysicsX lives up to the hype.

Addressing Overlooked Challenges:

The article contends that PhysicsX is addressing an overlooked problem in manufacturing and physical production. However, skeptics might question the uniqueness of the challenges PhysicsX claims to tackle, given the existing landscape of AI-driven simulation tools and platforms already in operation.

Debunking Computational Claims:

PhysicsX boasts the ability to significantly reduce computational costs and time limitations associated with engineering simulations. Skeptics may challenge this assertion, citing the advancements achieved by existing AI-driven simulation tools. Without a clear demonstration of how PhysicsX outperforms its competitors, its advantages may appear more speculative than revolutionary.

Reality Check on Disruption:

While the article positions PhysicsX as a disruptor, a counterargument questions whether the startup is genuinely breaking new ground or simply riding the wave of an established trend in AI and engineering simulations.

Digital Transformation Challenges:

The suggestion that PhysicsX can sidestep digital transformation challenges may be met with skepticism. While the startup focuses on engineering and R&D, the complexities of enterprise-wide transformations extend beyond IT issues, leaving uncertainty about PhysicsX’s ability to navigate such intricacies.

Conclusion:

In conclusion, the TechCrunch article presents PhysicsX as an AI trailblazer in engineering simulations. However, a closer look raises questions about the uniqueness of the problems it claims to solve, the comparative advantages of its platform, and the transformative impact it will have on industries. As the hype settles, only time will reveal whether PhysicsX emerges as the revolutionary force it’s touted to be or falls into the category of promising yet overhyped ventures in the dynamic world of AI.

Sources:

article:

PhysicsX emerges from stealth with $32M for AI to power engineering simulations

engine: chat gpt (https://chat.openai.com/auth/login)

useful links about the article and PhysicsX:

https://www.crunchbase.com/organization/physicsx

https://www.physicsx.ai/

https://www.linkedin.com/company/physicsx/

Tagged ,

Nvidia DLSS and how AI is changing gaming industry

Reading Time: 3 minutes

NVIDIA’s Deep Learning Super Sampling (DLSS) technology is a game-changer in the world of gaming. DLSS uses AI to generate pixels, thereby increasing frame rates in video games. This technique has been integrated into over 300 RTX games and enhances the gaming experience by increasing frame rates by up to 4x [1]. In this blog post, we will explore how NVIDIA’s DLSS technology works and how it has revolutionized the gaming experience. We will also discuss the impact of NVIDIA’s DLSS technology on the gaming industry and its future prospects.

DLSS 1.0 was the first version of this technology, which was introduced in 2018 . It was designed to improve the performance of games running on NVIDIA’s Turing architecture GPUs by using AI to upscale lower-resolution images to higher resolutions . This allowed gamers to enjoy better image quality and higher frame rates without sacrificing performance . While DLSS 1.0 was a significant improvement over traditional upscaling techniques, NVIDIA has since released several newer versions of the technology, each with its own set of improvements and features .

DLSS 2.0, 2.1, and 2.2 have all introduced significant improvements over their predecessors, including better image quality, reduced input lag, and improved support for games that use ray tracing and VRS .

However, NVIDIA has recently released DLSS 3.0, which is the next revolution in neural graphics . DLSS 3.0 combines DLSS Super Resolution, all-new DLSS Frame Generation, and NVIDIA Reflex, running on the new hardware capabilities of GeForce RTX 40 Series GPUs, to multiply performance by up to 4X over brute-force rendering . DLSS 3.0 is already being rapidly adopted by the ecosystem, with over 35 games and applications integrating the technology, the first of which launched in October. DLSS 3.0 is powered by the new fourth-generation Tensor Cores and Optical Flow Accelerator of the NVIDIA Ada Lovelace architecture, which powers GeForce RTX 40 Series graphics cards [2] . The DLSS Frame Generation convolutional autoencoder takes 4 inputs – current and prior game frames, an optical flow field generated by Ada’s Optical Flow Accelerator, and game engine data such as motion vectors and depth . DLSS 3.0 is a revolutionary breakthrough in AI-powered graphics that massively boosts performance, while maintaining great image quality and responsiveness .

DLSS 3.5 is the latest version of NVIDIA’s DLSS technology, which is set to be released in the fall of 2023 [3]. It will introduce a new AI model called “Ray Reconstruction,” which will create higher-quality ray-traced images for intensive ray-traced games and apps . Ray Reconstruction is an AI-powered denoising algorithm that learns from millions of high-quality images to generate more accurate pixels . DLSS 3.5 will be available in several games and applications, including Alan Wake 2, Cyberpunk 2077, Cyberpunk 2077: Phantom Liberty, Portal with RTX, Chaos Vantage, D5 Render, and NVIDIA Omniverse . DLSS 3.5 is powered by the Tensor Cores on GeForce RTX GPUs and is expected to deliver even better performance and image quality than its predecessors [4].

Artificial intelligence has been a game-changer in the gaming industry, and NVIDIA’s DLSS technology is a prime example of this. DLSS uses AI to create additional frames and improve image quality, resulting in a smoother and more immersive gaming experience. The technology has been widely adopted, with over 300 games and apps now supporting it . DLSS has also been praised for its ability to deliver high-quality images that rival native resolution while multiplying frame rates [5]. With the advent of DLSS, it’s clear that AI is transforming the gaming industry, and we can expect to see more innovations in the future.

[1]https://www.geeky-gadgets.com/how-to-run-ai-locally/#:~:text=In%20the%20realm%20of%20gaming%2C%20NVIDIA’s%20Deep%20Learning,by%20increasing%20frame%20rates%20by%20up%20to%204x.

[2]https://www.nvidia.com/en-us/geforce/news/dlss3-ai-powered-neural-graphics-innovations/

[3]https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-3-5-ray-reconstruction/

[4]https://www.makeuseof.com/what-is-nvidia-ai-powered-dlss-3-5/

[5]https://blogs.nvidia.com/blog/neural-graphics-gdc/

This post was made with bing ai

Image was generated by Dalle 3

Some of the prompts I’ve used:

write an introducion paragraph to a blogpost about nvidia dlls and ai in gaming

write a blogpost paragraph about nvidia dlss 1, dlss 2, dlss 2.1, dlss 2.2,dlss 3, and dlss 3.5

write a conclusion paragraph for a techblog post about nvidia dlss and how ai is changing gaming industry

Tagged ,

The ethical implications of AI in medicine

Reading Time: 2 minutes

Privacy is one of the concerns related to application of artifical intelligence in medicine. Ai technology can easily gather and analyse vast amount of patient data. This include sensitive information like medical history or generic data. Appropriate protocols must be established for data sharing, storage, and collecting in order to guarantee impartial data management.   

Another ethical concern is bias. AI systems are only as good as the data they are trained on, and if that data is biased, the system will be biased as well. This can lead to unfair treatment of certain groups of patients, particularly those from marginalised communities. To ensure the accuracy of AI systems, it is crucial to use diverse and comprehensive data that represents the entire population. Taking this step will enhance the objectivity and reliability of the technology. 

Another important consederation when it comes to artifical intelligence in medicine is transparency. Patients have a right to know how AI systems are being used to make decisions about their health and treatment.Doctors have responsibility to be transparent about the programs thay are using. This will help in building trust between patients and doctors. 

Finally, accountability is a key ethical consideration when it comes to AI in medicine. Healthcare providers have to ensure that AI systems are being used in a way that is consistent with ethical and legal standards. This includes being sure that the algorithms used are accurate, reliable and that they are not being used to discriminate against certain groups of patients. 

In conclusion, the use of AI in medicine has the big potential to change the healthcare and improve patients treatment. However it is important to be aware of ethical concerns that arise when using AI in medicine. By doing so we can ensure that artificial intelligence is used in a responsible and etical way. 

Tagged ,

The Beatles used AI for their new song «Now and Then»

Reading Time: 2 minutes

The Beatles are one of the most iconic bands in the history of music. Their music has been enjoyed by millions of people around the world for decades. Recently, a new song called “Now and Then” has been released, which features all four members of the band. What makes this song unique is that it was created using AI technology. The song was written and sung by John Lennon in the late 1970s, and it was completed by Paul McCartney, Ringo Starr, and George Harrison using AI-assisted software developed for Peter Jackson’s documentary “Get Back”. 

Short film is here!

Main part:

The technology was used to help separate Lennon’s original vocals from the piano music that backed it. McCartney and Ringo Starr recorded the backing instruments, with existing recordings of the late Harrison woven in. The song was released on November 2, 2023, and is dubbed as “the last Beatles song”. The song appeared on a double A-side single, paired with a new stereo remix of the band’s first single, “Love Me Do” (1962), with the two serving as “bookends” to the band’s history.

AI has been used before to restore The Beatles’ music. In the 2021 Peter Jackson-directed documentary series “Get Back,” AI was used to recognize The Beatles’ voices and separate them from background noise. However, archiving recordings of famous singers has been a practice long before AI or computers. Paris Smaragdis, a professor at the University of Illinois Urbana-Champaign who consulted on AI software used by The Beatles, gives the example of audio engineers who, in the early 20th century, worked on recovering the recording of Italian opera singer Enrico Caruso in order to extract his voice and use it with a live orchestra.The use of AI technology is not limited to restoring recordings. It can also be used to autotune the voice to adjust the pitch or replace the real voice with a synthetic one. However, the latter method raises concerns that the technology will replace real musicians. Technology and music have a close relationship, and music has always been about taking new stuff and incorporating it to expand its horizons. Musical instruments like the piano or the flute are examples of how technology has been used to create new sounds 2. In that sense, music created by or enhanced with new technology is a natural step and perfectly in line with what we expect.

Conclusion:

In conclusion, “Now and Then” is a remarkable achievement that showcases the power of AI technology in music production and how it can make possible to expand horizons and opportunities in different industries, help to develop and restore them, as in this case, the hole history of a legendary group was renovated! Once again this shows that intelligence must be developed as its possibilities are limitless. The song is a beautiful tribute to the legacy of The Beatles and a testament to their enduring popularity. It is a must-listen for fans of the band and anyone who appreciates great music 🙂

Sources: 1. Bing

2. Article: https://qz.com/new-beatles-song-now-and-then-ai-john-lennon-1850981701

3. Image: https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.gram.pl%2Fnews%2Fthe-beatles-ostatni-utwor-legendarnego-zespolu-z-liverpoolu-juz-dostepny&psig=AOvVaw0fUafeHqoB73SDYL3ivflr&ust=1700606783998000&source=images&cd=vfe&opi=89978449&ved=0CBMQjhxqFwoTCKDd5IvU04IDFQAAAAAdAAAAABAJ

4. https://www.billboard.com/lists/ways-ai-has-changed-music-industry-artificial-intelligence/

5. https://www.shaip.com/blog/training-data-for-music-ml-models/

6. https://www.mi.edu/in-the-know/ai-music-production-enhancing-human-creativity-replacing/

7. https://primesound.org/ai-in-music/

Tagged ,

AI in Medecine

Reading Time: 2 minutes

AI’s impact on our lives, especially in medicine, is undeniable. Often working behind the scenes, technology like AI not only analyzes data for marketing purposes but also enhances our lives, simplifies tasks, and even contributes to our health.

Let’s delve into how AI transforms medicine and promotes better health. While ChatGPT can’t diagnose or treat illnesses directly, it’s a valuable resource for a broad spectrum of health topics. It offers guidance on wellness, recommends healthy habits, clarifies symptoms, and provides information on various conditions.

The influence of AI in medicine is profound, offering numerous applications that significantly benefit people:

  1. Diagnosis and Prediction AI algorithms scrutinize symptoms, medical images (such as X-rays or MRIs), and patient data, aiding doctors in swift and accurate disease diagnosis. Additionally, it predicts potential health issues based on patient data patterns.
  2. Treatment Personalization Analyzing extensive patient data, AI tailors personalized treatment plans, considering individual genetics, lifestyle, and health history.
  3. Drug Discovery AI accelerates drug discovery by analyzing biological data to predict potential drug candidates, speeding up the development of new medications.
  4. Remote Monitoring AI-powered devices continually monitor patients’ health remotely, supplying real-time data to healthcare providers, particularly beneficial for managing chronic diseases.
  5. Robotic Surgery AI-enabled robots support surgeons during complex procedures, enhancing precision and reducing errors.
  6. Natural Language Processing (NLP) in Healthcare AI-driven chatbots and systems like ChatGPT aid in addressing healthcare queries, scheduling appointments, and offering basic medical information.
  7. Healthcare Management AI optimizes healthcare systems by streamlining workflows, managing resources efficiently, and predicting disease outbreaks.
  8. Genomics and Precision Medicine AI analyzes genomic data, improving disease understanding, identifying genetic predispositions, and proposing personalized treatment options.

These AI applications possess the potential to elevate healthcare by enhancing efficiency, accuracy, and accessibility while enabling more personalized and effective treatments for individuals.

This content was created with the assistance of ChatGPT 3.5, utilizing prompts on the roles of AI in medicine.

Tagged , ,

AI in Military: a chance for new peace or even a greatest threat?

Reading Time: 3 minutes

INTRODUCION

Lethal Autonomous Weapon Systems and AI: Developments and Resistance in ...

In the world of military operations, the use of artificial intelligence (AI) is fastly changing and altering. From Autonomous Weapons Systems (AWS) to advanced logistical support, the defense sector is embracing AI-driven technologies. However, as the abilities of AI develop, so do the challenges of controlling its use, especially in weapons deployment.


WHAT IS AUTONOMOUS WEAPONS SYSTEM?

An autonomous weapons system (AWS) is military technology that operates without direct human control, making decisions and executing actions based on programming and sensor inputs. Unlike traditional weapons, AWS can independently identify, select, and engage targets, raising concerns about loss of human control, ethical implications, and the rapid advancement of AI and machine learning in military contexts.


CONCERNS AND CONTROVERSIES SURROUNDING AWS

Athena AI detection of civilian and military objects

The development of Autonomous Weapons Systems (AWS) is growing, raising concerns about the ethical implications of machines making life-and-death decisions. That includes apprehensions, anxieties, and reservations about the loss of human control, ethical dilemmas, and the rapid progression of AI and machine learning in military applications. Alexander Kmentt, the disarmament director of the Austrian Foreign Ministry, draws attention to the urgency of regulating this technology, noting that “humanity is about to cross a threshold of absolutely critical importance.” The challenge lies in keeping regulations aligned with the fast-paced advancements in AI.


AI IN DEFENSE APPLICATIONS

While AWS sparks concerns, AI’s applications in the defense sector extend far beyond lethal force. Companies like C3 AI are leveraging predictive maintenance for the US Air Force, utilizing AI to analyze vast datasets and predict device failures before they occur. Such applications not only enhance efficiency but also demonstrate the transformative potential of AI in military logistics and maintenance.


HUMAN OVERSIGHT

Ban Killer Robots protest with robot in front of campaigners at Brandenburg Gate in Berlin

One of the most critical aspect of deploying AI in defense is the importance of maintaining human oversight. Catherine Connolly, the automated decision research manager for the Stop Killer Robots campaign, raises valid concerns about the potential for fully autonomous weapons. She argues that safeguards must be in place, ensuring meaningful human control over systems that detect and apply force. It cannot be expected when and where it comes to a tragedy beacuse AWS makes its own decision without someone’s input.


PRECISION VS. HUMAN ERROR

The precision promised by AI-enabled weapons is met with skepticism by some experts. Rose McDermott, a political scientist, questions whether AI can truly eliminate human errors, at the same time suggesting that algorithms should include brakes for human oversight. The debate underscores the need for a careful balance between leveraging AI for enhanced capabilities and preserving human judgment.


AI FROM GLOBAL PERSPECTIVE

Regulating AI in the military is an urgent global concern, not only for conflict situations but also for domestic security applications. Catherine Connolly (Stop Killer Robots campaign), remains cautiously optimistic about the possibility of international humanitarian law catching up with technological advancements. Past agreements on weapons like landmines and cluster munitions provide a precedent for creating norms around the use of certain technologies.


BALANCING INNOVATION WITH ETHICS

Armies Race to Deploy Drone, Self-Driving Tech on the Battlefield - WSJ

As the defense industry embraces AI, finding the right balance between innovation and ethics becomes crucial. The integration of AI in military operations offers unprecedented capabilities, but the ethical considerations and potential risks require careful attention. Striking a balance that ensures meaningful human control and adherence to international regulations will be crucial as AI continues to play an increasingly central role in the future of defense.

https://www.bbc.com/news/business-66459920

Tagged