Archive for the ‘Machine Learning’ Category

Fujitsu and the Linux Foundation launch Fujitsu’s automated … – Fujitsu

Japans leading developer of new AI technologies Fujitsu accelerates its commitment to open source innovation with new projects hosted in LF AI & Data Fujitsu Limited, The Linux Foundation

Tokyo, San Francisco, September 15, 2023

Fujitsu Limited and the Linux Foundation today marked the official launch of Fujitsus automated machine learning and AI fairness technologies as open source software (OSS) ahead of Open Source Summit Europe 2023, running in Bilbao, Spain, from September 19-21, 2023. The two projects will offer users access to software that automatically generates code for new machine learning models, as well as a technology that addresses latent biases in training data.

The Linux Foundation approved the incubation of two new projects, "SapientML" and "Intersectional Fairness" on August 24 to encourage developers worldwide to further experiment and innovate with AI and machine learning technologies, with plans to host future activities like hackathons to engage and build a community to promote open source AI.

With these projects, Fujitsu and the Linux Foundation aim to further democratize AI to realize a world in which developers everywhere can easily and securely use the latest technologies on open platforms to create new applications and find innovative solutions to challenges facing business and society.

Offering AI technologies as OSS to developers worldwide opens up new opportunities for innovation across various industries by lowering the barrier of entry. We are excited to work together with Linux Foundation to contribute to the further advancement and spread of AI by launching Fujitsus AI technologies for the Linux Foundations open source projects SapientML and Intersectional Fairness.

We anticipate that offering Fujitsu's automated machine learning and AI fairness technologies as OSS will greatly contribute to the advancement and diffusion of AI. The Linux Foundation welcomes these two projects and looks forward to building the future of AI together.

Fujitsu holds the largest number of AI-related inventions in Japan, with 970 patents from 2014 to October 2022, and in April 2023, launched Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Kozuchi enables users to rapidly and securely test advanced AI technologies and has offered a wide range of customers and partners access to some of Fujitsus most advanced AI technologies.

AI represents one of the most rapidly developing technologies of our time, contributing to the solution of various societal and industrial issues. However, this ongoing development requires advanced expertise in the development and operation of AI technologies, and concerns about the fairness of AI technology keep increasing. To enable further spread and enhancement of AI, a platform to openly share AI technologies with engineers worldwide represents an important prerequisite.

As a non-profit technology consortium, the Linux Foundation approves approximately a dozen selected technologies as OSS projects annually for the development of open technologies. Fujitsu is providing automated machine learning and AI fairness technologies as OSS via Linux Foundation, enabling developers around the world to access and widely use Fujitsu's technology at the source code level to accelerate technological advancement and the development of new applications, while also addressing concerns around fairness and transparency that remain a critical priority in the field of AI ethics.

Fujitsu has been further providing its technologies for automated machine learning and AI fairness as Fujitsu AutoML and Fujitsu AI Ethics for Fairness, together with various AI technologies and GUIs via the Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Moving forward, Fujitsu will offer technology updates for each project on its AI platform.

Fujitsus purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$28 billion) for the fiscal year ended March 31, 2023 and remains the top digital services company in Japan by market share. Find out more: http://www.fujitsu.com.

The Linux Foundation is the worlds leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects are critical to the worlds infrastructure including Linux, Kubernetes, Node.js, ONAP, PyTorch, RISC-V, SPDX, OpenChain, and more. The Linux Foundation focuses on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: http://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

Fujitsu Limited Public and Investor Relations Division Inquiries

All company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

Go here to see the original:
Fujitsu and the Linux Foundation launch Fujitsu's automated ... - Fujitsu

Baseline Scouting’s B2B system for teams combines the eye test … – Sports Business Journal

Baseline Scouting

* * * * *

Our Startups series looks at companies and founders who are innovating in the fields of athlete performance, fan engagement, team/league operations and other high-impact areas in sports. If youd like to be considered for this series, tell us about your mission.

* * * * *

Worlds shortest elevator pitch: Baseline Scouting is a next-generation scouting offering that combines the eye test with machine learning.

Company: Baseline Scouting

Location: New York, N.Y.

Year founded: 2021

Website/App: https://baselinescouting.com/

Funding round to date: We are self-funded/pre-seed.

Who are your investors? We have no investors yet, so we dont have any (funding) raise. We have roughly $10,000.

Are you looking for more investment? Yes.

Tell us about yourself, founder & CEO Anthony Herbert: I was born and raised in Queens, N.Y. I grew up a big basketball and track and field guy. Basketball was always something I had a passion for. Im built more for track long term, but never fell out of love with basketball. Growing up, I would always play video games, team build, draft and develop rosters and things like that. Once I finished running track in college, I still wanted to give back and help people learn things I didnt know growing up so they could develop faster. For me, its always been about development and sports aspects of it. The other half is IT. Growing up, my family joked I was always good with computers and I was the familys computers person when I was two. Once I got into high school I had a concentration in internetworking, so making networks work, computer networking, things like that. It slowly shifted to cybersecurity. When I got to college, those were my two concentrations: networking and cybersecurity. I realized the future was heading in that security direction. I went down the path, helped to found an information security club at the school and thats where my career took off. I learned about machine learning and behavioral analytics while working at Securonix, the leading company for SIEM and user-entity behavior analytics and data indexing and search. Taking both of those things, I was sitting in the living room one day and thinking of what I can do with these things. I have this passion for basketball and sports development and I have all this knowledge. I asked myself: What sets LeBron apart? What sets Magic Johnson apart from every other 6-7, 6-10 person that can jump? They do the right thing and they do it 9.5 times out of 10. When you think about doing the right thing and what that entails, thats behavioral. Thats when things started to click because I was like, I know how to look at it and analyze that it. That really spearheaded the company.

Who are your co-founders/partners? My co-founder is Erick Garcia. He is a best friend from middle school and very basketball oriented. He loves a lot of sports, but baseball and basketball are his two biggest sports loves. Hes not from a tech background, but is very much into New York sports culture. In working on this, we realized he has the knack for scouting. He takes so much initiative when it comes to learning what to scout. He is crucial in helping develop what we look for, how we develop our proprietary statistics and is our chief scout and co-founder. We are partnering with Zinn Sports Groups Sandy Zinn. Hes instrumental in consulting and advising us, basically saying, I believe in what you have, but you have to put it in a way that you can demonstrate what you are doing. We had a concept and semi-idea of what our product would be, and now we have a full-fledged product, social media presence and were doing more things. Thats where he stands out, his industry knowledge and connections and ability to level with us about what we need to do.

How does your platform work? The platforms name is War Room. It is the portal in which we give teams, agents and other end users the ability to see our scouting results but also have access to our data. Thats the two biggest things to say when were selling it to them. Not only are we selling our scouting metrics and results and these player profiles, but were also giving you all this data you can use to say, Hey, this is a different way of how they are looking at things but in the same vein, they are looking at it in an inclusive way for people who just believe eyes. I need to look at video, I can tell you whats good, but then theres a newer wave of, No, I need analytics. We need these advanced metrics and things of that nature. Its subscription-based, giving them access to the platform that meshes everything together for a comprehensive and next-generational picture of scouting. Were currently doing basketball but the plan is to expand pretty soon into other sports. Basketball is our bread and butter and root.

What problem is your company solving? Were solving a few problems. The biggest one is that sports scouting, as a whole, has this level of uncertainty that from time to time rears its head. It goes beyond taking someone at the top of the draft that doesnt pan out. Its also missing out at the end of the draft. Were about being able to quickly and better than everyone else identify where the value is at all levels of the draft, but also making sure teams are getting the biggest return on their value by doing so. The average career for most athletes in most sports lasts four to five years. You have to find that value to have sustained success. Last but not least, these last five years its been really uncertain things all over the place with the pandemic, inflation, things of that nature, so how do you make it so it stands the test of time? Maybe you dont have the resources to get to somewhere in person or to scour tape for hours. We have a system and company resources to do that and aid a team.

What does your product cost and who is your target customer? Our target customers are teams. We also are looking for agents, so anyone who has a value in regards to scouting and these amateur or international pros that havent been to the league yet. The cost for a subscription of one year is $1.5 million and $2 million for two years.

How are you marketing your product? Were doing a few things. Since were going business to business, its not just the traditional path of going on social media, trying to draw as many people as possible. We are still using social media as a visual store to say, These are various samples, bits and pieces of what we are doing, to show you this is what we look at. These are some of our results. Feeding these different tidbits to draw interest from those teams and at least have them asking questions like, How did you arrive at this conclusion, while making it visually appealing and easy to digest. A lot of our marketing is direct contact and sponsored marketing. Doing email, sending out newsletters, direct contact and working relationships on professional platforms such as LinkedIn, and things like that.

How do you scale, and what is your targeted level of growth? Thats the beautiful thing. What we implemented here as a base is very easy to manage. Scaling is just outward scaling. If you need more scouts or support, its adding those individuals in. I dont think you need a ton, considering your customer base, if you look at it, is third entities in basketball. As you go into other sports, youll get more and more, but really you need to support them from any questions or anything they have. Its easy scaling for our platform that is easy to manage and is pretty self sustainable. For our growth, especially with any first round of funding we get, we want to make sure we expand from the legal side and add more people for marketing, media and, of course, a couple more scouts. If we could double our staff size that would be ideal. That will probably sustain us for quite some time.

Who are your competitors, and what makes you different? Thats another beautiful part because theres not a lot of competitors. I was beating myself up trying to figure out who our competitor is. In this space, you have people who are doing half of what we do. There are independent sport scouting agencies, consulting agreements and things of that nature being hired by teams and assisting in-house scouts. From a technology perspective, we have a lot of companies doing video analysis and things like that, like SportVu, AutoStats, NetScouts. None are doing video scouting and analysis and producing results. They are doing two of those at max. Thats what sets us apart, were giving you a holistic solution that is plug and play as well because we have what we value and what we think, but the way the model works is say you have anything you feel should be weighed more than others. We can easily tune that to your needs based on your understanding. Say the organization has their own proprietary metrics or something like that. As long as it's data or something that can be quantified, we can plug that in and factor it in. That separates us from everyone else.

Whats the unfair advantage that separates your company? Its the proprietary aspect of it. We figured out some metrics from a scouting perspective, the visual scouting, being able to generate those metrics, and putting those into our proprietary algorithm. Also, one of our proprietary metrics weve created based off of research and historical context is potential. The potential rating is based on what prospects are capable of doing and some other historical analysis, which when added up produces their potential score. Its our proprietary statistics and analysis that is the unfair advantage we have.

Baseline Scouting

What milestone have you recently hit or will soon hit? Were doing a lot of network building. It has expanded very much. Were connected with a bunch of the teams, if not all of the teams in some aspect at this point. Through our partnership network, were starting to connect with other companies. Weve had talks with other companies on that. Thats the biggest milestone, actually having a network that our name is in and starting to get well known. With our newsletters and things we are sending out, were starting to get the ears and the eyes of some of these organizations. The final thing before we really start making money is to get in the rooms and start having these conversations with these teams and I think were on the cusp of that with our latest information shares and demonstrations.

What are the values that are core to your brand? Our core value is and I learned this from other companies is a family-oriented type of company. A lot of people that work with us are through previous relationships, actual familiar relationships and once you bring in someone like our partner Zinn Sports Group and they have someone that is reputable and they bring them in, its all something that is very self-built and close-knit. Because of that, we work very well together. Ive seen that in other workplaces. You want to keep and maintain it. I would also say simplicity. A lot of things with technology that deal with AI and machine learning and you start getting in-depth with that, you start to lose the simplicity of everything. Things start to get convoluted and you start to lose the goal.

What does success ultimately look like for your company? Success for us would be one of two things. Being the go-to product for all teams in any given league we sell it in, thats the ultimate success to me. When youre in a league, competition is king and money talks. If someone were ultimately to buy it out and absorb us into an organization and we have a role there, that would be a success as well. Ultimately, it would be optioning, which is to be in-house for all the teams in the league.

What should investors or customers know about you the person, your life experiences that shows they can believe in you? Its being knowledgeable in both areas of what we are selling. From an IT perspective, Ive been in the cybersecurity world for more than a decade. Ive done customer service. So not only do I know how to walk the talk, I know how to talk the talk and make sure were actually solving problems and putting out a product that does something when you say it and ultimately assist with the goal of making someone successful. From my roots, I do very much value what being an inner-city New York kid, and really inner-city anywhere, but I can speak very fondly of New York and the experience of what it means to be somewhere gritty and understand when you make it out of somewhere and have those values, you can do anything you set your mind to. My goal is to provide you with service and product thats going to do right by you, and make sure you know we are going to work closely to help us grow and the company grow as well.

What makes your analytics and scouting better than the competitors? One thing is the base of what we do. A lot of people have these technologies and means for the pro game. When it comes to the amateur level and outside of the pro game, there is a big gap in what people are doing, so that takes time, resources and money. We have that out there. Were already doing that and figured out that simplistic way to do it and weve boiled down whats meaningful. When I talk about whats meaningful, people will scout every game and be like, He dropped 30 points in this game. Im like, Well, that doesnt mean anything because its not translatable. Anything weve done, weve done the research, worked through our algorithms, means and methods to say were doing meaningful, translatable actions as our goal. Thats going to set us apart because weve already done that work and gone through that and a lot of these others dont care to go that deep into this level or are looking at something very niche or narrow like player movement. Thats great from understanding by a biomechanical aspect and stuff like that, but thats not getting into the game that shifts so rapidly. Our platform is dynamic and able to adapt with that stuff.

Originally posted here:
Baseline Scouting's B2B system for teams combines the eye test ... - Sports Business Journal

UK space sector has sights set on artificial intelligence and machine … – GOV.UK

New recruits with skills in artificial intelligence (AI) and machine learning are in high demand to harness the benefits of emerging technologies in the UK space sector, according to a new survey.

The latest research into space skills across businesses, government and academia shows that nearly all space organisations experience some skills-related issues (95%), well over a third (37%) are missing expertise in software and data analysis, and nearly a quarter (21%) are expressing the need for AI and machine learning specifically, higher than any other technical area.

According to the UK Space Agencys Space Sector Skills Survey 2023 developed in partnership with the Space Skills Alliance and know.space - software and data analysis accounted for half of all vacancies across the sector.

As set out in the National Space Strategy in Action the space sector needs a strong pipeline of talent, but the supply of skilled and experienced professionals has not kept pace with such a fast-growing industry, which has more than doubled in income over the last decade (from 8.3 billion in 2009 to 17.5 billion in 2021).

Thats why the UK Space Agency plans to invest 15 million in education, skills and outreach over the next two years, a near fivefold increase in support for these activities.

Professor Anu Ojha, Director for Championing Space at the UK Space Agency, said:

Our rapidly evolving space sector is home to ambitious organisations pursuing cutting edge science and technology, and generating significant investment opportunities. Were committed to catalysing this growth and ensuring a strong pipeline of highly skilled professionals into the sector.

The UK Space Agency is investing 15 million through our Inspiration Programme to deliver education, skills, and outreach interventions over the next two years as part of its commitment to delivering a skilled, diverse, and sustainable space sector workforce now and in the future.

The valuable information from this report strengthens this work by helping us build a clear picture of the skills landscape across the board, so we know where to focus our support.

The survey found that, while more larger organisations report experiencing skills gaps than smaller companies (65% and 52%, respectively), this is lower compared to their equivalent organisations across all other business sectors (86%).

However, while the need for AI and machine learning, as well as data analysis, has risen over the last three years, the demand for software and radio frequency engineering experts has decreased.

This is due to both successful recruitment and upskilling within organisations 72% have provided training in the last year combined with changing priorities.

Following the rapid advances of AI tools such as ChatGPT, space sector leaders anticipate a shift in skills needs over the next three years, with even higher demand for software and data specialists predicted by almost 41% of organisations.

When asked about the future, half of respondents expect their space skills needs to change over the next three years, with 70% expecting continued need for AI and machine learning skills, followed by 58% predicting a need for stronger strategy and leadership skills.

In some areas, leaders anticipate a higher demand for certain skills than they are currently experiencing. For instance, nearly a third (30%) foresee a need for stronger cyber security expertise in their workforce in the next three years, compared to the 15% feeling this gap now.

The study shows that skills gaps are linked to challenges in recruiting, with three quarters (76%) saying they struggle to recruit staff with necessary skills.

Most said that competition from other sectors is the biggest challenge (68%), followed by competition from other space companies (45%).

Retention issues have increased from 52% of large and medium organisations who reported this in 2020 compared to 61% (large) and 71% (medium) reporting this year. This is mostly due to staff poaching (57%) and lower pay levels compared to some other sectors (48%).

Most large space organisations (87%) provide training to help upskill their workforce and are increasing the number of apprenticeships on offer (30% this year compared to 20% in 2020).

Looking across the board of organisation size, almost three quarters (72%) provide training, compared with the average 48% rate across all sectors.

Provisions mostly take the form of on-the-job formal (92%) and informal (84%) learning, with 54% offering external training and 30% offering sponsorship for further study (apprenticeships or degrees).

The UK Space Agency is supporting the growth of the national space workforce by committing 15 million across programmes designed to inspire young people from all backgrounds to pursue STEM careers, empower teachers to include engaging space learning experiences in the classroom, and help space sector employers open pathways for more people taking their first steps into the industry.

Read the original here:
UK space sector has sights set on artificial intelligence and machine ... - GOV.UK

Meeranda, the Human-Like AI, Welcomes Recognized Machine … – PR Newswire

TORONTO, Sept. 14, 2023 /PRNewswire/ -- Meeranda, a privately held Artificial Intelligence (AI) solutions provider, serving both Small and Medium Businesses (SMBs) and Global Multinational Corporations (MNCs), announced today that Francesca Lazzeri, Ph.D., has joined Meeranda's Advisory Board.

Dr. Lazzeri's expertise lies in the field of applied machine learning and AI. She has more than 15 years of experience in academic research, applied machine learning, AI innovation, and engineering team management.

Currently serving as the Senior Director of Data Science and AI, Cloud and AI at Microsoft, Dr. Lazzeri leads a team of skilled data and machine learning scientists. She spearheads the development of intelligent applications on the Cloud, leveraging a wide range of data and techniques including generative AI, time series forecasting, experimentation, causal inference, computer vision, natural language processing, and reinforcement learning.

"We are honored that Dr. Lazzeri has accepted to join Meeranda's Advisory Board,"said Mr. Raji Wahidy, Co-Founder and CEO of Meeranda. "Dr. Lazzeri's contributions to the advancement of machine learning and AI technology are immense, quite well-known, and respected amongst her peers within this sector. Her addition is further validation that what we are embarking on at Meeranda is quite disruptive. We are excited and looking forward to leveraging Dr. Lazzeri's experience and expertise as we work towards delivering The New Personalized Customer Experience we promise to SMBs and Global MNCs."

Academically, Dr. Lazzeri is an Adjunct Professor at New York's Columbia University, teaching Python for machine learning and AI students. She has also contributed to the literature world by authoring several books including "Machine Learning Governance for Managers", "Impact of Artificial Intelligence in Business and Society", and "Machine Learning for Time Series Forecasting with Python."

"We are thrilled to welcome Dr. Lazzeri to Meeranda," said Mr. Jayson Ng, Co-Founder and Chief Research Officer of Meeranda. "Dr. Lazzeri's expertise will be instrumental in bridging the gap between cutting-edge research and real-world applications, thus pushing the technological boundaries and helping us take our product to new heights."

Dr. Lazzeri currently serves as an Advisor on the Advisory Board of the European Union for the AI-CUBE project and as a member of the Women in Data Science (WiDS) initiative. She is also known for having advised, mentored, and coached data scientists and machine learning engineers at the Massachusetts Institute of Technology (MIT) University. She was also a research fellow at Harvard University.

"I am very excited to join Meeranda's Advisory Board,"said Dr. Francesca Lazzeri, Senior Director of Data Science and AI, Cloud and AI at Microsoft. "Meeranda's unique and innovative approach at tackling a very pressing problem is quite disruptive. I strongly believe in their vision, mission, and the leadership team behind Meeranda. I look forward to further contributing to Meeranda's imminent success."

Dr. Lazzeri holds a Master's Degree in Economics and Institutional Studies from Luiss Guido Carli University, a Doctor of Philosophy (Ph.D.) in Economics and Technology Innovation from Scuola Superiore Sant'Anna, and a Postdoc Research Fellowship in Economics from Harvard University.

About Meeranda

Meerandais a privately held Artificial Intelligence (AI) solutions provider, serving Small and Medium Businesses (SMBs) and Global Multinational Corporations (MNCs). Meeranda is best known for its Real-Time Human-Like AI that intends to offer the new personalized customer experience to combat the ongoing frustration of dealing with chatbots and half-baked AI solutions. Although in its early stages, Meeranda already has agreements across six countries and seven industries, thus far.

Follow Meeranda

Website:https://meeranda.comMedia Kit:https://meeranda.com/media-kit/X:https://x.com/HelloMeerandaFacebook: https://www.facebook.com/HelloMeeranda/LinkedIn:https://www.linkedin.com/company/HelloMeeranda Instagram:https://instagram.com/HelloMeerandaThreads:https://instagram.com/HelloMeerandaYouTube:https://www.youtube.com/@meerandaTikTok:https://www.tiktok.com/@meeranda_ai

SOURCE Meeranda Inc.

Read the original post:
Meeranda, the Human-Like AI, Welcomes Recognized Machine ... - PR Newswire

An Introduction To Diffusion Models For Machine Learning: What … – Dataconomy

Diffusion models owe their inspiration to the natural phenomenon of diffusion, where particles disperse from concentrated areas to less concentrated ones. In the context of artificial intelligence, diffusion models leverage this idea to generate new data samples that resemble existing data. By iteratively applying a noise schedule to a fixed initial condition, diffusion models can generate diverse outputs that capture the underlying distribution of the training data.

The power of diffusion models lies in their ability to harness the natural process of diffusion to revolutionize various aspects of artificial intelligence. In image generation, diffusion models can produce high-quality images that are virtually indistinguishable from real-world examples. In text generation, diffusion models can create coherent and contextually relevant text that is often used in applications such as chatbots and language translation.

Diffusion models have other advantages that make them an attractive choice for many applications. For example, they are relatively easy to train and require minimal computational resources compared to other types of deep learning models. Moreover, diffusion models are highly flexible and can be easily adapted to different problem domains by modifying the architecture or the loss function. As a result, diffusion models have become a popular tool in many fields of artificial intelligence, including computer vision, natural language processing, and audio synthesis.

Diffusion models take their inspiration from the concept of diffusion itself. Diffusion is a natural phenomenon in physics and chemistry, where particles or substances spread out from areas of high concentration to areas of low concentration over time. In the context of machine learning and artificial intelligence, diffusion models draw upon this concept to model and generate data, such as images and text.

These models simulate the gradual spread of information or features across data points, effectively blending and transforming them in a way that produces new, coherent samples. This inspiration from diffusion allows diffusion models to generate high-quality data samples with applications in image generation, text generation, and more.

The concept of diffusion and its application in machine learning has gained popularity due to its ability to generate realistic and diverse data samples, making them valuable tools in various AI applications.

There are four different types of diffusion models:

GANs consist of two neural networks: a generator network that generates new data samples, and a discriminator network that evaluates the generated samples and tells the generator whether they are realistic or not.

The generator and discriminator are trained simultaneously, with the goal of improving the generators ability to produce realistic samples while the discriminator becomes better at distinguishing between real and fake samples.

VAEs are a type of generative model that uses a probabilistic approach to learn a compressed representation of the input data. They consist of an encoder network that maps the input data to a latent space, and a decoder network that maps the latent space back to the input space.

During training, the VAE learns to reconstruct the input data and generate new samples by sampling from the latent space.

Normalizing flows are a type of generative model that transforms the input data into a simple probability distribution, such as a Gaussian distribution, using a series of invertible transformations. The transformed data is then sampled to generate new data.

Normalizing flows have been used for image generation, music synthesis, and density estimation.

Autoregressive models generate new data by predicting the next value in a sequence, given the previous values. These models are typically used for time-series data, such as stock prices, weather forecasts, and language generation.

Diffusion models are based on the idea of iteratively refining a random noise vector until it matches the distribution of the training data. The diffusion process involves a series of transformations that progressively modify the noise vector, such that the final output is a realistic sample from the target distribution.

The basic architecture of a diffusion model consists of a sequence of layers, each of which applies a nonlinear transformation to the input noise vector. Each layer has a set of learnable parameters that determine the nature of the transformation applied.

The symbiotic dance of technology and art

The output of each layer is passed through a nonlinear activation function, such as sigmoid or tanh, to introduce non-linearity in the model. The number of layers in the model determines the complexity of the generated samples, with more layers resulting in more detailed and realistic outputs.

To train a diffusion model, we first need to define a loss function that measures the dissimilarity between the generated samples and the target data distribution. Common choices for the loss function include mean squared error (MSE), binary cross-entropy, and log-likelihood. Next, we optimize the model parameters by minimizing the loss function using an optimization algorithm, such as stochastic gradient descent (SGD) or Adam. During training, the model generates samples by iteratively applying the diffusion process to a random noise vector, and the loss function calculates the difference between the generated sample and the target data distribution.

One advantage of diffusion models is their ability to generate diverse and coherent samples. Unlike other generative models, such as Generative Adversarial Networks (GANs), diffusion models do not suffer from mode collapse, where the generator produces limited variations of the same output. Additionally, diffusion models can be trained on complex distributions, such as multimodal or non-Gaussian distributions, which are challenging to model using traditional machine learning techniques.

Diffusion models have numerous applications in computer vision, natural language processing, and audio synthesis. For example, they can be used to generate realistic images of objects, faces, and scenes, or to create new sentences and paragraphs that are similar in style and structure to a given text corpus. In audio synthesis, diffusion models can be employed to generate realistic sounds, such as speech, music, and environmental noises.

There have been many advancements in diffusion models in recent years, and several popular diffusion models have gained attention in 2023. One of the most notable ones is Denoising Diffusion Models (DDM), which has gained significant attention due to its ability to generate high-quality images with fewer parameters compared to other models. DDM uses a denoising process to remove noise from the input image, resulting in a more accurate and detailed output.

Another notable diffusion model is Diffusion-based Generative Adversarial Networks (DGAN). This model combines the strengths of diffusion models and Generative Adversarial Networks (GANs). DGAN uses a diffusion process to generate new samples, which are then used to train a GAN. This approach allows for more diverse and coherent samples compared to traditional GANs.

Probabilistic Diffusion-based Generative Models (PDGM) is another type of generative model that combines the strengths of diffusion models and Gaussian processes. PDGM uses a probabilistic diffusion process to generate new samples, which are then used to estimate the underlying distribution of the data. This approach allows for more flexible modeling of complex distributions.

Non-local Diffusion Models (NLDM) incorporate non-local information into the generation process. NLDM uses a non-local similarity measure to capture long-range dependencies in the data, resulting in more realistic and detailed outputs.

Hierarchical Diffusion Models (HDM) incorporate hierarchical structures into the generation process. HDM uses a hierarchy of diffusion processes to generate new samples at multiple scales, resulting in more detailed and coherent outputs.

Diffusion-based Variational Autoencoders (DVAE) are a type of variational autoencoder that uses a diffusion process to model the latent space of the data. DVAE learns a probabilistic representation of the data, which can be used for tasks such as image generation, data imputation, and semi-supervised learning.

Two other notable diffusion models are Diffusion-based Text Generation (DTG) and Diffusion-based Image Synthesis (DIS).

DTG uses a diffusion process to generate new sentences or paragraphs, modeling the probability distribution over the words in a sentence and allowing for the generation of coherent and diverse texts.

DIS uses a diffusion process to generate new images, modeling the probability distribution over the pixels in an image and allowing for the generation of realistic and diverse images.

Diffusion models are a powerful tool in artificial intelligence that can be used for various applications such as image and text generation. To utilize these models effectively, you may follow this workflow:

Gather and preprocess your dataset to ensure it aligns with the problem you want to solve.

This step is crucial because the quality and relevance of your training data will directly impact the performance of your diffusion model.

Keep in mind when preparing your dataset:

Choose an appropriate diffusion model architecture based on your problem.

There are several types of diffusion models available, including VAEs (Variational Autoencoders), Denoising Diffusion Models, and Energy-Based Models. Each type has its strengths and weaknesses, so its essential to choose the one that best fits your specific use case.

Here are some factors to consider when selecting a diffusion model architecture:

Train the diffusion model on your dataset by optimizing model parameters to capture the underlying data distribution.

Training a diffusion model involves iteratively updating the model parameters to minimize the difference between the generated samples and the real data.

Keep in mind that:

Once your model is trained, use it to generate new data samples that resemble your training data.

The generation process typically involves iteratively applying the diffusion process to a noise tensor.

Remember when generating new samples:

Depending on your application, you may need to fine-tune the generated samples to meet specific criteria or constraints.

Fine-tuning involves adjusting the generated samples to better fit your desired output or constraints. This can include cropping, rotating, or applying further transformations to the generated images.

Dont forget:

Evaluate the quality of generated samples using appropriate metrics. If necessary, fine-tune your model or training process.

Evaluating the quality of generated samples is crucial to ensure they meet your desired standards. Common evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and human perception scores.

Here are some factors to consider when evaluating your generated samples:

Integrate your diffusion model into your application or pipeline for real-world use.

Once youve trained and evaluated your diffusion model, its time to deploy it in your preferred environment.

When deploying your diffusion model:

Diffusion models hold the key to unlocking a wealth of possibilities in the realm of artificial intelligence. These powerful tools go beyond mere functionality and represent the fusion of science and art, as data metamorphoses into novel, varied, and coherent forms. By harnessing the natural process of diffusion, these models empower us to create previously unimaginable outputs, limited only by our imagination and creativity.

Featured image credit: svstudioart/Freepik.

See more here:
An Introduction To Diffusion Models For Machine Learning: What ... - Dataconomy