Incorporating AI in Lean manufacturing

We all know that the field of Artificial Intelligence is intruding into all areas and avenues of human activity. Even though Lean Manufacturing will not change with the introduction of AI, the field of Lean Manufacturing will definitely get influenced by AI in a big way with the help of automation of repetitive tasks, usage of sensors, data analytic models, optimisation and scheduling models to name a few. AI can be incorporated in Lean Manufacturing in many more ways that we can even comprehend now. Some of them are given here. 

AI can be a powerful tool in lean manufacturing, helping to identify and eliminate waste, optimize processes, and drive continuous improvement.

1. Predictive maintenance: AI can be used to analyze machine data and predict when maintenance is needed, reducing downtime and improving equipment reliability.
2. Quality control: AI-powered cameras and sensors can be used to monitor production processes and identify defects in real-time, allowing for quick correction and improved quality.
3. Supply chain optimization: AI algorithms can be used to analyze data from the supply chain and identify bottlenecks, helping to optimize the flow of materials and reduce waste.
4. Inventory management: AI can be used to predict demand and optimize inventory levels, reducing the need for safety stock and improving responsiveness to changes in demand.
5. Process optimization: AI can be used to analyze production data and identify opportunities for process improvement, helping to streamline operations and reduce waste. AI can be used to analyze data from manufacturing processes and identify opportunities for improvement, such as reducing cycle times or eliminating unnecessary steps.
6. Workplace organisation - While 5S is a lean principle, AI based sensors can help monitor and sustain 5S incentives to be implemented across the shopfloor and offices
7. Identifying and removing waste : AI can with the help of monitors help identify areas where waste happens like in transport, inventory, methods, waiting, over production, over processing, generation of defects and under utlisation of human resources and help formulate ways, means and methods by which these wastes can be reduced
8. Predictive analytics: AI can be used to analyze data from across the organization and provide forecasts and recommendations to help drive continuous improvement.
9. Demand Forcasting - AI can be used to look at past data and predict the future needs
10. Automation - AI can tell the areas where operations are repetitive and can be automated to a greater extent than areas where operations may be innovative and creative
11. Workforce Optimization: AI can be used to analyze data from the workforce and identify opportunities to improve efficiency, such as by reducing non-value-added activities or improving training programs.
12. Production scheduling: Use AI to analyze data on production capacity, machine availability, and product demand to create more efficient production schedules.

Eventhough the changes and improvements are small and incremental, most of the people overlook it. Some common challenges of implementing Kaizen include:

1. Resistance to change: Employees may resist the implementation of new processes and ideas.

2. Lack of management support: Without support from upper management, it can be difficult to implement change on a large scale.

3. Limited resources: Kaizen requires resources such as time, money, and personnel to be effective.

4. Difficulty measuring success: Measuring the success of continuous improvement can be difficult, making it hard to determine if the Kaizen process is working.

5. Maintaining momentum: It can be difficult to sustain the momentum of continuous improvement over time.

6. Lack of employee involvement: Without employee involvement, it can be difficult to generate new ideas and implement change.

7. Cultural differences: Kaizen originated in Japan and may not be well-received in other cultures with different work traditions.

Kaizen is a fundamental philosophy that looks at improvement in all spheres of activity around us. 

George 

In the age of AI, is there a future for Lean Manufacturing ?

 I asked this question to my students and also put it to chatGPT. I was amazed by the responses I got from the class and the depth of the answers. 

Lean manufacturing is a methodology that aims to minimize waste and increase efficiency in the production process. It was first developed in the 1950s by Taiichi Ohno, an engineer at Toyota, and has since been adopted by manufacturers around the world. The future of lean manufacturing looks promising, as companies continue to seek ways to improve efficiency and reduce costs.

One of the main trends in the future of lean manufacturing is the increasing use of technology. Advancements in areas such as artificial intelligence, the internet of things, and big data analytics are making it possible for manufacturers to collect and analyze large amounts of data in real-time. This allows them to identify bottlenecks and other inefficiencies in their production processes and make adjustments to improve flow and reduce lead time.

Another trend in the future of lean manufacturing is the growing emphasis on sustainability. As companies become more aware of the environmental impact of their operations, they are looking for ways to reduce waste and minimize their carbon footprint. Lean manufacturing principles such as JIT and Kanban can be used to reduce inventory and minimize the use of resources, which can help companies become more sustainable.

The future of lean manufacturing also involves the integration of virtual and augmented reality technologies. This will make it possible for manufacturers to simulate their production processes and identify areas for improvement before implementing changes in the real world. This technology will also enable remote collaboration and training of employees, which will be an important aspect of manufacturing in the future.

Another trend that will play a big role in the future of lean manufacturing is the increasing use of robots and automation. The use of robots and automation will help manufacturers to increase efficiency and reduce labor costs. However, it also raises concerns around job displacement and the need for retraining.

Another trend that is likely to shape the future of lean manufacturing is the growing importance of data and analytics. As manufacturers collect more and more data about their operations, they will be able to use this data to identify areas of waste and inefficiency, and to make more informed decisions about how to improve their processes. This data can come from sensors, connected devices, and other sources of IoT (Internet of things) data.

The future of lean manufacturing will also involve the greater use of digital technologies, such as 3D printing, virtual reality, and cloud computing. These technologies can help manufacturers to create new products more quickly and at lower cost, and to improve the efficiency of their operations. Additionally, digital technologies can help manufacturers to improve collaboration and communication among employees, which is a key part of lean manufacturing.

The future of lean manufacturing will involve new technologies and changes in business practices, but the core principles of lean manufacturing will remain the same. The goal of eliminating waste and increasing efficiency will continue to be important, and manufacturers will need to adapt to new technologies and trends in order to achieve these goals. Additionally, manufacturers will need to focus more on sustainability and data analytics in order to remain competitive in the future.

Though AI will continue to influence lean manufacturing in the many different ways mentioned above, AI impact on motivating people and systems to give better and better output will be less. This impact will be maximum when industry and business leadership come in front and lead the organisation. Data analytics can be helpful in a big way, but equally helpful will be the congitive understanding and expression of human motivation and performance and pushing it to perform even better, achieving better and far-reaching goals and objectives. 

In conclusion, the future of lean manufacturing looks bright as companies continue to seek ways to improve efficiency, reduce costs, and become more sustainable. The increasing use of technology, emphasis on sustainability, integration of virtual and augmented reality and automation will play a key role in shaping the future of lean manufacturing. However, it will also bring challenges such as job displacement, and the need for retraining of employees. Companies that are able to adapt and implement these changes will be well-positioned to succeed in the future.

George  .. (prepared with the help of ChatGPT)

Challenges of incorporating AI in Operations Management

We look at Operations Management as an area that focuses on the operations of any industry, physical manufacturing,  service sector, IT related, retail, batch processes and so on. It has always been a challenge ma aging Operations in any industry. But when there is a disruption like Artificial intelligence in your work area, how can it impact the workplace, worker morale etc is an interesting area to study.

Challenges of incorporating AI in Operations Management are given here

• Cost: Implementing AI systems can be expensive, and there may be ongoing costs for maintenance, updates, and training.
• Data quality: In order for AI systems to be effective, they need access to high-quality data. Ensuring that data is accurate, complete, and up-to-date can be a challenge.
• Data privacy: Companies may be hesitant to share sensitive data with AI systems due to concerns about data privacy and security.
• Ethical concerns: There may be ethical concerns around the use of AI in operations management, such as the potential for job displacement or the potential for biased decision-making.
• Integration: Integrating AI systems into existing operations can be complex, and may require changes to processes, training for employees, and other resources.
• Algorithm bias: AI systems can be biased if the data they are trained on is not representative of the entire population. This can lead to unfair or inaccurate predictions and decisions. Careful attention to data selection and pre-processing can help reduce bias in AI systems.
• Lack of human understanding: AI systems can process vast amounts of data and identify patterns that humans might miss. However, this complexity might be hard to understand and explain, which makes it challenging for the operations team to make sense of it.
• Implementation and maintenance: Incorporating AI into operations management requires a significant investment of time and resources, including training, system development, and infrastructure. It can also require specialized expertise that some companies may not have in-house.

In brief, there are many ways in which OM can benefit from AI. In short while AI can improve the eficiency and effectiveness of operations, it can result in job loss and loss of satisfaction among employees. (with inputs from ChatGPT) 

Role of AI in the future..

A very legitimate question everyone asks these days , what is the role of AI in the future ? Is it going to be beneficial to mankind or detrimental ? A big thanks to ChatGPT .

I did a search and found areas where it could impact humanity and different sectors.

1. Automation: AI has the potential to automate many tasks that are currently performed by humans, which could lead to increased efficiency and productivity.

2. Decision making: AI can be used to analyze large amounts of data and make decisions based on that data, which could help organizations to make more informed and effective decisions.

3. Predictive analytics: AI can be used to forecast the outcomes of future events or the results of future experiments, which could help organizations to make more accurate predictions and plan for the future.

4. Personalization: AI can be used to create personalized experiences for users, such as personalized recommendations or customized products and services.

5. Healthcare: AI has the potential to transform healthcare by improving diagnosis, treatment, and patient care.

6. Education: AI can be used to personalize learning and improve the effectiveness of education.
7. Transportation: AI is expected to play a key role in the development of autonomous vehicles and other transportation systems.
8. Finance: AI can be used to analyze financial data and make better investment decisions.
9. Augmentation: AI can be used to enhance human capabilities, such as by providing real-time assistance or support in decision-making.

10. Medicine: AI has the potential to revolutionize healthcare by helping to diagnose diseases, predict patient outcomes, and personalize treatment plans.

The Government takes up a lot of our resources as tax money and there are a lot of sharks in terms of politicians and contractors who are out there trying to scam the system. AI can be a real help in such cases.

1. Improved decision making: AI can help governments to analyze large volumes of data and provide insights and recommendations that can inform policy decisions.
2, Increased efficiency: AI can help governments to automate tasks and processes, leading to increased efficiency and productivity.
3. Enhanced public services: AI can be used to improve the delivery of public services, such as healthcare and education, by providing personalized recommendations and support.
4. Predictive modeling: AI can be used to forecast future events and outcomes, helping governments to anticipate and plan for potential challenges or opportunities.
5. Improved security: AI can be used to enhance security by analyzing data and identifying potential threats, such as cyber attacks or fraud.
6. Process automation: AI can be used to automate certain tasks and processes, which could improve efficiency and reduce the workload of governmental employees. For example, AI could be used to automate the processing of applications or to analyze data to identify trends and patterns.
7. Customer service: AI can be used to improve customer service by providing fast and accurate responses to queries and requests. For example, AI could be used to provide instant responses to questions about government services or to help people navigate complex bureaucracy.
8. Service delivery: AI can be used to automate certain tasks and processes related to service delivery, such as processing applications, answering questions, and providing information to citizens.
9. Predictive modeling: AI can be used to forecast and predict future events and outcomes, which can help governments to anticipate and plan for potential issues or challenges.
10. Fraud detection: AI can be used to detect and prevent fraudulent activity, such as false claims or misuse of resources.
11. Resource allocation: AI can be used to optimize the allocation of resources, such as budgets, personnel, and infrastructure, to ensure that they are used efficiently and effectively.

12. Citizen services: AI can be used to improve the delivery of citizen services, such as by automating the processing of government applications and providing personalized assistance to citizens.

13. Efficiency: AI can be used to automate a variety of tasks, such as data entry and analysis, which can improve the efficiency of government operations.
14. Personalization: AI can be used to provide personalized services to citizens, such as personalized healthcare recommendations or personalized education resources.
15. Public safety: AI can be used to support public safety efforts, such as by analyzing crime data to identify patterns and hotspots, or by helping to identify potential threats to public safety.

The role of AI appears very bright and promising considering the different fields and sectors where it can influence public opinion and bring about positive change too.

George. 

Role of AI in higher education

Higher education has had lot of challenges over the years the more important being that the way higher education has been delivered over the past two centuries. 

Benjamin Franklin had once said that "an investment in education is the best investment ever possible"

While educators have been thinking deeply of how to reduce the costs for education, improving the delivery, whether one to one peer education or one to many classroom style, the costs can be very prohibitive. It can play a role in the modern day as the old one-to-many model, the challenges have been many, especially with the onset of numerous technology tools like e-learning and so on. The advent of AI chatbot, ChatGPT it is claimed will change the face of education and teaching like never before.

There are several ways that AI can help improve higher education:

1. Personalized learning: AI can help tailor course content and recommendations to individual students based on their strengths, weaknesses, and interests, providing a more personalized and effective learning experience.
2. Grading and feedback: AI can help grade assignments and provide feedback to students, freeing up teachers to focus on more high-level tasks such as providing one-on-one support to students and designing new courses.
3. Improving efficiency: AI can help streamline administrative tasks, such as managing course schedules and enrolments, allowing educators to focus on more high-impact activities.
4. Enhancing research: AI can help researchers analyze large datasets and identify patterns and trends, leading to new insights and discoveries.
5. Improving accessibility: AI can help make education more accessible to students with disabilities, by providing tools such as text-to-speech and translation capabilities.
6. Course design: AI can be used to design and optimize courses, including selecting relevant materials and creating personalized study plans for students.
7. Tutoring and support: AI can be used to provide 24/7 support for students, including answering questions and offering guidance.
8. Adaptive testing: AI can create personalized tests for students, adjusting the difficulty of questions based on their responses.
9. Automated grading: AI can grade assignments and exams, providing instant feedback to students and freeing up teachers' time for other tasks.
10. Virtual tutoring: AI can provide one-on-one tutoring to students through virtual assistants or chatbots.
11. Content creation: AI can be used to create personalized learning materials, such as customized lesson plans or learning modules.
12. Plan and deliver effective quizzes / feedback systems : students are more committed and engaged if they get to know their progress in almost real-time. AI systems can help in that.
13. Predictive analysis: AI can be used to analyze student data and predict academic performance, which can help educators identify students who may be at risk of falling behind and intervene early
14. Create personalised learning experiences that could remove the challenge of disengagement that is happening with some of the students. (click here for HBR 2019 article)
15. Making education affordable : Many aspiring students have to discontinue higher studies due to the exhorbitant costs. AI can democratise and make economical higher education for the masses.
16. Handle student queries more effectively and personally helping them make decisions to join Universities quicker and efficient
17. Engagement and commitment : While in the University chatbots for academics and other extra curricular activities can enable students to be more enagaged and committed to their career 

Overall, AI has the potential to personalise and revolutionize the way we think about and deliver education, making it more personalized, efficient, and effective.

For students sharing of personal data with different sites on the Internet can have a great liability into the future and can disincentivise students from sharing their data. Sites like mydata.org can help students to discern with which sites they can share or not. 

Recently, the AI based chatbot at the University of Murcia in Spain has been able to handle almost 39,000 queries from prospective admission seekers of the facilities and teaching learning experience for the students with almost 91% correctness.

While we may be aware of the benefits of AI in higher education in the modern context, there are challenges too that need to be handled with great caution. 

• high cost, 
• compromising privacy (fear of losing control over personal data), 
• lack of understanding for some, 
• fear of bias, 
• fear of unemployment due to automation, 
• issues with the seamless integration with existing educational practices and systems are challenges that  higher education systems across the world can face.

George.. (A big thanks to OpenAI, some of the inputs of this blog have been adapted from ChatGPT)

Role of Free and Open Source software in Artificial Intelligence field

Free and Open Source Software (FOSS), as the name implies, tells us that the source code of the software is open and anyone can see how it works and with specific knowledge can work to get it rid of virus or privacy violations. Offering free access, it means that anyone can work on the software to whatever depth they want, offerin g the whole software back to the community for public use and benefit.

In the world of Microsoft offerings, the Free and Open Sourec Software ofered by the Gnu/Linux group and Linux Foundation was a source of inspiration to kickstart the Internet revolution. Open Source software on the other hand, only the source code is open, the rights to edit the source code is with the organisation releasing the open source software, eg. Google and its Android OS.   

ChatGPT has this to say, 

Open-source software is software that is available to the public for use and modification. It is typically developed by a community of volunteers, who work together to improve the software and share their modifications with others.

One of the key features of open-source software is that the source code is made available to the public. This allows anyone to view, modify, and distribute the source code, as long as they follow the terms of the open-source license that the software is released under. This allows for a high level of collaboration and innovation, as developers can build upon and improve upon the work of others.

There are many advantages to using open-source software.  

• It can be freely used and modified by anyone, which can lead to a diverse range of applications and a large and active community of users and developers.  
• It can also be more secure and reliable, as the source code is available for anyone to review and identify potential vulnerabilities.  

Many of the most popular and widely-used software tools and platforms are open source, including the Linux operating system, the Apache web server, and the TensorFlow machine learning library.

Because of the  free use of the software libraries in AI, the growth of the software system instead of being linear is exponential. The field of Internet also saw the fast growth thanks to the use of Free and Open Source software like Apache webserver, the kernel for the Operating system put by Linus Torvalds etc. the development in the Internet area was exponential. Thanks to Google, Android was a free software based on Linux given free to the world. Almost 100% of the Fortune 500 companies work with Linux / FOSS as their backend.

Tensor Flow, the basic software library for Machine Learning and Artificial Intelligence was released as Free and Open Source by Google in 2015, meaning anyone from around the world can work on it and improvise it for the benefit of mankind.

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.

TensorFlow was developed by the Google Brain team for internal Google use, but it was released as an open-source library in 2015. It has since become one of the most widely-used machine learning libraries in the world, with a large and active community of users, contributors, and developers.

TensorFlow is designed to be flexible and efficient, with a focus on running machine learning models on a variety of platforms, from desktop and server environments to mobile devices. It can be used for a wide range of applications, including image and speech recognition, natural language processing, and predictive modeling.

Thanks to the Free and Open Source Community, the field of Artificial Intelligence is also bound to grow exponentiall in the coming years.

George. 

Steps in Kaizen Implementation

Kaizen is a Japanese term that means "continuous improvement." It is a philosophy that focuses on small, incremental changes in processes and systems in order to improve efficiency, quality, and customer satisfaction. Kaizen has been found to be very helpful in Toyota and a lot of other industries innhelping them to come with improved systems and processes in place.

Whilw repetitive manufacturing and process industry is found to be the most appropriate for lean implemetations, more often than not we find design and service industries also benefitting from lean because of the workplace organisation and value delivery processes. Amazon thus stands a big way in benefiting from lean implementation. 

Here are the steps involved in implementing kaizen in an organization:

1. Define the problem: The first step in implementing kaizen is to identify a problem or opportunity for improvement. This can be done through the use of tools such as value stream mapping, which helps to identify waste in a process, or through the use of other problem-solving tools such as the 5 Whys or root cause analysis.

2. Identify the root cause: Once the problem has been defined, the next step is to identify the root cause of the problem. This involves understanding the underlying factors that contribute to the problem and identifying the root cause of the issue.

3. Develop a plan: After the root cause has been identified, the next step is to develop a plan to address the problem. This plan should include specific actions that will be taken to eliminate the root cause of the problem, as well as a timeline for implementing those actions.

4. Implement the plan: The next step is to put the plan into action. This involves implementing the actions identified in the plan and making any necessary changes to the process or system.

5. Evaluate the results: Once the plan has been implemented, it is important to evaluate the results to ensure that the problem has been effectively addressed and that the desired improvements have been achieved. This can be done through the use of metrics such as process cycle time, defect rates, and customer satisfaction.

6. Sustain the improvements: To sustain the benefits of kaizen, it is important to establish a culture of continuous improvement in which all employees are encouraged to identify and solve problems on an ongoing basis. This can be achieved through the use of daily improvement activities, such as gemba walks, in which managers go to the gemba (the place where value is created) to observe and identify opportunities for improvement.

Kaizen is to be sustained over a long period of time to ensure the culture is propagated and beneficial for other organisations. Toyota is very open about the lean operations process and it has only benefitted Toyota in improving their systems better day by day. Sharing the learnings from lean implementations and hand holding small organisations in their len journey can help organisations in the long run to be more efficient and effective.  (some points have been taken from ChatGPT in this article)

Different steps in Lean Implementation

Lean manufacturing is a production method that aims to eliminate waste and improve efficiency in the manufacturing process. It was developed by Toyota in the 1950s and has since been adopted by many other companies around the world.

One of the main principles of lean manufacturing is the idea of continuous improvement, or "kaizen." This involves constantly looking for ways to streamline the production process and eliminate waste, such as unnecessary steps, excess inventory, and defects.

To implement lean manufacturing, companies must first identify the value that the customer is seeking and then design the production process to deliver that value as efficiently as possible. This involves breaking down the production process into individual steps and analyzing each one to determine whether it is adding value or creating waste. Any steps that do not add value can then be eliminated or streamlined.

Another key element of lean manufacturing is the use of "just-in-time" production, which involves producing only what is needed, when it is needed. This helps to reduce excess inventory and the costs associated with storing and managing it.

Lean manufacturing also relies on the involvement of employees at all levels of the organization. Employees are encouraged to suggest improvements to the production process and to take an active role in identifying and eliminating waste.

There are many benefits to implementing lean manufacturing, including increased efficiency, reduced costs, and improved quality. It can also lead to increased customer satisfaction and a more positive work environment for employees.

However, implementing lean manufacturing can be a complex and challenging process. It requires a significant investment of time and resources, and it requires a culture of continuous improvement and employee involvement. But for companies that are able to successfully implement lean manufacturing, the benefits can be significant

Different steps

Lean is a method for improving efficiency and effectiveness in business processes by identifying and eliminating waste. There are several steps involved in implementing a lean process:

1. Identify value: The first step in implementing lean is to identify the value that the customer is seeking. This helps to focus improvement efforts on activities that matter most to the customer.

2. Map the value stream: The next step is to map the current value stream, which includes all the activities required to deliver value to the customer. This includes both value-added activities, which directly contribute to the final product or service, and non-value-added activities, which do not add value from the customer's perspective.

3. Identify waste: The next step is to identify the waste in the value stream, which includes activities that do not add value from the customer's perspective. Common types of waste include overproduction, waiting, unnecessary motion, excess inventory, unnecessary processing, defects, and unused talent.

4. Eliminate waste: Once the waste has been identified, the next step is to eliminate it. This can be done through a variety of methods, including process redesign, standardization, and the use of lean tools such as value stream mapping, 5S, and visual management.

5. Create flow: The final step in implementing lean is to create flow in the value stream, which means ensuring that products or services flow smoothly through the process with minimal interruption. This can be achieved through the use of pull systems, where work is only started when it is needed, and the use of single-piece flow, where work is completed one piece at a time rather than in batches.

6. Establish a continuous improvement culture: To sustain the benefits of lean, it is important to establish a culture of continuous improvement in which all employees are encouraged to identify and eliminate waste on an ongoing basis. This can be achieved through the use of kaizen events, in which teams work together to identify and solve problems, and through the use of daily improvement activities such as gemba walks, in which managers go to the gemba (the place where value is created) to observe and identify opportunities for improvement.

With generous inputs from Open AI and Google AI...

AI programming languages

There are many programming languages that are commonly used for artificial intelligence and machine learning projects. Some of the most popular include Python, R, Java, C++, and Lisp.

Python is a high-level, general-purpose programming language that is widely used in the field of artificial intelligence because of its simplicity and flexibility. It has a large and active community of users, and there are many libraries and frameworks available for use in AI projects, such as TensorFlow and scikit-learn.

R is a programming language and software environment for statistical computing and graphics. It is popular among data scientists and researchers for its powerful statistical analysis and visualization capabilities.

Java is a popular, general-purpose programming language that is used in a variety of fields, including artificial intelligence. It is known for its portability, which means that Java programs can run on a variety of devices and platforms without the need for modification.

C++ is a high-performance programming language that is often used in AI projects that require fast processing times. It is a relatively low-level language, which means that it is closer to the machine code that computers understand, making it more efficient but also more difficult to learn and use.

Lisp is a programming language that was specifically designed for artificial intelligence and symbolic computing. It is known for its simplicity and flexibility, and it has been used in many AI projects over the years.

There are also many other programming languages that are used in artificial intelligence and machine learning, including Prolog, Ruby, and Julia, to name a few.

(this input was provided by Chat GPT (Generative Pre-trained Transformer) AI) 3 jan 2023

Text to image generation, Diffusion process in AI ..

Text to image generation by diffusion process in AI is an interesting area of Machine learning which builds models on vast amounts of data collected.

Deep fake AI

In Dream image generating AI app, I used the seeding words 'Image of a boy in a flowery meadow' and this is the result. (bottom left)

As different from deep faking apps, tech two years old, where people's portraits are inserted into existing images and videos, see my example, the latest technology is slightly interesting and advanced.

 

This worked using Generative Adversarial Networks (GAN)  having a generator and a discriminator. While the generator produces synthetic examples from random data, the discriminator component tries to distinguish between synthetic examples and real examples from a training dataset

 

Diffusion model on input Boy in a flowery meadow

AI has gone forward using Diffusion technology where using Contrastive Language Image Pretraining, diffusion system reconstructs data from noise, based on the word prompts. 

 

It is analogous to a master sculptor telling a novice where to chip a marble block to get a beautiful marble sculpture. 

 

On the right is an image generated using diffusion, where I have given the prompt words "George Easaw working in Alliance University Bangalore"

Diffusion model on prompt words of author at AU Bangalore

"At a high level, Diffusion models work by destroying training data by adding noise and then learn to recover the data by reversing this noising process. In Other words, Diffusion models can generate coherent images from noise. Diffusion models train by adding noise to images, which the model then learns how to remove" - scale.com

Diffusion model on prompt words of author at AU, Bangalore

The two high resolution images given to the left and right are generated from two different noise models. It is analogous to saying the two marble sculptures are taken from two different marble blocks excavated from two geographically different locations.

 

Click here for a practical guide to Diffusion Models.

Diffusion is applied these days in bio medicine to understand new treatments and to biochemistry to come out with new DNA sequences and molecules.

Diffusion models are generative models in the sense that they are trained to generate the same data output on which they are trained. How diffusion models work, click here ..

Sky is the limit, Diffusion is being used to compress images, generate videos, synthesise speech etc.

George

Top Global events of 2022 ..

Cheerful news from 2022 ..

The top ten research and application highlights of 2022 are given here. Courtesy Guardian UK. Humanity as a whole found 2022 to be a beneficial one with these top ten highlights -

1. The world reached peak agricultural land, meaning we will not be using any more land for agricultural processes, we will improve the density of cultivation on existing land and cultivate using aquaponics and so on, to save precious land

2. We deployed a malaria vaccine that is 100 % effective

3. US deployed the $10 billion James Webb telescope in space (click here) which can see into the Universe 100 x more effectively and deeper than the Hubble telescope. Our understanding of the Universe has improved, recently using the Webb telescope we discovered a galaxy 13.4 billion light years away

4. Europe for the first time registered an increase in the population of wild animals in recent years

5. Philippine farmers harvest for the first time genetically modified (GM) Golden rice,  (click here) which is having higher levels of Vit A.

6. Guinea worm has been almost eradicated from the planet

7. Serum Institute from India develops first cervical cancer vaccine (click here)

8. Lab grown meat (click here) gets US FDA approval in US.

9. SARS Cov2 vaccine saved 14-20 million lives across the world due to Covid 19 related deaths

10. Human Genome editing tool CRISPR, (click here) registers major success in blood cancer treatment.

A fact we can all be happy about is humanity as a whole is development. Even though we hear and read about lot of negative things happening around, there is cause for cheer as good things too keep happening. 

The world twenty years hence will be a much better place than what we see now, let us hope.

Doing an analysis, I found that 6 of these 10 points were related to the medical/ healthcare field, four were related to the agriculture and food spectrum while just one was related to the Science/technology area, though we know these times are technology times and how Internet and communications technology (ICT) continues to make human lives easier and more productive over the years.

When the world population from the present 7.8 billion touches 10 billion around 2052 AD, will we then be thankful for the year 2022 ?

George.

Going back in time ..

 An interesting statistic

If you were to travel back in time a million seconds, you would be touching 19th December 2022.

If you were to travel back in time a billion seconds, you would be touching 11 April, 1992.

If you were to travel back in time a trillion seconds, you would be touching the middle of 30,000 BC. India as a country was just starting to get terrestrial settlers from Africa and middle east, courageous seafarers on rafts from Africa had settled in South India ten thousand years earlier, around 40,000 BC. They were possibly ancestors to the present day tribals.

All of us came out of Africa, the cradle of human civilisation, one time or the other, during the past 50,000 years .. 

 

Map courtesy - Human Genographic Project, National Geographic Society, US.

George..