Harnessing Machine Learning: Advancements in Tobacco Research and the Internet of Drones

 

Machine Learning (ML) has been a disruptive factor in many industries in recent years. This blog article explores two areas where machine learning (ML) is making great progress: Internet of Drones (IoD) and tobacco research. Despite their apparent differences, these domains are united by the advancement and creativity brought about by cutting-edge data analysis methods.

Machine Learning for Research on Tobacco:

A scoping assessment was carried out by Rui Fu and associates to assess the influence of machine learning on tobacco research. Their thorough analysis, which was published in the journal Tobacco Control, found 74 studies that used machine learning techniques. Four unique domains were identified from these studies:

1. ML-powered smoking cessation technology (n = 22)

2. Content analysis (n=32) of data on tobacco use on social media platforms

3. Classifying smokers using narrative clinical materials

4. Prediction of outcomes related to tobacco use based on administrative, survey, or clinical trial data (n=14)

This review demonstrates the enormous potential of machine learning to advance tobacco control initiatives and influence policy choices.

ML in Quitting Smoking:

Machine learning applications have demonstrated potential in offering tailored interventions in the field of smoking cessation. ML algorithms have the capability to customize tactics to enhance the probability of stopping by examining individual smoking behaviors and aspects that contribute to the success of cessation. These technological advancements not only empower individuals but also improve the overall effectiveness of smoking cessation programs.

Examining Social Media Content: More info

Will Artificial Intelligence Replace Architects?

 

Will architects be replaced in their positions by artificial intelligence? Thomas Lane claims that AI may automate up to 37% of the work that engineers and architects normally do in the May 2023 issue of Building magazine. However, it is likely that mundane and less creative jobs will be the focus of this automation, freeing up professionals to focus on more creative and strategic aspects of their work.

The same is true of AI tools—just as Revit and 3D software did not replace architects, but rather changed their workflows. AI is about to change the landscape of architecture by bringing with it new duties like AI management in addition to current ones.

Early in 2023, the volume of photos produced by AI systems like Midjourney has left manyarchitects thinking about the ramifications. While there's a common fear that artificial intelligence will become omnipotent, architects are curious in AI and actively investigating its potential integration into their work in an effort to understand its potential uses in their industry.

It seems unlikely that AI will soon completely replace architects. The architectural scene is changing quickly, and while new applications will always emerge, our understanding of AI's potential and limitations is increasingly becoming more apparent. A clearer knowledge of how AI might influence and revolutionize our professional activities is being shaped by this growing awareness.

Until AI emerges victorious in a competition for architectural design, we have nothing to fear.

More Info: https://www.archdaily.com/1007802/will-artificial-intelligence-replace-architects

OnPassive Chief Marketing Officer Mohammad Nazzal On The Power Of Artificial Intelligence To Boost Your Business

 

Even though there are still a number of concerns around the application of artificial intelligence (AI), it is evident that those who adopt this technology first stand to gain a competitive advantage over others.

We recently had a conversation about best practices for incorporating AI technology, how it may boost marketing campaigns, and how OnPassive's array of AI products can help companies of all sizes with Mohammad Nazzal, CMO of OnPassive.

Nazzal advises beginning the process of incorporating AI technology into your company by determining a particular use case—such as enhancing customer satisfaction, productivity, or efficiency—where AI may assist your enterprise. Watch the video to hear Nazzal's full message!

Read More: 

Age of AI: Everything you need to know about artificial intelligence

 Understanding the lingo, getting a sense of the key actors, and staying current on AI news

I can be found in what seems to be every aspect of contemporary life, from business and productivity to music and media to relationships. It might be difficult to keep up with everything, so keep reading to learn about anything from the most recent significant advances to the words and businesses you need to be familiar with in order to stay informed in this rapidly evolving industry.

A type of software system based on neural networks, known as artificial intelligence or machine learning, was originally invented decades ago but has only recently gained popularity because tostrong new computer capabilities. Effective voice and picture recognition, as well as the production of artificial speech and graphics, have all been made possible by AI. And researchers are working hard to make it possible for an AI to perform tasks like web browsing, ticket booking, recipe modification, and more.

Oh, but if you're concerned about a rising of the machines a la The Matrix, don't be. Later, we'll talk about that.

Our guide to AI is divided into three main sections that may be read in any order and will each receive frequent updates:

First, the most fundamental ideas you should understand, followed by some more recent yet crucial ideas.

Afterwards, a summary of the key AI actors and why they matter.

Last but not least, a compiled list of current news stories andThere are changes that you need to be aware of.

You will be as up to date as anyone can hope to be in this day and age by the time you finish reading this essay. As we advance into the age of AI, we will also be updating and enhancing it.

AI 101

Read more:

www.dprg.co.in

7 Fun Facts About AI

 

Fun fact about AI

It is commonly accepted that the Dartmouth Conference in 1956, where the phrase "artificial intelligence" was first used in public, marked the beginning of the field of AI, under the leadership of John McCarthy and a team of respected researchers. The phrase did, however, make its debut in a 1955 proposal for a "2 month, 10 man study of artificial intelligence" put forth by John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

2 Fun AI Facts

Soon after AI, the phrase "machine learning" was first used. The IBM Journal published Arthur L. Samuel's article, "Some Studies in Machine Learning Using the Game of Checkers," in July 1959.

 
AI Fun Fact 3

In 1966, ELIZA, the first chatbot with artificial intelligence, debuted. You did read that correctly. Before Amazon's Alexa, ELIZA was first introduced 48 years ago. ELIZA, which was inspired by the literary character Eliza Doolittle, would effectively reformulate the user's input as a question. Therefore, if you informed ELIZA about some weekend activities that you were looking forward to, she would ask, "What about those plans excites you?" Creator of ELIZA Joseph Weizenbaum of MIT warned the public about the risks of allowing AI to play such a significant role in society after witnessing ELIZA in operation.

Fourth AI Fun Fact:

 Harold Cohen's AARON shows how AI can be applied in the arts. Cohen, a pioneer of computer art, graduated from the Slade School of Fine Art at the University of London in 1950 and started his career as a painter. Nearly two decades later, Cohen was seeking his next hobby when he developed an interest in computer science. Then he united his two passions and developed AARON, the name for a collection of computer programs that produce unique creative visuals.

Hugo Caselles-Dupré, Pierre Fautrel, and Gauthier Vernier are members of the collective known as Obvious who used generative adversarial networks (GANs) to generate the portrait of Edmond Belamy, which was put up for auction by renowned auction house Christie's in 2018.

For the next fun:

www.dprg.co.in

What You Need to Know About AI and Data Science in 2023?

 
AI and Data Science are Revolutionizing the world in 2023

AI and data science are two of the most exciting and impactful technology domains 2023. They enable us to extract valuable insights from massive amounts of data, automate complex tasks, and create innovative solutions for various problems. However, they also pose new challenges and opportunities for businesses, society, and individuals. This article will explore some of the key trends, applications, and challenges of AI and data science in 2023.
Trends

AI and data science constantly evolve, with new developments and breakthroughs happening yearly. Here are some of the significant trends that are shaping these fields in 2023:

Data Democratization

Data democratization refers to making data and analytics accessible and understandable to everyone, not just data experts. This enables more people to leverage data-driven insights for decision-making, innovation, and collaboration. Data democratization is facilitated by tools and platforms that simplify data collection, processing, visualization, and sharing. Examples include natural language processing (NLP) tools that can analyze text and speech, augmented analytics tools that can generate insights and recommendations automatically, and cloud-based platforms that can store and manage data securely and efficiently.

Ethical and Responsible AI

Ethical and responsible AI designs and deploys AI systems aligned with human values and principles, such as fairness, transparency, accountability, privacy, and security. This is important because AI systems can significantly impact people’s rights and well-being. Ethical and responsible AI requires a multidisciplinary approach involving stakeholders from different domains, such as developers, users, regulators, ethicists, and society. Examples include frameworks and guidelines for ethical AI development, methods and tools for AI explainability, and mechanisms for AI governance.

AutoML

AutoML refers to the automation of machine learning (ML) processes, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. This can reduce the time, cost, and complexity of building ML models and improve their performance and quality. AutoML can also enable more people to use ML without requiring extensive coding or domain knowledge. Examples include platforms and services that offer end-to-end AutoML solutions, such as Google Cloud AutoML, Microsoft Azure AutoML, or Amazon SageMaker Autopilot.

Applications

AI and data science have various applications across various industries and domains. Here are some of the prominent examples of how they are used in 2023:

Healthcare

AI and data science can help improve healthcare outcomes, efficiency, and accessibility. They can enable better diagnosis, treatment, prevention, and management of diseases, enhance drug discovery and development, optimize healthcare operations, personalize healthcare services, empower patients, and support public health initiatives. Examples include AI-powered medical imaging, wearable devices, chatbots, telemedicine, digital therapeutics, precision medicine, drug discovery platforms, electronic health records, healthcare analytics, epidemic modeling, etc.

Retail

AI and data science can help enhance customer experience, loyalty, and satisfaction, increase sales revenue, reduce operational costs, optimize inventory management, improve product quality, enable omnichannel retailing, create new business models, etc. Examples include recommender systems, sentiment analysis, customer segmentation, price optimization, demand forecasting, fraud detection, product search, image recognition, voice assistants, etc.

Manufacturing

AI and data science can help improve manufacturing productivity, quality, efficiency, and safety. They can enable predictive maintenance, quality control, defect detection, process optimization, supply chain management, energy management, etc. Examples include computer vision, robotics, industrial IoT, digital twins, additive manufacturing, etc.

Education

AI and data science can help enhance learning outcomes, engagement, and accessibility. They can enable personalized learning, adaptive assessment, feedback generation, content creation, tutoring systems, gamification, etc. Examples include intelligent tutoring systems, adaptive learning platforms, educational games, MOOCs, etc.

More info: 

www.dprg.co.in

The state of artificial intelligence: Stanford HAI releases its latest AI Index Report

The Stanford Institute for Human-Centered Artificial Intelligence today released the latest edition of its AI Index Report, which explores the past year’s machine learning developments.

Stanford HAI, as the institute is commonly known, launched in early 2019. It researches new AI methods and also studies the technology’s impact on society. It releases its AI Index Report annually.

The latest edition of the study that was published today includes more than 350 pages. It covers a long list of topics, including the cost of AI training, efforts to mitigate bias in language models and the technology’s impact on public policy. In each area that it surveys, the report points out multiple notable milestones that were reached during the past year.
AI advances and challenges

The most advanced neural networks have become more complicated over the past year. Stanford HAI points to Google LLC’s Minerva large language model as one example. The model, which debuted last June, features 540 billion parameters and took nine times more compute capacity to train than OpenAI LP’s GPT-3.

The growing hardware requirements of AI software are reflected in the rising cost of machine learning projects. Stanford HAI estimates that PaLM, another Google model released last year, cost $8 million to develop. That’s 160 times more than GPT-2, a predecessor to GPT-3 that OpenAI released in 2019.

Though AI models can perform significantly more tasks than a few years ago, they continue to have limitations. Those limitations span several different areas.

In today’s report, Stanford HAI highlighted a 2022 research paper that found advanced language models struggle with some reasoning tasks. Tasks that require planning are often particularly challenging for neural networks. Last year, researchers also identified many cases of AI bias in both large language models and neural networks optimized for image generation.

Researchers’ efforts to address those issues came to the fore in 2022. In today’s report, Stanford HAI highlighted how a new model training technique called instruction tuning has shown promise as a method for mitigating AI bias. Introduced by Google in late 2021, instruction training involves rephrasing AI prompts to make them easier to understand for a neural network.
New use cases

Last year, researchers not only developed more capable AI models but also found new applications for the technology. Some of those applications led to scientific discoveries.

In October 2022, Google’s DeepMind machine learning unit detailed a new AI system called AlphaTensor. DeepMind researchers used the system to develop a more efficient way of carrying out matrix multiplications. A matrix multiplication is a mathematical calculation that machine learning models use extensively in the process of turning data into decisions.

Last year also saw scientists apply AI to support research in a range of other areas, Stanford HAI pointed out. One project demonstrated that AI could be used to discover new antibodies. Another project, also led by Google’s DeepMind, led to the development of a neural network that can control the plasma in a nuclear fusion reactor.
The societal impact of AI

Stanford HAI’s new report also dedicates multiple chapters to the impact of AI on society. Though large language models have only entered the public consciousness in recent months, AI is already making an impact across several areas.

In 2021, only 2% of federal AI-related bills proposed by U.S. lawmakers were passed into law. Last year, that number jumped to 10%. At the state level, meanwhile, 35% of all AI-related bills passed in 2022.

The impact of machine learning is also being felt in the education sector. According to Stanford HAI’s research, 11 countries have officially endorsed and implemented a K-12 AI curriculum as of 2021. Meanwhile, the percentage of new computer science Ph.D. graduates from U.S. universities who specialized in AI nearly doubled between 2010 and 2021, to 19.1%.

www.dprg.co.in

From Artificial Intelligence to Artificial Intuitions: CEO, Affordable Robotic & Automation Ltd

 Artificial intelligence (AI) is rapidly transforming various industries and revo ..

Read more at:

https://infra.economictimes.indiatimes.com/news/construction/from-artificial-intelligence-to-artificial-intuitions-ceo-affordable-robotic-automation-ltd/99212712

www.dprg.co.in

Is AI Ethics Just An Eyewash?

Desperate times call for desperate measures and no other big tech company is feeling the heat more than Meta Platforms Inc. A report published by Wall Street Journal last week revealed the strict new policy it has imposed on some employees asking them to either look for new positions somewhere else within the company or face termination. Meta has announced that it plans to cut costs by 10%. In the earnings released for the previous quarter, Meta’s results looked grim. The company had lost close to 50% of its value by the second-quarter of this year. The company also reported an outlook predicting higher-than-expected losses for the third-quarter.

In a bid to rid itself of all excesses, the axe fell first on the company’s Responsible Innovation Team (RIT). The team was a crucial part of Meta’s efforts to redress the many blows that have been dealt to its reputation in the past few years. The company has had more than its fair share of scandals including Cambridge Analytica—which was recently settled—breeding political extremists and spreading misinformation during the US elections, violation of children’s privacy in Ireland and staking its money on the metaverse.
Turbulent times in Meta

In 2018, a vice president of product design with the company—Margaret Stewart—established the team to tackle the “potential harms to society” caused by Facebook’s products. Ironically, just last year, Stewart posted a blog titled, ‘Why I’m optimistic about Facebook’s Responsible Innovation efforts’, stating that she inherently believed that a lot of good could come from technology and Meta was ready to put in the work for it. “Goodness isn’t inevitable. It comes through sustained hard work, investing time in foresight work early in the development process, surfacing and planning mitigations for potential harms, struggling through complex trade-offs, and all the while engaging with external stakeholders, including members of affected communities, “ Stewart explained.

Despite dissolving the team, Meta has promised that the team which comprised two dozen engineers and ethic specialists will continue with its work albeit in a scattered way. Eric Porterfield, a spokesman with the company, said that employees from the RI team would work in safety and ethical product design with specific issues in teams. He also stated that they weren’t guaranteed new jobs.

For more info : https://analyticsindiamag.com/is-ai-ethics-just-an-eyewash/

www.dprg.co.in

Why Researchers Working on AI Argue It Could Cause ‘Global Disaster’

The new magic pill on the market is amorphous and versatile. The consensus among many researchers is that artificial intelligence’s efficiency will aid everything from healthcare and firefighting to hiring, art, and music. Even environmental catastrophes like the Bengaluru floods could benefit from five nifty A.I. solutions, the prophesied promise goes.



But skepticism surrounds its intent and purpose. What are the perils of A.I., in a world where it promises so much? A new paper written by researchers working on A.I. argues that such pervasive reliance on algorithms and machine learning could cause a global catastrophe on par with a nuclear disaster. The key isn’t that it’s the machines’ fault per se — it’s us. Whom we appoint to create and control them, and what they, in turn, instruct, can have devastating consequences for us all. It points to a need for understanding A.I. as a public good, with public consequences — bolstering the need to democratize our engagement with it.

The root of the current bout of anxiety around A.I. can be traced back to a paper authored by a working group of experts for RAND Corporation, an American non-profit. The experts included people working in A.I., government, national security, and business, some of whom concluded that the integration of quicker and smarter A.I. could create a false sense of fear. For instance, the rise of open-sourced data may be inferred to mean that a country’s nuclear capacity is at risk of exposure, which may push the country to take steps. Another scenario is that A.I.’s data may be used to decide where to strike. Overall, A.I. can manufacture a series of events where country A would be in a capacity to target country B, and that “might prompt Country B to re-evaluate the advantages and disadvantages of acquiring more nuclear weapons or even conducting a first strike.” A.I. “could considerably erode a state’s sense of security and jeopardize crisis stability,” the paper argued. If fake news meets A.I., the thinking is, it could lead to a third war.

This is neither a novel nor a unique fear: that A.I. could one day wipe out humanity or cause human extinction is a scenario many have dissected in all its dystopic scenarios. “Scary A.I.” is a sub-genre of its own, with many observing with suspect fascination about the “wild” things A.I. can do, and others preparing to enter the future with them. Pop culture gives plenty of references; The Matrix, The Terminator, and Ultron in Avengers all reflect a reality where A.I. entities cultivate a hatred for humans and are set on a warpath.

Arguably, catastrophe will not come at a machine’s whim. But there is merit to thinking deeply about Scary A.I. as a future and what, and who, may give machines enough power to wipe out an entire civilization. “The problem isn’t that AI will suddenly decide we all need to die,” as scientist Dyllan Mathews noted, “the problem is that we might give it instructions that are vague or incomplete and that lead to the A.I. following our orders in ways we didn’t intend.” Scary A.I. has more to do with us, our wild ambitions and unchecked dreams. This complicates how we look at ethics, transparency, and research within A.I. itself.

The legitimacy of the concern aside, the paper reflects the helplessness of a world where A.I. leads and we follow. But there is a significant context to this. Computer scientist Stuart Russell literally wrote the book on how A.I. could be disastrous for humans. And while he agrees we’ve set ourselves up for failure, he argues that it’s because the “objective” we’ve set for the A.I. are themselves misleading and vague.

For more info : https://theswaddle.com/why-researchers-working-on-ai-argue-it-could-cause-global-disaster/

www.dprg.co.in

Artificial Intelligence: Path-breaking revolution in Indian Healthcare System

In the form of machine learning, AI has the capability for the development of pr ..

Read more at:

https://health.economictimes.indiatimes.com/news/health-it/artificial-intelligence-path-breaking-revolution-in-indian-healthcare-system/93813886 

www.dprg.co.in

Artificial Intelligence Model Can Detect Parkinson’s From Breathing Patterns

Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset.

Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson’s just from reading a person’s breathing patterns.The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson’s from their nocturnal breathing—i.e., breathing patterns that occur while sleeping.

The neural network, which was trained by MIT Ph.D. student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone’s Parkinson’s disease and track

the progression of their disease over time.

Yang and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine. Katabi, who is also an affiliate of the MIT Computer Science and Artificial Intelligence Laboratory and director of the Center for Wireless Networks and Mobile Computing, is the senior author.

Read more :

www.dprg.co.in

How machine learning model from IIT-Madras team could boost personalised cancer therapy

IIT-Madras study, published in the journal ‘Frontier in Genetics’, dwells on algorithm to identify personalised genes that have the potential to form and drive cancer in individuals.


Researchers at Indian Institute of Technology (IIT)-Madras have developed a machine learning (ML) algorithm to identify personalised genes that have the potential to form and drive cancer in individuals. The model uses a ‘multiomic’ approach, the combined study of intersectional studies that end with the suffix ‘-omics’.

Details of the algorithm were published in a peer-reviewed paper in the journal Frontier in Genetics last month. The findings are expected to help in devising more personalised cancer therapies, contributing to the growing field of targeted therapy and immunotherapy trials.

Called ‘Personalized Identification of driVer OGs and TSGs’, or PIVOT, the model identifies personalised drivers of cancer genes and classifies them as either tumour suppressor genes (TSG) or oncogenes (OG) — the two types of genes involved in cancer.

The algorithm has also identified rarer driver genes that have not been studied enough to be associated with some cancers in large pan-cancer databases.ML works by consuming large datasets and understanding previously identified patterns. It then applies logic to identify new patterns in new (or existing) data.

For this study, the IIT-Madras researchers worked on datasets that contained genomes of individuals with four kinds of cancers — breast cancer, colorectal adenocarcinoma, lower grade glioma (brain tumours), and lung adenocarcinoma — and their identified driver genes and mutations.


The model classified genes as neutral or drivers, and further labelled them into TSGs and OGs.

It also identified other trends in data and newer driver genes from medical literature.

“It is difficult to predict how well we can train and predict genes in unseen cancer types,” said Malvika Sudhakar, lead author of the paper, to ThePrint. “A lot of factors, such as the number of samples, influence the performance of the model.”

Read more :

www.dprg.com

LaMDA: The hype about Google AI being sentient

The article has been authored by Sanur Sharma, an associate fellow, Manohar Parrikar Institute for Defence Studies and Analyses.


Artificial Intelligence (AI) has been considered the key to the future when it comes to imitating the human brain or becoming sentient. Recently, Google’s AI engineer Blake Lemoine going public on Google LaMDA, has sparked a discussion on AI models achieving consciousness. But what is more important here with these sparks is the serious concern regarding AI ethics.

So, what exactly is LaMDA, and why it is called sentient?

LaMDA is Google’s Language Model for Dialogue Applications. It is a chatbot based on the big advanced language model that can ingest trillions of words from the internet to inform its conversation. It is built on a massive corpus of data or text crawled from the internet. It is a statistical abstraction of all the text. So, when this system or model is asked, it takes the text written in the beginning, tries to continue based on the words related to one another, and predicts what words it thinks will come next. So, it is a suggestive model that continues to the text you put in. LaMDA has similar skills to BERT and GPT-3 language models and is built on Transformer, a Neural Network architecture that google research invented in 2017. The model produced through this architecture has been trained to read words, sentences and paragraphs, relate words with one another, and predict words that would come next in the conversation.

So how is it different from other chatbots also designed for conversations? Chatbots are conversational agents meant for specific applications and follow a narrow predefined path. In contrast, according to Google, “LaMDA is a model for dialogue application capable of engaging in free flow conversations about seemingly endless topics”.

The general characterisation of conversations tends to revolve around specific topics, and due to their open-ended nature, the conversation can end up in a completely different domain. According to Google, LaMDA is trained to pick up these several nuances of language that differentiate open-ended conversations from other forms making them more sensible. The Google 2020 research states that “Transformer based Language Model based on Dialogue could learn to talk about virtually anything”. It further stated that LaMDA could be fine-tuned to improve its sensibleness and specificity of the response

Read more : 

www.dprg.com

IIT Madras Develops Artificial Intelligence-based Tool to Predict Cancer-causing Genes

The Indian Institute of Technology (IIT) Madras researchers claims to have developed an artificial intelligence-based tool, called ‘PIVOT’, that can predict cancer-causing genes in an individual. This tool aims to help in devising personalised cancer treatment strategies, the institute said. The researchers added that the tool is based on a machine learning model that classifies genes as tumour suppressor genes, oncogenes or neutral genes.



Explaining the use of newly developed tool to treat cancer, researchers claimed that it is an uncontrolled growth of cells that can occur due to mutations in oncogenes or by tumor suppressor genes or both but not all mutations necessarily result in cancer. Therefore, it is important to identify genes that are causing cancer to devise appropriate personalised cancer treatment strategies.

“PIVOT is designed to predict genes that are responsible for causing cancer in an individual. The prediction is based on a model that utilizes information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression," IIT Madras said.

The IIT Madras researchers said that they have built AI prediction models for three different types of cancer including breast invasive carcinoma, colon adenocarcinoma and lung adenocarcinoma. The team is also working on a list of personalised cancer-causing genes that can help in identifying the suitable drug for patients based on their personalized cancer profile.

The research was led by Prof Raghunathan Rengaswamy, Dean (Global Engagement), IIT Madras, and Professor, Department of Chemical Engineering, IIT Madras, Dr Karthik Raman, associate professor, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras and a core member, Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, and Malvika Sudhakar, a research scholar, IIT Madras.

Highlighting the significance of the research, Dr Karthik Raman said, “Cancer, being a complex disease, cannot be dealt with in a one-treatment-fits-all fashion. As cancer treatment increasingly shifts towards personalised medicine, such models that build toward pinpointing differences between patients can be very useful.” “The research area of precision medicine is still at a nascent stage. PIVOT helps push these boundaries and presents prospects for experimental research based on the genes identified,” Malvika Sudhakar, Research Scholar, IIT Madras said.

More info: 

www.dprg.com

Enough with the AI washing!

In 2010, web developers and designers Dan Tocchini, Brian Axe, Leigh Taylor, and Henri Bergius set out to build perhaps the world’s first AI-based site builder. Just one and a half years later, Facebook offered USD 10-15 million to acquire The Grid– though they had no commercial product. In 2014, the team launched a crowdfunding campaign to raise USD 70,000.

“We’ve spent the last few years building a form of artificial intelligence that functions like your own personal graphic designer, able to think about your brand and present it in the best way possible,” said Dan, CEO and co-founder of The Grid. “The design adapts to your content, not the other way around.”

For a brief moment, all was well. The team was gearing up for a Spring launch in 2015 after raising USD 4.6 million in Series A.

But when the time came, only 100 of the 50,000+ backers from the crowdfunding got access to a Beta product. A year later, the company launched the final product and chaos ensued.

“AI is definitely over-hyped. Don’t buy into it or fall for it. AI is the cure-all tonic of the 21st century. It solves everything or could kill everything. At least that’s what science fiction would lead you to believe. Like the cure-all tonics of the early 20th century, AI won’t solve every problem or come close any time soon,” said Josh Greig, software developer at Next Healthcare Technologies.

“Elon Musk is quite brilliant but overestimates the speed and quality of software Tesla can produce for autonomous driving. Tesla has frequent delays and fatal car accidents related to its Autopilot technology as a result of this wishful thinking and rushed deployment. The hype pushes advertisers to use “AI” whenever anything software-related is used to solve a problem now,” he added.
Hyped much?

As per IDC’s latest reports, the global AI market is expected to cross the USD 500 billion mark in 2023. “AI, over the past few years, has become a critical addition for enterprise toolkits. Across industry surveys, and from our own experience, we are noticing that companies are reporting benefits of AI adoption on their bottom line. While researchers and many companies are experimenting with some exciting technologies, enterprise software is certainly among the most successful use cases for proving the utility of AI technologies,” said Onnivation’s founder & CEO Saket Agarwal.

According to Bert Labs’ Executive Chairman and CEO Rohit Kochar, AI is a general-purpose technology– just like electricity–reshaping the future.

“Currently, machines are intelligent enough to replace some mundane tasks, and automate some level of data processing and recognition. But they aren’t intelligent enough to make business decisions. So far, commercially available tech in the market provides machines (AI) that are able to process large amounts of data and identify and sort them, but aren’t capable of providing actionable insights,” said Dinesh Varadharaj, CPO, Kissflow Inc.

for details :

www.dprg.co.in

The Future Of AI: 5 Things To Expect In The Next 10 Years

There has been no better time to be in the world of artificial intelligence than now. AI has achieved an inflection point and is poised to transform every industry. Much has already been written about specific applications of AI. In this article, I take a step back to consider how artificial intelligence is poised to fundamentally restructure broader swaths of our economy and society over the next decade with five bold predictions that are informed by my expertise and immersion in the field.


1. AI and ML will transform the scientific method.


Important science—think large-scale clinical trials or building particle colliders—is expensive and time-consuming. In recent decades there has been considerable, well-deserved concern about scientific progress slowing down. Scientists may no longer be experiencing the golden age of discovery.




With AI and machine learning (ML), we can expect to see orders of magnitude of improvement in what can be accomplished. There's a certain set of ideas that humans can computationally explore. There’s a broader set of ideas that humans with computers can address. And there’s a much bigger set of ideas that humans with computers, plus AI, can successfully tackle. AI enables an unprecedented ability to analyze enormous data sets and computationally discover complex relationships and patterns. AI, augmenting human intelligence, is primed to transform the scientific research process, unleashing a new golden age of scientific discovery in the coming years.

2. AI will become a pillar of foreign policy.

We are likely to see serious government investment in AI. U.S. Secretary of Defense Lloyd J. Austin III has publicly embraced the importance of partnering with innovative AI technology companies to maintain and strengthen global U.S. competitiveness.




The National Security Commission on Artificial Intelligence has created detailed recommendations, concluding that the U.S. government needs to greatly accelerate AI innovation. There’s little doubt that AI will be imperative to the continuing economic resilience and geopolitical leadership of the United States.


3. AI will enable next-gen consumer experiences.

Next-generation consumer experiences like the metaverse and cryptocurrencies have garnered much buzz. These experiences and others like them will be critically enabled by AI. The metaverse is inherently an AI problem because humans lack the sort of perception needed to overlay digital objects on physical contexts or to understand the range of human actions and their corresponding effects in a metaverse setting.

More and more of our life takes place at the intersection of the world of bits and the world of atoms. AI algorithms have the potential to learn much more quickly in a digital world (e.g., virtual driving to train autonomous vehicles). These are natural catalysts for AI to bridge the feedback loops between the digital and physical realms. For instance, blockchain, cryptocurrency and distributed finance, at their core, are all about integrating frictionless capitalism into the economy. But to make this vision real, distributed applications and smart contracts will require a deeper understanding of how capital activities interact with the real world, which is an AI and ML problem.

4. Addressing the climate crisis will require AI.

As a society we have much to do in mitigating the socioeconomic threats posed by climate change. Carbon pricing policies, still in their infancy, are of questionable effectiveness.

Many promising emerging ideas require AI to be feasible. One potential new approach involves prediction markets powered by AI that can tie policy to impact, taking a holistic view of environmental information and interdependence. This would likely be powered by digital "twin Earth" simulations that would require staggering amounts of real-time data and computation to detect nuanced trends imperceptible to human senses. Other new technologies such as carbon dioxide sequestration cannot succeed without AI-powered risk modeling, downstream effect prediction and the ability to anticipate unintended consequences.

5. AI will enable truly personalized medicine.

Personalized medicine has been an aspiration since the decoding of the human genome. But tragically it remains an aspiration. One compelling emerging application of AI involves synthesizing individualized therapies for patients. Moreover, AI has the potential to one day synthesize and predict personalized treatment modalities in near real-time—no clinical trials required.

Simply put, AI is uniquely suited to construct and analyze "digital twin" rubrics of individual biology and is able to do so in the context of the communities an individual lives in. The human body is mind-boggling in its complexity, and it is shocking how little we know about how drugs work (paywall). Without AI, it is impossible to make sense of the massive datasets from an individual’s physiology, let alone the effects on individual health outcomes from environment, lifestyle and diet. AI solutions have the potential not only to improve the state of the art in healthcare, but also to play a major role in reducing persistent health inequities.

Final Thoughts

The applications of artificial intelligence are likely to impact critical facets of our economy and society over the coming decade.

for details :

www.dprg.co.in

The role of artificial intelligence in today’s digital advertising

 #dprg

By Manu Gupta

In recent years, the world has been more inclined toward technological advancement and the emergence of new-age technologies in the digital space affects every sector throughout the world. As a result of this, improvements in various machine learning techniques, like AI, have been playing a big part in digital advertising.

According to The Insight Partners’ analysis, the worldwide Artificial Intelligence in the marketing sector is estimated to reach US$ 107,535.57 million by 2028, growing at a 31.6% compound annual growth rate (CAGR). However, AI is transforming not just the overall operations, but the digital advertising landscape as well, from chatbots and virtual assistants to content development and user experience upgrades, among other things.

Digital Advertising benefits from the use of Artificial Intelligence.

AI helps to make appropriate judgments, AI thinks like a human to speed up and simplify the planning as well as execution process. Once the requirements are implemented and the inputs are provided, AI automates the entire routine procedure. A lot of growth marketing agencies are making use of AI & Machine learning. ET Medialabs is one such agency that has been successfully capitalizing on AI & Machine learning in order to provide sustainable business growth to its partner brands. At ET Medialabs, we understand the importance of moving with time and have made Artificial Intelligence and Machine learning a part of our daily execution processes. Our daily reporting contains the full-funnel view of all campaigns across Google and Facebook and helps us make informed decisions to drive sustainable growth.

AI offers many other benefits as well, as mentioned below –

Efficient Data Monitoring: It is common knowledge that marketing without data is like driving with your eyes closed. Taking data-backed decisions helps to drive real impact and real results through all the marketing channels.

More Effective Advertising: With AI’s data-driven research, businesses can now create a more effective advertising strategy for their company. By using technology, businesses can now forecast and improve the user experience and improve the user experience to meet their needs based on their buying patterns and decisions.

Increased Productivity and ROI: Using AI in Digital Advertising increases productivity because the operations are automated based on the instructions supplied. AI can also figure out which content works best and assist with content selection. As a consequence, the appropriate content reaches the right audience, resulting in a higher Return on Investment (ROI).

Enhances User Experience: Machine Learning (ML) is a concept used by Artificial Intelligence (AI) to learn about its users. It can analyze their habits and display content tailored to their preferences, keeping the audience interested and engaged. People are more inclined to acquire products or services if they have a pragmatic user experience.

further details please visit: https://www.financialexpress.com/brandwagon/the-role-of-artificial-intelligence-in-todays-digital-advertising/2541323/

www.dprg.co.in

Ethical Issues in Artificial Intelligence

Niraj DubeyAI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. Artificial intelligence (AI) may still be far from the stuff of science fiction, but its impact on industries and society is profound and getting more prevalent.
With its subset, machine learning, AI helps create safer workplaces, easier access to information and reliable health diagnoses. Artificial intelligence is also used in virtual assistants, self-driving cars, facial recognition, and recommendation systems in social media, entertainment platforms, and e-commerce. The potential of AI is limitless, but as this technology advances, the risks also increase. One of the problematic issues with unrestrained AI is data privacy violations, like what Facebook did when it gave Cambridge Analytica access to the AI-collected personal data of more than 50 million users. Tech giants like Microsoft, Facebook, Google, and others are building teams to address ethical issues that result in the wholesale collection of data. Clearly, businesses and industries are responsible for ensuring that AI development is ethical and unbiased. The various factors that justify the artificial intelligence ethics are like:-
Customize AI ethical framework to suit your industry
Different companies use technologies differently. An organization serious about building an ethical AI should unequivocally express its ethical standards, including naming all its stakeholders and how the standards will be maintained. Moreover, risk mitigation should be baked into the framework. With this method in place, the ethical standards that the different stakeholders-product developers, data collectors, managers, and owners-should comply with are easily determined.
Conform with global AI ethical guidelines
Artificial intelligence has the potential to raise the global gross domestic product (GDP) by 14% by the year 2030. This game-changing impact makes it imperative for businesses to take advantage of AI. International organizations, such as UNESCO, developed a framework for member-states to adopt and ensure that disruptive technologies such as AI benefit the greatest number of people in a bid to ensure that AI builds are trustworthy and human-centric.
Security
Another vital concern for building ethical AI is data privacy and security. This concern becomes apparent when an organization has no governance or data strategy set up at the project’s onset. Privacy, however, isn’t the sole concern when it comes to data. Take companies that deal in financial services. Often, they collect confidential data that needs added security measures. The ideal data partner would have various security options to meet the clients’ requirements and a robust security system to protect the clients’ data and prevent data breaches. Moreover, the data partner should comply with the data regulations specific to the industry and the area.
AI-powered Addiction
Smartphone app makers have turned addiction into a science, and AI-powered video games and apps can be addictive like drugs. AI can exploit numerous human desires and weaknesses including purpose-seeking, gambling, greed, libido, violence, and so on. Addiction not only manipulates and controls us; it also prevents us from doing other more important things-educational, economic, and social. It enslaves us and wastes our time when we could be doing something worthwhile. When I talk about this topic with any group of students, I discover that all of them are “addicted” to one app or another. It may not be a clinical addiction, but that is the way that the students define it, and they know they are being exploited and harmed. This is something that app makers need to stop doing: AI should not be designed to intentionally exploit vulnerabilities in human psychology.
Isolation and Loneliness
Society is in a crisis of loneliness. For example, recently a study found that “200,000 older people in the UK have not had a conversation with a friend or relative in more than a month”. This is a sad state of affairs because loneliness can literally kill. It is a public health nightmare, not to mention destructive of the very fabric of society: our human relationships. One might think that social media, smartphones, and AI could help, but in fact they are major causes of loneliness since people are facing screens instead of each other. Loneliness can be helped by dropping devices and building quality in-person relationships. In other words: caring. This may not be easy work and certainly at the societal level it may be very difficult to resist the trends we have already followed so far. But resist we should, because a better, more humane world is possible. Technology does not have to make the world a less personal and caring place-it could do the opposite, if we wanted it to. If we instead find our humanity not in our brains, but in our hearts, perhaps we will come to recognize that caring, compassion, kindness, and love are ultimately what make us human and what make life worth living. Perhaps by taking away some of the tedium of life, AI can help us to fulfill this vision of a more humane world.

The author is Sr. Faculty – GCET Jammu & Cyber Passionate – (J&K)

link : https://www.dailyexcelsior.com/ethical-issues-in-artificial-intelligence/

www.dprg.co.in

Driving successful AI transformations at the enterprise level

Chandramauli Chaudhuri leads the Data Science initiatives across Fractal’s

Tech Media & Telecom vertical in the UK & Europe. He works in close

collaboration with senior business stakeholders and CXO teams across

some of the leading global enterprises, enabling the development of long-term strategic AI solutions.

Being in the field of Artificial Intelligence and Machine Learning for close to a decade and working across a wide range of industries, his primary area of interest lies in R&D, algorithmic customisation, capability enhancement, and MLOps deployments of solutions. Analytics India Magazine interviewed Chandramauli to gain insights into AI transformation at the enterprise level.

As a business leader driving AI transformation across an organisation, it is critical to understand that Artificial Intelligence is just the means of value realisation and not an end goal by itself. Thus, the factors differentiating success and failure lie in its synergy with the company’s core principles, value proposition and customer-centricity. AI adoption is not a plug-and-play solution that yields overnight returns. Businesses need to think beyond just the cutting-edge software, high-end infrastructure and skilled coders. Alignment of the company’s culture, customer expectations and ways of working to support such transformations need to take equal if not greater importance. The companies that are doing well, especially in banking, finance, media, telecom, and tech, are those that have integrated AI into their day-to-day functions. They are moving it away from being a siloed and ‘specialised’ initiative undertaken in small pockets, to broader cross-functional collaboration.

As far as emerging trends are concerned, organisations have started focusing a lot more on two key areas – execution excellence and risk management. This means nurturing an agile mindset across teams, pursuing the right use cases, developing a strong data foundation, investing in the right skills, and having a robust strategic roadmap. There has also been growing acknowledgement of the challenges associated with cybersecurity, user privacy, and digital consent. Issues like lack of explanations, absence of audit trails and presence of bias in AI systems have gained far greater prominence from the global community in the last couple of years than in the past decade. It’s true that we still have a long way to go and yet to fully appreciate the complex socio-political and economic implications. However, we have started looking in the right direction, focusing on building greater transparency and trust. The early adopters of these practices stand to reap the rewards in both the short and the longer term.

more info :

www.dprg.co.in