Name: How to avoid Bias in AI Models
Start: 2021-02-04T16:30:00Z
End: 2021-02-04T16:30:58.000Z
Location: BrightTALK
Rating: 5

Retail industry is facing a big threat from cyber criminals. They can attack retail companies' cloud environment from anywhere, anytime with impunity. A successful attack by cyber criminals pose a huge risk not only to retail companies’ business, but it will damage its reputation, affect the morale among its employees, ruin trust with its customers and partners and destroy its market valuation. 
What are you doing to stay ahead of cyber criminals?  
In this talk, we will present how complex the cloud environment in the retail industry has gotten today; hence, it has opened up many vulnerabilities for cyber attacks. For retail companies to address this challenge, they have to use artificial intelligence (AI) and machine learning (ML) methods. The reason for this is that the manual approach to identifying potential vulnerabilities is time-consuming and error-prone and has proven to be ineffective in thwarting cybercriminals from attacking the cloud environment. 
We will share our experience on how to effectively use AI & ML methods in the retail industry to stay ahead of cyber criminals.

How to stay ahead of  cyber criminals in the retail industry?

Today, Data Science is one of the most in-demand, highly paid, and sought after jobs. If you look at the list of current job openings, you will find 1000s of data scientist jobs. Every university graduate, every experienced software coder, every database administrator wants to acquire skills that are essential to become a data scientist. In this session, on April 2nd , 2021 at 1130AM EST, we will be discussing the career prospects of a data scientist and how to develop skills required to become a data scientist with our special guest George Firican.
George is a passionate advocate for the importance of data. His innovative approach to addressing data challenges commanded international recognition through award-winning program and project implementations in data governance, data quality, business intelligence and data analytics. George has been a data professional for more than 10 years. One of his passions is to create informative, practical and engaging online courses and videos, and help organizations get more visibility on social media. George is the proud founder of LightsOnData.com and its YouTube channel and is a co-host of the Lights On Data Show. You can contact him on: georgefirican@gmail.com

Becoming A Data Scientist

Cyber Criminals are innovating new ways to target the people and organizations.  With ransomware to social engineering techniques like phishing, vishing, etc. they seem to be getting better at their game and are impacting adversely the reputation and data of individuals to big corporate entities.  The problem is compounded because of lack of adequate and appropriate attention to information security by the individuals and organizations.  According to the latest research by Cybersecurity Ventures, by the year 2021 cybercrime losses will cost upwards of $6 trillion annually. Many of the cybersecurity experts and professionals believe that AI and Machine Learning can help in finding solutions to cybersecurity threat problems. AI can efficiently analyze the user behaviors, identify the abnormal patterns in the network, predict cyber vulnerabilities, etc.
On January 21st, 2021 at 1130AM Eastern Time (EST), NUSA Lab will be organizing discussion (webinar) with Mr. Satish Narayanan, co-founder, President & CEO of SecqureOne, a silicon-valley based Cyber Security & Compliance Solution provider. In this session, Mr. Sathish will be explaining the current cyber security threats in today’s digital world as well as the how AI and Machine Learning can be applied to prevent organizations from getting hacked.

Cyber Security Threats in Digital World

AI systems and algorithms are trained and modeled and validated by humans on the data generated by humans or sensors or other automated systems. This poses challenge and risk of introducing human bias into the models, though many of us assume the AI systems are unbiased. As we are marching towards 2021 and making significant progress towards more sophisticated algorithms and models, we should also consider reducing bias in AI. Without managing bias, any AI initiative may fall apart due to the lack of trust and confidence by CEOs and leadership. In this session, we will be discussing some of the ethical and trust issues of AI systems due to AI Bias, and how organizations can model and validate AI systems and algorithms to reduce AI Bias.

Bias in AI and Machine Learning Models

Artificial Intelligence is transforming every industry around the world, including the healthcare. Healthcare has abundance of data including patients who visit to the clinic, prescriptions, lab tests, radiology, medical procedures performed, etc. In this session, Mr. Rangaprasad Sampath, will provide insights on how AI and Deep Learning technologies can be applied to solve some of the challenges of healthcare industry. Also, we will be discussing how safely and ethically apply these technologies for the current and future applications of AI in healthcare. 
Rangaprasad Sampath (Ranga) is an engineer by education and a technologist by training. After graduating from Stanford University, USA with a Masters in Electrical Engineering, he has spent over a decade and a half honing up his skills in Networking Technology i.e. switches and routers, wired and wireless. From that same spirit of a connected world, he created You+AI which is an initiative to connect Humans (You) in various endeavors with Artificial Intelligence (AI). One of the first initiatives of You+AI has been the You+AI Podcast where Ranga speaks with healthcare professionals and the conversation explores where AI may drive the most impact. Season 1 with 15 episodes has listeners across 6 continents---36 countries---297 cities. Stay tuned via youplusai.com for Season 2, starting soon. 
About NUSA:
Umesha Nayak & Dr. Umesh Hodeghatta, are both innovative and exploring ways to always solve customer problems and challenges. We are practical oriented and focused on delivering effective solutions. We have been delivering effective solutions vide consulting on applying AI and Machine learning to various industries like Retail, Healthcare, Software, Product Development, Information Security, and Human Resources.
For more details, contact us on umesh@businessanalyticsr.com or +1 408 757 0093

AI in Healthcare

Today every CEO in the organization wants to leverage the competitive advantage with the help of AI and Machine Learning. However, whether this can be achieved or not is still a question to many senior executives. In this presentation, our goal is to go over the best practices and framework of AI and Machine Learning and share our experience for making every project successful.  We will also highlight on the pitfalls to be avoided.

AI/ML - Pitfalls and Success Formulae

Abstract:
We have achieved significant improvements in Machine Learning along with computing infrastructure. Many varieties of machine learning algorithms have been proposed, implemented and deployed. More often than not, these deployments are unitary learners with a clever refinement, the so called hyper-parameter tuning. While it is definitely a step in the right direction, the strategy is depth first and runs counter to both Ockhams Razor and No Free Lunch (NFL), two fundamental axioms in Machine Learning. Raman and his research students have engineered a system that provides an alignment with both the razor and NFL. In this talk we will introduce NFL and Razor axiomatically and demonstrate a system that remains consistent with NFL and facilitates efficient and effortless analytics without relying on any particular algorithm.

"Towards Assisted Cognition"

Artificial Intelligence, Machine Learning, Deep Learning are the buzz words across the globe today. Businesses, as well as people, are trying to make out if it is simply hype or not. While there is hope there is also gloom somewhere, as far as AI is concerned. There is a lot of work to be done to ensure effective and valid deployment of AI solutions.

Today’s AI concern is  Frameworks and toolsets to manage the entire lifecycle in an integrated manner to ensure dependable results - which is the burgeoning need for AI to move forward. In this discussion, we will be focussing on some of the important challenges faced with respect to resources, frameworks, tools, and techniques for implementing AI and also the direction it is trending towards.

AI – Warriors and Weapons

Artificial Intelligence, Machine Learning, Deep Learning are the buzz words across the globe today.  Businesses as well as people are trying to make out if it is hype or not.  While there is a hope there is also gloom somewhere, as far as AI is concerned.  Lots of work needs to be done to ensure effective and valid deployment of AI solutions.  Today’s AI concern is  Frameworks and toolsets to manage the entire lifecycle in an integrated way to ensure dependable results is the beckoning need for AI going forward. In this discussion, we will be focussing on some of the important challenges faced with respect to the resources, frameworks, tools and techniques and also the direction AI is trending towards.

What is covered in AI Warriors and Weapons AI-inar on May 17th

Where human beings are involved bias cannot be ruled out.  This is true even in case of data visualization, data analysis, building and validating AI/ML models.  Bias in these areas can have disastrous effects particularly when you apply the results to human beings or nature, etc.  On February 4th , 1130AM EST, session, Simon Dagenais, lead data scientist at Snitch AI will be exploring various scenarios where bias is possible and how we can overcome the same to avoid adverse impacts of the results of AI/ML."

Simon Dagenais, is the Lead Data Scientist at Snitch AI, a machine learning model and data validation tool. Coming from a data science consultant background, he now aspires at solving problems data scientists will encounter during the course of a machine learning project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.

How to avoid Bias in AI Models

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How to avoid Bias in AI Models

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