This year you can expect the unexpected. Worldwide AI Webinar will gather more than 10 000 participants that will have the opportunity to listen to more than 20 world-recognized speakers from companies such as IBM, SAP, Samsung, Google, and many more. And in this year’s edition, Worldwide AI Webinar will also be FREE and open to everyone as our aim is to spread the knowledge and strengthen AI enthusiat network!
Now, let’s check out some of the topics you can find at Worldwide AI Webinar
Worldwide AI Webinar topic agenda
Responsible Applied AI Solutions
As organizations start scaling up their use of AI to capture business benefits, they need to be mindful of new and pending regulation and the steps they must take to make sure their organizations are compliant. That’s where Responsible AI comes in.
Responsible AI is the practice of designing, developing, and deploying AI with good intention to empower employees and businesses, and fairly impact customers and society—allowing companies to engender trust and scale AI with confidence.
Despite the real value organizations can achieve through Artificial Intelligence (AI), many still struggle to address the risks associated with it.
In a global survey
of risk managers, 58% identify AI as the biggest potential cause of unintended consequences over the next two years. Only 11% say they’re fully capable of assessing risks associated with organization-wide AI adoption.
Bias, discrimination, fairness, and explainability are areas of paramount concern. And while there are some specific definitions for these problem areas, translating them into action involves tough decisions and application-specific constraints.
AI is gaining traction as an "intelligent assistant"
for physicians and clinicians. AI helps radiologists analyze images faster and organize them better. It pours through volumes of electronic medical record (EMR) data and symptoms to diagnose disease. And it determines which neighborhoods have a higher risk of diabetes and heart disease so health systems can begin interventions.
Some AI-based software use "black box" models. In black box AI, users don't know how the program produces results. Turning data into insights is so complicated that even program designers may not know how they work.
AI in healthcare conflicts with the ethical principles of autonomy and justice. Without understanding, there can be no equity. For the sake of honesty and transparency, not to mention liability, physicians must be able to independently review the clinical basis for AI's decisions.
To break through the black box, developers must design AI-based products with transparency and ethical principles in mind from concept to completion.
Epistemic Artificial Intelligence
Although artificial intelligence (AI) has improved remarkably over the last years, its inability to deal with uncertainty severely limits its future applications. In its current form, AI cannot confidently make predictions robust enough to stand the test of data generated by processes different (even by tiny details, as shown by ‘adversarial’ results) from those seen at training time. While recognising this issue under different names (e.g. ‘overfitting’ or ‘domain adaptation’), traditional machine learning seems unable to address it in nonincremental ways. As a result, even state-of-the-art AI systems suffer from brittle behaviour, and find it difficult to operate in new situations.
The epistemic AI project
re-imagines AI from the foundations, through a proper treatment of the “epistemic” uncertainty stemming from our forcibly partial knowledge of the world. Its overall objective is to create a new learning paradigm designed to provide worst-case guarantees on its predictions, thanks to a proper modelling of real-world uncertainties.
aims to formulate a novel mathematical framework for optimisation under epistemic uncertainty, radically departing from current approaches that only focus on aleatory uncertainty. This new optimisation framework will in turn allow the creation of new ‘epistemic’ learning settings, spanning all the major areas of machine learning: unsupervised learning, supervised learning and reinforcement learning.
Machine Learning Models in Banking & Finance
So to mitigate credit risks
, banks are now renewing their business models by employing technologies associated with Big Data, data availability, and Artificial Intelligence. Under machine and deep learning analysis, banks are now able to carry out a complex task like credit risk predictions, monitoring, model reliability, and predicting loan default probability. Machine learning algorithms enhance predictive abilities and can, therefore, help the lenders receive real-time insights about their current and potential borrowers. This shall allow them to disburse loans to the right set of clients, especially in countries with little or no past credit information.
Machine learning helps in maintaining transparency and improves overall accuracy by detecting instances of fraudulent activities or any potential anomalies taking place. It is also not new for banks to continually review high-risk accounts.
Testing AI & Quality Assurance
Software testing efficiency and software testing effectiveness are two key metrics that determine the overall progress of a test strategy. Artificial Intelligence (AI) and Machine Learning (ML) in testing essentially focus on these two parameters. AI & ML can optimize
risk coverage, prevent redundancies, perform portfolio inspection, detect false positives, diagnose defects automatically, and analyze user experience.
It is estimated that more than 60% of the test cases in an enterprise test case portfolio are redundant
. AI identifies such test cases that are physically as well as logically identical and eliminates the duplicates, which do not add any business value and can be removed without decreasing the business risk coverage. AI is capable of maximizing defect detection and risk coverage while minimizing costs, execution time, and the number of test cases by identifying the optimal test sets. It can uncover weak spots in test case portfolios by tracking flaky test cases, unused test cases, untested requirements, and those test cases that are not linked to the requirements. Additionally, AI has self-healing automation properties, which means it can heal the broken automated test cases and make test automation better resilient to changes.
All in all, AI makes software testing smarter while promoting higher efficiency and effectiveness.
Learn the best AI analysis & applications from top-industry experts at Worldwide AI Webinar
Worldwide AI Webinar
is brought to you by Wow AI
- a global provider of high-quality AI training data. with a view to gather all the best AI researchers and practitioners and promote a stronger network of AI and Machine Learning professionals around the world.
It Gathers Top-AI Industry Experts From Google, IBM, SAP, AWS, Samsung, etc.
The Founder, AI Leadership Institute at IBM, the Director of Cloud Partner Engineering at Google, the VP at SAP for Artificial Intelligence, Director of AI, Professor at Oxford, etc. are all gathered at the conference. Our presenters bring the most cutting-edge AI research and practice to the stage, offering their extensive knowledge and experience. Within 2 days of the AI Webinar program, we take you on a unique journey through the innovations of Artificial Intelligence and best practices for business cases.
Nevertheless, you can also get your questions answered live at the conference!