Name: Applying Machine Learning to unstructured files and data for research
Start: 2017-12-12T11:00:00Z
End: 2017-12-12T11:00:44.000Z
Location: BrightTALK
Rating: 4.33

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

The collaboration application landscape is rapidly changing. Generative AI offers the potential to improve productivity and streamline employee engagement. New apps are quickly arriving that aim to bridge the home and office employee divide. And, employee experience platforms offer new ways to share information and establish employee relationships no matter the location. 

In this webinar, we'll share the top five emerging collaboration applications and what IT and business leaders need to do to ensure success.

Emerging Collaboration Applications

Machine learning is the most efficient known way to create software models that solve real-world problems. When trained correctly, ML models can know more than any human who ever lived, explain or summarize anything, and find patterns in the data that world-class experts have been missing for decades. But what happens when you train these incredible artificial brains by feeding them noise?

In this talk, we will explore both directions of this noise problem:
- What does AI "learn" when you train it in an unsupervised way, using data that hasn't been scrubbed beforehand?
- What do humans learn when the information that surrounds them is 90% noise, including noise that was produced by AI?

In the process, we will work together to articulate a new paradigm of AI that applies to everything from news media to business intelligence tools -- a paradigm that emphasizes quality over quantity, and focuses on removing bad inputs to produce better outputs.

About the speaker
Alex Fink is the Founder and CEO of the Otherweb, a Public Benefit Corporation that uses AI to help people read news and commentary, listen to podcasts and search the web without paywalls, clickbait, ads, autoplaying videos, affiliate links, or any other junk. The Otherweb is available as an app (ios and android), a website, a newsletter, or a standalone browser extension.

De-Noising AI: Why Quality Beats Quantity for Humans and Machines Alike

Unveil the AI platform dilemma! In this session presented by Gopinath Manimayan, lead architect at Moonraft Innovation Labs, you’ll delve into the ultimate showdown between open-source and commercial AI platforms. Discover the strengths, weaknesses, and unique offerings of each option, helping you make the perfect choice tailored to your business needs. Empower your enterprise with invaluable insights to leverage AI strategically and gain a competitive edge in today's dynamic market. 

In this presentation, you will: 
- Understand the key differences between open-source and commercial AI platforms.
- Explore the advantages and disadvantages of each option for your business.
- Learn how open-source platforms promote customization and community-driven innovation.
- Discover the benefits of commercial AI platforms, including robust support and seamless integration.
- Gain valuable guidance on selecting the right AI platform to accelerate your business growth.

Open Source Vs. Commercial AI Platforms: Make the Right Choice for Your Business

In recent times, generative AI (GenAI) has garnered significant attention and organizations are eager to explore its potential for enhancing business operations. 

In this session, scientist and professor Dr. Umesh R. Hodeghatta will explore the practical applications of GenAI within an enterprise context, shedding light on how it can propel innovation, creativity, and operational efficiency. Further, he’ll discuss the transformative role of AI as a catalyst for creativity, efficiency, and innovation within organizations.

Generative AI in Enterprise Operations

Data storytelling is building stories supported by data, allowing analysts and data scientists to present their insights to engage the audience and inspire them to make decisions. Generative AI (GenAI) can assist analysts and scientists while writing their stories supported by data and speed up the story creation process, making data-driven narratives more accessible, engaging, and impactful. 

This webinar will analyze how to use GenAI to empower data storytelling, by building data stories tailored to a specific target audience and insight. Through practical examples and real-life applications, we will explore how GenAI can adapt its narrative style and language to engage different audience groups. By attending this webinar, you will gain a comprehensive understanding of the concept of data storytelling and how it can be harnessed to make a lasting impact. In addition, you will discover the potential of GenAI in enhancing data storytelling, revolutionizing the way in which information is communicated and received. 

3 key takeaways:
- The concept of data storytelling.
- Using GenAI to enhance data storytelling.
- Tailoring GenAI to specific audiences and insights.

AI-Assisted Data Storytelling

While ChatGPT took the world by storm, we also saw the darker side of new GPT and GenerativeAI solutions. There are so many emerging use cases around data-driven AI solutions. And many of these evolve a legacy approach to AI. While many of these new initiatives aim to make life easier for users and the organization, there is a darker side to data. 

With over 90% of threats coming in directly through an inbox, how do data centers ensure they're protected against some of the most advanced threats in our industry? All-new findings and security research points to an entirely new era for security and the enterprise data center. Get ready to hear some of the newest findings. 

This session will explore the meteoric rise of LLMs and GPT solutions. We'll also dive into the dark web to see how malicious actors are using these technologies to target the data center. Most of all, we'll explore how leaders protect their clients, data, and digital infrastructure. 

This session will cover:
- Enterprise data centers and the evolution of AI.
- Updating the data center: Preparing for weaponized AI
- A dive into the dark web: Learn what your Data is worth 
- Understanding AI threats impacting enterprise data centers.
- Tools and strategies to fight back.

Weaponized AI and GPT: Updating data center security practices

The arrival of ChatGPT was a culture-altering event that upended business strategies and made AI a priority in IT planning. But ChatGPT and similar generative AI tools, with their large language models and uncanny talent for human-like communication and analysis, have complicated the AI picture. It can be hard for business and IT leaders to understand their options and make good choices.

Few areas seem more suited to AI-based automation and decision making than the supply chain with all its challenges and complexities. AI has long been touted as a way to improve predictive analytics and make supply chains more responsive by optimizing demand planning, inventory management, logistics and other key processes. Now generative AI promises unheard-of levels of intelligent automation.

In this in-depth discussion, speakers will explain AI types and terminology and their implications for supply chain management. Avoiding rosy scenarios, they'll zero in on the following:

- Where AI is making a difference in supply chains today
- How to sort through the hype and choose AI apps that add real value
- AI's possible impact on supply chain security

Practical AI Applications in the Supply Chain: Finding Value While Managing Risk

In today's ever-changing business landscape, supply chain management faces numerous challenges. From volatile market demands to complex logistics and inventory control, staying competitive requires innovation and efficiency. Just as market volatility slows and supply chains begin to stabilize, another disruption is coming – but this time, it will be for the better. 

With applications across demand forecasting, inventory management, quality control and more, Artificial Intelligence (AI) has the potential to revolutionize supply chain operations, leveraging real-time data insights to enhance performance, reduce costs, and improve customer satisfaction. 

In this session, join Dr. Amin Kaboli (Lecturer, Swiss Federal Institute of Technology at Lausanne, EPFL) to get a roadmap to future-proof your supply chain with AI.  

Topics for discussion include: 
- Practical applications of AI to effectively optimize your supply chain. 
- The latest AI trends and successful practices reshaping the supply chain landscape. 
- Strategic navigation strategies to successfully guide your organisation through its AI-driven transformation journey. 
- And more...

AI and the Supply Chain: What's Changing and Why Should You Care?

Practical AI applications in the supply chain: Finding value while managing risk

Data analysis is the heartbeat of modern marketing, operational improvement and human capital management. But with an increasing emphasis on privacy and governance, it’s important to strike a delicate balance between utilizing data for analytics and ensuring best practice. This session aims to shed light on this crucial issue.
 
Why should you attend this session?
 1.) Understanding the Importance of Privacy: Explore the significance of privacy in data analytics and why it must not be overlooked.
2.) Strategies for Balancing Analytics and Privacy: Hear about effective strategies that allow you to draw insights from data without compromising on privacy.
3.) Learning from Case Studies: Gain insights from real-life examples showcasing successful integration of data analytics and privacy.

Data analytics meets privacy

There has been much work done, by academics and product designers on data visualisation. But relatively little attention has been paid to the overall analytic experience - how users discover the features, orient themselves and then make use of their discoveries.
 
Tune into this session from industry thought leader Donald Farmer as he explores some very useful behavioral patterns which enable us to create a more engaging and decision-oriented analytic experience. The webinar will further consider how these patterns can be applied in tools for analytics specialists, business users and when embedded in operational applications.

Analyzing the analytic user experience

In the world of big data, the importance of data visualization and related technologies cannot be stressed enough. These tools play a crucial role in transforming raw data into easily comprehensible forms, stripping away unnecessary noise, and highlighting valuable insights that tell a compelling story. Effective data visualization is an art that requires a delicate balance between aesthetics and functionality. Data and visuals must come together to work in such a manner where the combination of analysis and storytelling make a powerful imprint and convey the right message.

But just as a coin has two sides, a foundational understanding of data visualization tools is essential for swiftly grasping data trends, outliers, and patterns. In this presentation, Dr. Sue Tripathi will explore: 
- What data visualization is and why is it important today. 
- How to create accessible data visualizations.
- Tools available on the market and how to evaluate them. 
- Implementation strategies. 
- Real-world examples showcasing the potential of data visualization in business.
- Trends and the future of data visualizations.

Creating accessible data  visualizations and evaluating appropriate tools

AI can be applied to various fields, including manufacturing, automobile, healthcare, finance, and IT, and its impact is becoming increasingly evident. However, developing an AI system that meets essential criteria such as reliability, safety, trustworthiness, security, explainability, interpretability, accountability, transparency, privacy enhancement, and fairness while managing harmful biases poses significant challenges. It is the collective responsibility of all stakeholders to address these challenges.

During the session, I will delve into the obstacles and ethical considerations involved in creating a responsible AI system. I will provide valuable insights into areas such as data privacy, data security, governance, regulations, and the integration of AI and ML into existing digital strategy frameworks. By participating in this session, attendees will gain a comprehensive understanding of the importance of developing responsible AI practices to harness the power of data-driven decision-making, ultimately driving business revenues and success in today's competitive digital world.

Key topics to be covered include:
- The significance of data in shaping data-driven decision-making strategies.
- Ensuring robust data governance and maintaining high standards of data security.
- Ethical considerations surrounding AI and the impact on data-driven decision making.

Ethical considerations in data-driven decision making

In today’s era of digital disruption, extending automation with AI capabilities, also known as intelligent automation (IA), remains on the agenda of C-suite executives. Enterprise leaders are recognizing that automation and AI technologies have the potential to transform productivity, improve accuracy, reduce costs, and improve customer and employee experiences. Extending automation with AI opens the door to driving further exponential impact in terms of accelerated innovation, higher revenue, better efficiency and enhanced experience for customers and employees.

By virtue of being at the intersection of process automation & AI, IA brings increased benefits. However, IA can also increase execution complexities. To successfully adopt and extend automation capabilities with AI, more than just the technical implementation is required – a holistic approach is required to achieve and sustain breakthrough value. Organizations need to navigate across process centric automation and data and machine learning centric AI for what may appear to be a minefield of complexities and dependencies – such as mapping business challenges to processes, incorporating business process optimization, having high data quality, creating integrated AI & automation governance, and having the right expertise in ML and MLOps, to name a few. 

Join Deloitte Consulting's leaders, Joe Surprenant (VP of Innovation, AI & Innovation) and Ketan Ambani (AI & Data Operations Managing Director), where they will present findings from surveying over 2,600 business leaders from Deloitte’s latest State of AI in the Enterprise, as well as findings from Deloitte’s Automation with Intelligence report where Deloitte surveyed over 470 executives across 35 countries. They’ll also dig into the keys to success and lessons from driving and maintaining intelligent automation programs, while highlighting success stories across several organizations.

Extending automation with AI capabilities

In recent years, there has been a significant and rapid shift in how organizations in the Life Sciences, Bio pharma, and Medical Device sectors prioritize digital transformation.

The Life Science Industry, in particular, has emerged as a frontrunner in digital disruption, transitioning from basic healthcare services to digital therapeutics and connected care. The COVID-19 pandemic has further accelerated the evolution of the healthcare landscape. Patient data sharing has increased, patients themselves are demanding greater engagement in their care, and non-pharmaceutical interventions are increasingly being utilized to treat various diseases. This heightened awareness of the future of health has prompted enterprise leaders in the industry to scale up and accelerate their automation and AI initiatives. However, without a well-defined strategy and approach, organizations may encounter stagnation in their digital transformation efforts.

To overcome these challenges, leading Life Sciences organizations have turned to AI-powered automation as a means to enable growth and success. This approach involves leveraging advanced technologies such as Generative AI, Machine Learning, Natural Language Processing, computer vision, and Robotic Process Automation to enhance the efficiency of automated processes.

By incorporating AI into automation workflows, organizations can empower their technology to make intelligent decisions, perform cognitive tasks, and automate non-rules-based processes. This not only opens up numerous opportunities for innovation but also presents new challenges.

Join Deloitte Consulting's leaders, Bhavish Madurai and Prashanth Sivadasan, to learn the importance of choosing the right capability model, engaging successfully with the business, and creating the right approach for demand generation. They will also dig into the keys to success and lessons for scaling and accelerating AI and automation programs across Life Sciences organizations.

Accelerating and scaling AI-powered automation in Life Sciences

In the session, attendees will explore the critical role of data in shaping successful digital strategies and discover how AI and ML technologies can be utilized to extract meaningful insights. Topics covered include leveraging AI and ML algorithms for data analysis, real-time analytics, predictive modeling, customer segmentation, and targeting. 

The session also addresses challenges and ethical considerations in data-driven decision making and provides insights on integrating AI and ML into existing digital strategy frameworks. By attending this session, participants will gain the knowledge needed to leverage AI and ML for data-driven decision making, enabling them to develop effective digital strategies that drive business growth and success in today's competitive digital landscape.

Key Topics: 
- Importance of data in shaping digital strategies.
- Leveraging AI and ML for data analysis and insights.
- Real-time analytics and predictive modeling for proactive decision making.
- Optimizing customer segmentation and targeting using AI and ML.
- Addressing challenges and ethical considerations in data-driven decision making.
- Integrating AI and ML into digital strategy frameworks.
- Exploring emerging trends and future directions in data-driven decision making.

Data-driven decision making: Harnessing AI and ML for digital strategy

Crowdfunding is a new frontier for financial service firms; but one that holds great promise for providing extended value to all of a financial service firm’s clients. For the firm’s issuer clients, crowdfunding is a relatively new, and often effective, way to raise capital for the issuer. For the firm’s investor clients, it is a chance for investors that were not able to access parts of the equity markets previously, to reach those markets and investments now. It’s also a more effective and efficient way for all investors to find and access different types of alternative investments.

The crowdfunding that will be discussed in this webinar is equity crowdfunding, as opposed to donation-based crowdfunding. The differences between these two major types of crowdfunding will be discussed, along with the unique characteristics and benefits of equity crowdfunding.

This presentation will also discuss how the use of Alternative Asset Classes and individual Alternative Assets can substantially increase portfolio returns while reducing portfolio risk. Alternative investments can be delivered by means of digital platforms such as crowdfunding platforms.

Here are some of the topics which will be discussed:
1. The different types of crowdfunding.
2. Equity versus donation-based crowdfunding.
3. The unique characteristics and benefits of equity crowdfunding.
4. Regulation CF crowdfunding.
5. Non- regulation CF crowdfunding.
6. How to get into the crowdfunding business.
7. Alternative investments – what they are and how to use them?
8. Increasing portfolio returns and reducing portfolio risk with alternative investments.
9. Putting alternative investments on crowdfunding platforms.

Using crowdfunding and alternative Investments to better serve clients

Maximize Business Critical Decision Making with an Innovative Big Data Platform

The Last Mile: Implementing Your Data Strategy

5 Ways to Fuel Your Big Data Analytics in 2018

Can multipurpose BI tools take over from specialist offering

How AI is Transforming the World of Analytics

How the “Three I’s” Will Change Enterprise Analytics in 2018

The Ideal Architecture for BI on Big Data

5 Hot Trends for Data and Analytics in 2018

How to build a successful analytics culture in your organisation

Enterprise Analytics Journey, the IBM point of view for IBM Z customers

Big Data Analytics vs Privacy: Risks and Opportunities

CTO Outlook: Big Data Gets Real

Mixed Reality - Market Potential and Use Cases

Have You Seen Your Data in Virtual Reality Yet?

AR and VR by the Numbers: A Data First Approach to the Technology and Market

The Role of Data in XR

AI in VR: Enabling a world of “6 DoF” Analytics

Natural Language Processing Exposed: The Art, the Science and the Applications

Using Augmented Analytics to Gain a Competitive Business Advantage

Driving Value with Data Science

Data Fabric: A New Paradigm For Self-Service Data & Data Scientists

Building a Fast, Scalable & Accurate NLP Pipeline on Apache Spark

The Human Role in AI

AI in social housing

From Big Data to AI: Building Machine Learning Applications

BI & Big Data Analytics in 2018

Researchers generate huge amounts of valuable unstructured data and articles from research every day. The potential for this information is huge: cancer and pharmaceutical breakthroughs, advances in technology and cultural research that can improve the world we live in. 

This webinar discusses how text mining and Machine Learning can be used to make connections across this broad range of files and help drive innovation and research. We discuss using Kubernetes microservices to analyse the data and then applying Machine Learning and graph databases to simplify the reuse of the data.

Applying Machine Learning to unstructured files and data for research

machine learning

data science

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Applying Machine Learning to unstructured files and data for research

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