How AI and Machine Learning can Transform Organizations
•
0 gefällt mir•166 views
About the webinar 83% of businesses say AI is a strategic priority for their businesses today, while only 23% of businesses have incorporated AI into processes and product/service offerings today [source: Forbes]. Artificial intelligence and machine learning have started to disrupt the traditional way of doing business and revolutionize everything from farming to rocket science. Do you want to be left behind? Through this webinar, we will discover how various industries are adopting technologies to innovate and disrupt their business models to increase revenue, reduce costs, improve quality and customer satisfaction as well as to handle risks. What you will learn - How organizations have gained benefits with AI in their business to increase revenue, reduce cost, improve quality and manage risks - Mind-blowing Innovative and disruptive emerging AI use cases in various industry sectors - How to leverage AI in your business to get a competitive advantage
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie How AI and Machine Learning can Transform Organizations
Ähnlich wie How AI and Machine Learning can Transform Organizations (20)
Mehr von Skyl.ai
Mehr von Skyl.ai (13)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
How AI and Machine Learning can Transform Organizations
- 1. How AI and Machine Learning can Transform Organizations
- 2. Technology enthusiast with 13+ years of experience working in the information technology and services industry. Leads cutting-edge solutions for businesses using Machine Learning and Artificial Intelligence. Areas of expertise includes Architecture design, Solutioning, Data Engineering and Deep Learning.Mohit Juneja Solutions Architect The Speaker
- 3. Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. Shruti Tanwar Lead - Data Science The Speaker
- 4. Bikash Sharma CTO and Co-founder at Skyl.ai CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects. Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis. The Panelist
- 5. Getting familiar with ‘Zoom’ All dial-in participants will be muted to enable the presenters to speak without interruption Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
- 6. How AI and ML have transformed various industries What is Artificial Intelligence and Machine Learning 1 2 ...In the next 45 minutes
- 7. Machine Learning automation platform for unstructured data A quick intro about Skyl.ai Guided Machine Learning Workflow Build & deploy ML models faster on unstructured data Collaborative Data Collection & Labeling Easy-to-use & scalable AI SaaS platform
- 8. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
- 9. Artificial Intelligence and Machine Learning01
- 10. What is Artificial Intelligence? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Source: Investopedia Artificial General Intelligence (AGI) A machine capable of understanding the world as well as any human, and with the same capacity to learn how to carry out a huge range of tasks. Narrow AI Effective at performing specific tasks. The most common form of AI, and is seen through many tasks in daily life.
- 11. What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed
- 12. Machine Learning = Disruptive Technology Machine learning changes the way we think about a problem and fundamentally changes the way we solve it. Adoption of AI/ML allows us to: ⊚ Reduce the time of programming ⊚ Scale your product ⊚ Solve problems which are unprogrammable
- 13. Google shrinks language translation code from 500,000 to 500 lines with AI Traditional software Approach written using logic and assertion like if- else solves problems with experiments, learning through examples Machine Learning Approach
- 14. Why should you adopt Machine Learning & why now is the right time? Digitization Technology & Computational Power Need for businesses to scale
- 15. What kind of business problems can be solved? Improving efficiency and effectiveness Problems with ever- changing complex rules: Dynamic Pricing, Estimated time of arrival Problems that can’t be solved with rules: unstructured data - text, audio, video, images Scaling: Personalization, Recommendation s Repetitive nature of work: RPA, Chatbots
- 16. How AI and ML have transformed various industries 02
- 17. E- commerce
- 18. Product Recommendation ‘Similar product’ or ‘Top picks for you’ recommendations based on individual buyer history Related products aligned with shopper’s affinity to upsell/cross sell
- 19. Product Categorization Automated identification of products for inventory addition.
- 20. Product Review Moderation Generating a summary of reviews and ratings
- 21. Personalized Discovery Better than text search: ‘I am looking for this shirt’ ‘What is the price of this table’ Enabling quick discovery of products Shopping for your favorite celebrity look becomes much easier
- 23. Contract Analysis ⊚ Identify and extract relevant information like aggressive clauses, legal anomalies, future financial obligations, renewal or expiration dates and even summarise contract data down to concise points. Contract Title Start Date Contracting Parties
- 24. Regulatory Compliance Monitoring ⊚ Use Natural Language Processing (NLP) to quickly scan legal and regulatory text for compliance issues and do so at scale.
- 25. Insurance Claims Property Risk AssessmentDamage Assessment
- 26. Healthcare
- 27. Medical Imaging & Detection ⊚ Image classification aids radiologists to detect pneumonia using an X-ray image of lungs, identify early development of tumors in the lungs, breasts, brain and other areas, for skin cancer detection, diagnose tuberculosis, heart disease and alzheimer’s disease.
- 28. Electronic Records Analysis ⊚ A large part of medical notes made on EMR systems are free- text notes by physicians. These can be tedious to analyze manually to gain insight into patients various medical conditions and risk factors.
- 29. Manufacturing
- 30. Product Quality Inspection Defective Labeling Identification - Identify misbranding, incorrect positioning and damaged labels to ensure the product conformity Inspect Automobile parts - detection of missing nuts and bolts in the automobile under production
- 31. ⊚ Verify whether employees are wearing appropriate safety gears for a given job-site like safety helmets, vests, glasses/goggles, shoes or other protective gears, accordingly generate alerts and reports to enable safety gear compliance. Workplace Surveillance
- 32. ⊚ Extract information like delivery address, vendor names, product details, quantity and pricing from these documents. ⊚ Using the extracted data, AI can match PO’s with their Invoices and ORN’s, maintaining transaction consistency. Procurement Matching (Invoices, order receipt notes,…)
- 33. Real Estate
- 34. Detect watermarks of competitors or third parties on real estate listing images Watermark Detection
- 35. When it comes to the real estate market, property listing photos are aplenty. Often, buyers are frustrated at the lack of good quality images and can quickly be turned off by examining inferior ones. Image Quality of Property Listings
- 36. Skyl.ai - as ML automation platform Efficient Data Management Solve your data issues; collect and manage data efficiently Accuracy & Quality Maintain accuracy and quality; train and test faster; monitor quality Effective Collaboration Collaborate and manage projects efficiently Early Visibility Get early visibility; visualize and affirm correctness on every step of the way Scalable High - Performance Access on-demand and scalable, high-performance infrastructure Reduce Cost Reduce cost of implementation; do it with less specialized resources
- 37. Some Examples of AI Models in Skyl.ai
- 38. We can help you with... ⊚ AI Adoption Assessment ⊚ AI Systems Integration ⊚ AI Performance Evaluation ⊚ AI-Enabled Software Development Our AI Consulting Services www.skyl.ai contact@skyl.ai
- 39. ⊚ Free 1 month Trial + POC ⊚ Complimentary 30 min consultation ⊚ AI Implementation Playbook www.skyl.ai contact@skyl.ai Special offer for you...
- 40. Questions? ?
- 41. We hope to hear from you soon Thank you for joining! 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 contact@skyl.ai
Hinweis der Redaktion
- Hello everyone and welcome. Thank you for joining today’s webinar on How AI and Machine Learning can Transform Organizations. My name is Edwin Martinez and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
- First we have Mohit Juneja, Mohit is a Solutions Architect and Technology supporter with over 13 years of experience in the IT and Service industry. He leads cutting-edge solutions for businesses using Machine Learning and AI. He’s an expert in Architect design, Data Engineering, and Deep Learning. Welcome Mohit!
- Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- Finally, we have Bikash Sharma, joining us as a panelist. Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- Before we begin, I’d like to briefly talk about some Zoom features that will be relevant to us. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen. We’ll make sure to address your questions during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again. So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Mohit
- Let me start with a quick intro about Skyl.ai and its capabilities. Skyl.ai is a ML automation platform for unstructured data which includes text, images, audio etc. Using Skyl.ai business can build and deploy high quality NLP, Computer Vision models in hours rather than days or weeks. So how does Skyl do that? Skyl.ai provides an easy to use unified platform for the entire machine learning workflow which includes data collection, data labeling, feature engineering, training the model by choosing out of the box algorithms at scale, once model is trained, carrying out model evaluation and finally one click deployment and monitoring the model in production. So with Skyl.ai Platform you can basically. Manage your ML projects in one place. And allows you to take your AI experiments to production in no time with scale and leads to faster model release iteration cycles. The best part doing all this with no infrastructure or MLops effort required.
- Exploring - Curious about it Planning - Creating AI/ML strategy Experimenting - Building proof of concepts Scaling up - Some departments are using it In production - Using it in product features Transforming - AI/Ml driven business
- "Artificial intelligence" is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving. Calling specific applications "artificial intelligence" is like calling a 2013 Honda Accord a "vehicle" – it's technically correct, but it doesn't cover any of the specifics. -AI is experimental in nature As mentioned earlier machine learning is different paradigm for writing software which involves observation and statistical based results which makes it experimental in nature. Treat Data as your source-code not just algorithm. Data plays a critical role in building out machine learning system as this data shall be fed into an ml algorithm. And output as well as outcome which includes accuracy and accuracy is purely dependent on the input data You can consider it as a garbage in and garbage out situation so it's very important.
- Machine Learning is all about continuous learning and iteration. In order to achieve accurate and fair results we need to continuously train our model which may include adding more unseen data or increasing the scope of the prediction
- Let us try to understand how machine learning can help you deliver business impact by exploring the opportunities it can provide. Let’s take a few examples to understand it better. How many of you are Netflix binge-watchers? I am sure you must have come across the recommendations for shows based on your content preference. Companies offering content or e- commerce companies selling thousands of products deal with a LOT of data. Machine learning makes it easy to offer personalization or recommendations based on individual preference and interest which otherwise wouldn’t have been possible. Think about another example. You are surrounded by a lot of machines - from AC to aeroplane. Our lives are dependent on functioning of these machines. So, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is. And together with reliability comes maintenance. With ML we can predict when a machine needs repair and maintenance and schedule these services to avoid any mishaps. One of ML’s biggest strengths is pattern recognition and prediction. Because systematic repetitions lead to patterns in even large and seemingly disparate forms of data. Let’s take an example of a chatbot that handles customer queries for an airline service . It can answer commonly asked questions like what is my flight’s departure time can be answered my a chatbot and for more complex questions it can be passed to human operators. This saves a lot of cost. Next, we will see problems that need building complex rules. Eg: Dynamic price of flights or ETA of a cab These can be solved by ML Last but not the least example involves unstructured and noisy data where Machine learning magic happens. With changing interactions that involve different formats like text, image, video, sound, Machine learning algorithms help in connecting the dots and give outcome.
- From salt to satellite, products and services today have moved online. Most basic needs such as food, shelter and clothing have transformed into a click away on a smartphone screen. Businesses seek the help of new-age tools and techniques to cater to the global and larger audience base which came along with the internet era. Here, AI steps in and train machines to help e-commerce businesses handle most of the mundane and repetitive tasks and add value to both the organization as well as the customer.
- Predict customer behaviour & offer recommendations to individuals based on their preference user level personalization can improve similar product recommendations. On the left hand side, we have a query product. (AI can make the process of searching for products and services on an e-commerce site easier for a user. Most of the time, this convenience is what customers look for in an online retailer as a differentiating factor. AI can recommend us products and help us to get this done by training algorithms to associate products with keywords. It turns the buying process efficient for the customer and drives sales for the business.)
- Ecommerce businesses thrive on the element of a wide variety of product and service options to choose from, which a traditional brick and mortar shop can’t provide. The business, at the same time, should make it easier for the consumer to find the product with minimal effort. Ever growing product portfolios make it very difficult to do this categorization manually. AI can categorize products automatically into predefined topics and help to automate this process as and when new products and categories get added.
- Ecommerce sites use product review pages as a tool for customers to engage with the platform and provide feedback. The website has no control over the way customers can respond to good or bad products, but it can choose which reviews to be displayed on site. It is important to make sure that no inappropriate language or content is posted on the product review page.
- Customers spend a lot of time in ineffective keyword searches for products that they wish to purchase, leading to reduced product discovery. E-commerce platforms implementing a visual search function, enables shoppers to take a photo or upload an image of an item of interest. AI analyzes the attributes of the item and can recommend similar products in their online and offline stores. This recommendation engine can be further enhanced using options to narrow down the search results by personal preference. AI ensures customers find exactly what they are looking for each time they visit the platform, greatly increasing sales revenue and opportunities to provide further product recommendations.
- Documentation analysis is one of the most time-consuming, yet most crucial processes in financial institutions. Employees spend a lot of time reading through physical and digital documents, and can still miss key information. Using Named Entity and Optical Character Recognition, AI models can assist in this analysis by automatically extracting relevant clauses and entities from Loan/Credit Agreements, Collateral Valuations Reports, Financial Leasing Contracts etc. This saves Banks hundreds of thousands in man hours, minimizes risk, uncovers hidden costs and maximizes revenue by diverting employee attention to more productive tasks.
- Regulatory changes have increased exponentially for the financial industry in the past decade. Compliance officers have to interpret tons of regulatory documentation manually and run the risk of making mistakes and oversights. Employing AI, banks can automatically curate regulatory content from financial, federal and state-level regulatory sources. Using Named Entity Recognition (NER), meaningful insight can be extracted quickly from this content, saving banks time and reducing manual resource costs. Banks can then align their policies and workflows to meet these regulations much more efficiently.
- Damage Assessment: Inspection is usually the first step in a damage insurance claims process, whether it’s an automobile, mobile phone or property. Assessing the damages to calculate an estimate of repair costs can be a challenging task for insurance providers. Deep Learning models can be used to detect the different types, area, and severity of damage with greater accuracy and automate the claims process. Property Risk Assessment: Traditional approaches to property risk assessment might not be able to capture the entire picture. Inspections for risk assessment can be assisted by Artificial Intelligence. The imagery of property and its surroundings can be utilized to determine the risk of future claims. Computer Vision technology helps to detect characteristics like fire hazards, gas leak chances, natural calamity risks, absence of safety features, poor upkeep and existing damages. Insurers can provide coverage to their clients based on this assessment.
- Deep Learning has demonstrated remarkable progress in image-recognition functions. Medical imaging is one of the most performed tasks using this technology. AI methods excel at recognizing complex patterns in the images and providing assessments of medical characteristics. AI models can be an effective tool for analyzing medical ailments like cardiovascular abnormalities, lung diseases like pneumonia, the development of tumors and melanoma, and checking for fractures from high-resolution medical imagery. This helps to provide timely treatments to patients.
- For healthcare services, analyzing electronic medical records is crucial in making the correct clinical decisions for their patients. A large amount of patient information is recorded in the form of free-text notes by physicians. Analyzing this unstructured text data is tedious, but using Natural Language Processing can automatically extract features or risk factors of patient health from these notes. Apart from clinical data, notes about patients’ emotional wellbeing and their speech transcripts can be analyzed to get insights about their mental health as well. AI extracts clinical information that would normally be missed using manual analysis methods.
- Manufacturers are applying Machine Learning to improve everyday processes and regulatory tasks in their factories. Product Quality, Safety of Workers and Workspaces can be boosted using Artificial Intelligence. As a result, operators and supervisors can prioritize other activities, that helps reduce resource needs and optimize cost.
- ⊚ Here we aim at reducing defective products leaving the process line using Computer Vision and algorithms on images captured through existing Automated Optical Inspection Images (AOI systems that leverage multi camera for Imaging). Manual inspection of products, parts, and components is a cumbersome and expensive task. Even a slight variance in material quality can make the entire production run defective. AI techniques that automatically detect early errors can help reduce material waste, repair and rework costs. Automated inspections, assisted by Computer Vision, uses multiple scanners and cameras for inspecting the manufacturing line. This ensures that only the highest quality items move onto the next manufacturing process.
- When an accident or workplace safety incident takes place, it is important to notify the concerned department as soon as possible. In large warehouses, it is difficult to take notice of all such cases through manual monitoring of CCTV footage alone. An AI system can detect such instances automatically and report it to concerned departments quicker than a human can. The ML model can be taught to identify workers fallen on the floor, unexpected breakdown of vehicles, crowding of workers, and blocking of surveillance equipment.
- In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
- Manufacturers are applying Machine Learning to improve everyday processes and regulatory tasks in their factories. Product Quality, Safety of Workers and Workspaces can be boosted using Artificial Intelligence. As a result, operators and supervisors can prioritize other activities, that helps reduce resource needs and optimize cost.
- Watermarks on property listing images are one way that competitors or third parties attempt to advertise on real estate platforms. Fraudulent agents may also upload images from disparate sources carrying their watermarks. It is a visual barrier, making listing photos less visually appealing and confusing buyers. AI, backed with computer vision, can be taught to comb through these images and detect watermarks much more efficiently and at a larger scale than a human reviewer. AI prevents real estate platforms from turning into a scam and spam-filled space and can prevent repeated offenders from creating new listings.
- In the real estate digital space, property listing images are aplenty. Oftentimes, buyers become frustrated at the lack of good quality images, and can quickly be turned off by examining inferior ones. Blurred, skewed, poorly-lit, digitally fabricated and duplicated images convey very little or misleading information about the property to the buyers. Real Estate platforms are leveraging AI in order to audit these images, detect images of poor quality, and retain the ones that provide a better viewing experience to the buyers. When it comes to the real estate market, property listing photos are aplenty. Oftentimes, buyers are frustrated at the lack of good quality images, and can quickly be turned off by examining inferior ones. Real Estate platforms are leveraging the use of AI in order to audit these images and provide an exceptional viewing experience to their prospects.
- Thank you Mohit and Shruti, for the wonderful presentation and demo. I’d like to mention that Skyl.ai is dedicated to helping people with their Machine Learning journey by offering consulting services. Services such as: AI Adoption Assessment, Skyl will help find key areas in your organisation where AI is beneficial. AI Systems Integration, Skyl will help find the best ways to integrate AI models with your current software systems AI Performance Evaluation, Skyl will assess your AI workflow and help find ways to improve your AI system’s performance And AI-Enabled Software Development, The team at Skyl can develop highly customized, AI-enabled software solutions catered towards your organisation’s needs. If you’d like to find out more, please check out the skyl.ai website or you can send an email directly to contact@skyl.ai.
- Skyl also has special offers for those of you that are curious about incorporating Machine Learning to your business. Skyl offers a free 1 month trial, plus Proof of Concept. You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model! Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
- Alright, now it’s Q&A time! As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen. We’ll try to answer as many questions as possible in the time that we have left. So let’s answer some questions. Sample questions: Shruti After building models with Skyl.ai, how do I use the models in my business? If I build a lot of models, how do I handle model deployment in that case? Mohit What kinds of ML problems can Skyl.ai solve? Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
- All right, so we have reached the end of the webinar. We hope you enjoyed it. We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming webinars as well Thank you for joining and I hope you have a wonderful day.