Wednesday, June 26, 2019

10 Steps to Adopting Artificial Intelligence in Your Business

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What does artificial intelligence (AI) in real-world business scenarios look like? Check out these tips for integrating machine learning, deep learning algorithms, and more into your existing products and services.


Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier's site to your web hosting service provider's support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definietely having its moment.
This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet, or even Tony Stark's Jarvis assistant. This AI plateau is happening under the surface, making our existing tech smarter and unlocking the power of all the data that enterprises collect. What that means: Widespread advancement in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) have made it easier than ever to bake an AI algorithm layer into your software or cloud platform.
For businesses, practical AI applications can manifest in all sorts of ways depending on your organizational needs and the business intelligence (BI) insights derived from the data you collect. Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets.
ML is playing a key role in the development of AI, noted Luke Tang, General Manager of TechCode's Global AI+ Accelerator program, which incubates AI startups and helps companies incorporate AI on top of their existing products and services.
"Right now, AI is being driven by all the recent progress in ML. There's no one single breakthrough you can point to, but the business value we can extract from ML now is off the charts," Tang said. "From the enterprise point of view, what's happening right now could disrupt some core corporate business processes around coordination and control: scheduling, resource allocation and reporting." Here we provide tips from some experts to explain the steps businesses can take to integrate AI in your organization and to ensure your implementation is a success.

1. Get Familiar With AI

Take the time to become familiar with what modern AI can do. The TechCode Accelerator offers its startups a wide array of resources through its partnerships with organizations such as Stanford University and corporations in the AI space. You should also take advantage of the wealth of online information and resources available to familiarize yourself with the basic concepts of AI. Tang recommends some of the remote workshops and online courses offered by organizations such as Udacity as easy ways to get started with AI and to increase your knowledge of areas such as ML and predictive analytics within your organization.
The following are a number of online resources (free and paid) that you can use to get started:

2. Identify the Problems You Want AI to Solve

Once you're up to speed on the basics, the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have in mind specific use cases in which AI could solve business problems or provide demonstrable value.
"When we're working with a company, we start with an overview of its key tech programs and problems. We want to be able to show it how natural language processing, image recognition, ML, etc. fit into those products, usually with a workshop of some sort with the management of the company," Tang explained. "The specifics always vary by industry. For example, if the company does video surveillance, it can capture a lot of value by adding ML to that process."

3. Prioritize Concrete Value

Next, you need to assess the potential business and financial value of the various possible AI implementations you've identified. It's easy to get lost in "pie in the sky" AI discussions, but Tang stressed the importance of tying your initiatives directly to business value.
"To prioritize, look at the dimensions of potential and feasibility and put them into a 2x2 matrix," Tang said. "This should help you prioritize based on near-term visibility and know what the financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives."
AI in Marketing Industry: 2020 Projection













4. Acknowledge the Internal Capability Gap

There's a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. Tang said a business should know what it's capable of and what it's not from a tech and business process perspective before launching into a full-blown AI implementation.
"Sometimes this can take a long time to do," Tang said. "Addressing your internal capability gap means identifying what you need to acquire and any processes that need to be internally evolved before you get going. Depending on the business, there may be existing projects or teams that can help do this organically for certain business units."
Artificial Intelligence AI

5. Bring In Experts and Set Up a Pilot Project

Once your business is ready from an organizational and tech standpoint, then it's time to start building and integrating. Tang said the most important factors here are to start small, have project goals in mind, and, most importantly, be aware of what you know and what you don't know about AI. This is where bringing in outside experts or AI consultants can be invaluable.
"You don't need a lot of time for a first project; usually for a pilot project, 2-3 months is a good range," Tang said. "You want to bring internal and external people together in a small team, maybe 4-5 people, and that tighter time frame will keep the team focused on straightforward goals. After the pilot is completed, you should be able to decide what the longer-term, more elaborate project will be and whether the value proposition makes sense for your business. It's also important that expertise from both sides—the people who know about the business and the people who know about AI—is merged on your pilot project team."
Predictive Analytics - What It Does and Why You Should Care

6. Form a Taskforce to Integrate Data

Tang noted that, before implementing ML into your business, you need to clean your data to make it ready to avoid a "garbage in, garbage out" scenario. "Internal corporate data is typically spread out in multiple data silos of different legacy systems, and may even be in the hands of different business groups with different priorities," Tang said. "Therefore, a very important step toward obtaining high-quality data is to form a cross-[business unit] taskforce, integrate different data sets together, and sort out inconsistencies so that the data is accurate and rich, with all the right dimensions required for ML."

7. Start Small

Begin applying AI to a small sample of your data rather than taking on too much too soon. "Start simple, use AI incrementally to prove value, collect feedback, and then expand accordingly," said Aaron Brauser, Vice President of Solutions Management at M*Modal, which offers natural language understanding (NLU) tech for health care organizations as well as an AI platform that integrates with electronic medical records (EMRs).
A specific type of data could be information on certain medical specialties. "Be selective in what the AI will be reading," said Dr. Gilan El Saadawi, Chief Medical Information Officer (CMIO) at M*Modal. "For example, pick a certain problem you want to solve, focus the AI on it, and give it a specific question to answer and not throw all the data at it."
Marketing and AI - Success Factors

8. Include Storage As Part of Your AI Plan

After you ramp up from a small sample of data, you'll need to consider the storage requirements to implement an AI solution, according to Philip Pokorny, Chief Technical Officer (CTO) at Penguin Computing, a company that offers high-performance computing (HPC), AI, and ML solutions.
"Improving algorithms is important to reaching research results. But without huge volumes of data to help build more accurate models, AI systems cannot improve enough to achieve your computing objectives," Pokorny wrote in a white paper entitled, "Critical Decisions: A Guide to Building the Complete Artificial Intelligence Solution Without Regrets." "That's why inclusion of fast, optimized storage should be considered at the start of AI system design."
In addition, you should optimize AI storage for data ingest, workflow, and modeling, he suggested. "Taking the time to review your options can have a huge, positive impact to how the system runs once its online," Pokorny added.

9. Incorporate AI as Part of Your Daily Tasks

With the additional insight and automation provided by AI, workers have a tool to make AI a part of their daily routine rather than something that replaces it, according to Dominic Wellington, Global IT Evangelist at Moogsoft, a provider of AI for IT operations (AIOps). "Some employees may be wary of technology that can affect their job, so introducing the solution as a way to augment their daily tasks is important," Wellington explained.
He added that companies should be transparent on how the tech works to resolve issues in a workflow. "This gives employees an 'under the hood' experience so that they can clearly visualize how AI augments their role rather than eliminating it," he said.

10. Build With Balance

When you're building an AI system, it requires a combination of meeting the needs of the tech as well as the research project, Pokorny explained. "The overarching consideration, even before starting to design an AI system, is that you should build the system with balance," Pokorny said. "This may sound obvious but, too often, AI systems are designed around specific aspects of how the team envisions achieving its research goals, without understanding the requirements and limitations of the hardware and software that would support the research. The result is a less-than-optimal, even dysfunctional, system that fails to achieve the desired goals."
To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. Security is an oft-overlooked component as well. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards -- encryption, virtual private networks (VPN), and anti-malware -- may not be enough.
"Similarly, you have to balance how the overall budget is spent to achieve research with the need to protect against power failure and other scenarios through redundancies," Pokorny said. "You may also need to build in flexibility to allow repurposing of hardware as user requirements change."
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Why Universities Need To Prepare Students For The New AI World

Artificial intelligence is increasingly embedded in our consumer and business lives, and it is poised to transform how societies function in the years to come. Yet universities are not adequately preparing students for a changing world. To better prepare students for a changing world, AI needs to be increasingly embedded into higher education.
Shutterstock
Shutterstock
For students, AI will inevitably impact their careers. Those interested in careers in AI could pursue a wide range of exciting new career possibilities focused on data science, machine learning or advanced statistics. And, even students not focused on AI would benefit from a sound education in artificial intelligence and familiarity with working with machines.
The AI era will inevitably create new job types, ranging from machine regulators to emotion engineers. To succeed, all students will need to understand, at least at a high level, how machines perform. In addition, they should better equip themselves to do what machines cannot do.
Moreover, traditional roles such as business analyst, sales, human resources and others will be augmented by AI, requiring a new degree of proficiency for front-line staff to interact with machines. Executives and managers will need to work with machines in strategic decision-making and problem-solving roles.
McKinsey predicts that AI will replace up to 800 million jobs by 2030. That’s a drastic reshaping of the workforce — and one that universities can and should help students prepare for. While some schools have world-class AI and technology programs, others have ample room for improvement.
Based on my observations as a Stanford University graduate now working for an AI company, Aera Technology, I’d like to share ideas on how universities can improve student readiness through curriculum requirements, projects with corporations and mentorship programs.
Curriculum Requirements
While it’s impossible for universities to keep up with the rate of change in the technology industry, I recommend that universities re-evaluate their core curriculums to ensure that students are prepared for the new age of employment that is approaching. I strongly advise all universities to incorporate computer science, entrepreneurship and social impact classes into curriculums.
By taking computer science classes, students will be able to understand the back-end systems that drive machines. At a minimum, a high-level understanding of machines is necessary to operate in an augmented workforce.
Shutterstock
Shutterstock
Entrepreneurship is another focus area, as continuous innovation has become the standard across industries. Entrepreneurship no longer solely drives startups, but it also drives large companies. For students to become leaders in the workplace, they should understand the principles of entrepreneurship and how they can innovate in any environment.
And social impact is more important than ever before. If you are going to build, sell or use AI, IoT or other technologies, you have to be able to understand what impact the technology will have on society. Inherently, by working in the world of technology, you are driving change. Technologists have a societal obligation to drive positive change as much as possible.
Projects with Corporations
One thing I am surprised I do not see at more universities are classes driven by projects with corporations. All students should have the opportunity to take classes that enable them to see how their academic curriculum is setting them up for their careers in the new machine-driven world.
With a complete reshaping of the workforce, getting out of the classroom is necessary for students to understand the emerging job opportunities. An increase in university-corporate projects would provide students with the experience required to determine which careers to pursue. These projects would help companies gain new perspectives and reflect positively on universities.
Mentorship Programs
In college, it is important for students to receive guidance from mentors. With the vast amount of opportunities available, students can become overwhelmed. Through the effective matching of mentors, they can gain insights into how to set up their careers, utilize technology and select the best employer.
Additionally, mentors can offer support throughout the ups and downs of choosing a career path in the new AI world. These programs should be fairly easy to set up since many university alumni want to give back to current students.

Of course, it also falls on students to conduct due diligence on which institutions are geared up to help undergraduates excel in a tech-driven future. Universities that embed AI learning and experience into the academic environment will benefit with positive reviews and successful alumni as AI transforms the job market.


Stephanie Glass Contributor
I am the head of product marketing for Aera Technology’s Artificial Intelligence solutions. My previous experience includes product management, marketing and business development roles at Anaplan, GoodData and Jive Software. Since 2015, I have also worked as an independent researcher focused on entrepreneurship and technology. I graduated from Stanford University with a B.A. in Science, Technology and Society.

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Artificial intelligence will transform universities. Here’s how

Artificial Intelligence (AI) is a technology whose time has come.
As AI surpasses human abilities in Go and poker – two decades after Deep Blue trounced chess grandmaster Garry Kasparov – it is seeping into our lives in ever more profound ways. It affects the way we search the web, receive medical advice and whether we receive finance from our banks.
The most innovative AI breakthroughs, and the companies that promote them – such as DeepMind, Magic Pony, Aysadi, Wolfram Alpha and Improbable – have their origins in universities. Now AI will transform universities.
We believe AI is a new scientific infrastructure for research and learning that universities will need to embrace and lead, otherwise they will become increasingly irrelevant and eventually redundant.
Through their own brilliant discoveries, universities have sown the seeds of their own disruption. How they respond to this AI revolution will profoundly reshape science, innovation, education – and society itself.
Deep Mind was created by three scientists, two of whom met while working at University College London. Demis Hassabis, one of Deep Mind’s founders, who has a PhD in cognitive neuroscience from UCL and has undertaken postdoctoral studies at MIT and Harvard, is one of many scientists convinced that AI and machine learning will improve the process of scientific discovery.
It is already eight years since scientists at the University of Aberystwyth created a robotic system that carried out an entire scientific process on its own: formulating hypotheses, designing and running experiments, analysing data, and deciding which experiments to run next.
Complex data sets
Applied in science, AI can autonomously create hypotheses, find unanticipated connections, and reduce the cost of gaining insights and the ability to be predictive.
AI is being used by publishers such as Reed Elsevier for automating systematic academic literature reviews, and can be used for checking plagiarism and misuse of statistics. Machine learning can potentially flag unethical behaviour in research projects prior to their publication.
AI can combine ideas across scientific boundaries. There are strong academic pressures to deepen intelligence within particular fields of knowledge, and machine learning helps facilitate the collision of different ideas, joining the dots of problems that need collaboration between disciplines.
As AI gets more powerful, it will not only combine knowledge and data as instructed, but will search for combinations autonomously. It can also assist collaboration between universities and external parties, such as between medical research and clinical practice in the health sector.
The implications of AI for university research extend beyond science and technology.
Philosophical questions
In a world where so many activities and decisions that were once undertaken by people will be replaced or augmented by machines, profound philosophical questions arise about what it means to be human. Computing pioneer Douglas Engelbert – whose inventions include the mouse, windows and cross-file editing – saw this in 1962 when he wrote of “augmenting human intellect”.
Expertise in fields such as psychology and ethics will need to be applied to thinking about how people can more rewardingly work alongside intelligent machines and systems.
Research is needed into the consequences of AI on the levels and quality of employment and the implications, for example, for public policy and management.
When it comes to AI in teaching and learning, many of the more routine academic tasks (and least rewarding for lecturers), such as grading assignments, can be automated. Chatbots, intelligent agents using natural language, are being developed by universities such as the Technical University of Berlin; these will answer questions from students to help plan their course of studies.
Virtual assistants can tutor and guide more personalized learning. As part of its Open Learning Initiative (OLI), Carnegie Mellon University has been working on AI-based cognitive tutors for a number of years. It found that its OLI statistics course, run with minimal instructor contact, resulted in comparable learning outcomes for students with fewer hours of study. In one course at the Georgia Institute of Technology, students could not tell the difference between feedback from a human being and a bot.
Global classroom
Mixed reality and computer vision can provide a high-fidelity, immersive environment to stimulate interest and understanding. Simulations and games technology encourage student engagement and enhance learning in ways that are more intuitive and adaptive. They can also engage students in co-developing knowledge, involving them more in university research activities. The technologies also allow people outside of the university and from across the globe to participate in scientific discovery through global classrooms and participative projects such as Galaxy Zoo.
As well as improving the quality of education, AI can make courses available to many more people. Previously access to education was limited by the size of the classroom. With developments such as Massive Open Online Courses (MOOCs) over the last five years, tens of thousands of people can learn about a wide range of university subjects.
It still remains the case, however, that much advanced learning, and its assessment, requires personal and subjective attention that cannot be automated. Technology has ‘flipped the classroom’, forcing universities to think about where we can add real value – such as personalised tuition, and more time with hands-on research, rather than traditional lectures.
Monitoring performance
University administrative processes will benefit from utilising AI on the vast amounts of data they produce during their research and teaching activities. This can be used to monitor performance against their missions, be it in research, education or promotion of diversity, and can be produced frequently to assist more responsive management. It can enhance the quality of performance league tables, which are often based on data with substantial time lags. It can allow faster and more efficient applicant selection.
AI allows the tracking of individual student performance, and universities such as Georgia State and Arizona State are using it to predict marks and indicate when interventions are needed to allow students to reach their full potential and prevent them from dropping out.

Such data analytics of students and staff raises weighty questions about how to respect privacy and confidentiality, that require judicious codes of practice.
The blockchain is being used to record grades and qualifications of students and staff in an immediately available and incorruptible format, helping prevent unethical behaviour, and could be combined with AI to provide new insights into student and career progression.
Universities will need to be attuned to the new opportunities AI produces for supporting multidisciplinarity. In research this will require creating new academic departments and jobs, with particular demands for data scientists. Curricula will need to be responsive, educating the scientists and technologists who are creating and using AI, and preparing students in fields as diverse as medicine, accounting, law and architecture, whose future work and careers will depend on how successfully they ally their skills with the capabilities of machines.
New curricula should allow for the unpredictable path of AI’s development, and should be based on deep understanding, not on the immediate demands of companies.
Addressing the consequences
Universities are the drivers of disruptive technological change, like AI and automation. It is the duty of universities to reflect on their broader social role, and create opportunities that will make society resilient to this disruption.
We must address the consequences of technological unemployment, and universities can help provide skills and opportunities for people whose jobs have been adversely affected.
There is stiff competition for people skilled in the development and use of AI, and universities see many of their talented staff attracted to work in the private sector. One of the most pressing AI challenges for universities is the need for them to develop better employment conditions and career opportunities to retain and incentivize their own AI workers. They need to create workplaces that are flexible, agile and responsive to interactions with external sources of ideas, and are open to the mixing of careers as people move between universities and business.
The fourth industrial revolution is profoundly affecting all elements of contemporary societies and economies. Unlike the previous revolutions, where the structure and organization of universities were relatively unaffected, the combinations of technologies in AI is likely to shake them to their core. The very concept of ‘deep learning’, central to progress in AI, clearly impinges on the purpose of universities, and may create new competition for them.
If done right, AI can augment and empower what universities already do; but continuing their missions of research, teaching and external engagement will require fundamental reassessment and transformation. Are universities up to the task?

Written by
Mark Dodgson, Professor of Innovation Studies, University of Queensland
David Gann , Vice President, Imperial College
The views expressed in this article are those of the author alone and not the World Economic Forum.

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Tuesday, June 25, 2019

Four Waves of Artificial Intelligence


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Saturday, June 8, 2019

CBSE Class 9 Artificial Intelligence Syllabus 2019-20

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AI PROJECTS

General Problems

• Employee Skills Management and Development Planning Tool
• Context Aware TV Program Recommender System for Android TV
• Context Aware Service Recommendation Engine for Mobile Phone
• Predicting User Intention for a given user Context
• Real time object recognition and context Aware Service recommending app for Self-Driving Car
• App to generate music genres and benefits of the given classical music. (Minimum 1000 Music Data Analytics)
• App for Predicting Energy Consumption of SDMIT College and Hostel Buildings
• Performance Evaluation of Student and Teacher of Engineering College
• Agriculture Data Analytics of Dakshina Kannada
• Health Care Data Analytics of Dakshina Kannada
• Natural Resources Data Analytics of Dakshina Kannada
• Waste Data Analytics of specific Location
• App for Object Classification and Analytics in Retail Stores
• Event Detection and Notification App for CCTV mounted in Specific Location.
• Cloud based customer data analytics for the success of a given business (Customer behavior, satisfaction, etc)
• Chatbot for Campus Interview
• Chatbot for Tourism
• Chatbot for Customer Service
• Teaching Assistant Chatbot
• Sentimental Analysis of Colleges in Dakshina Kannada through Social Media Data.
• Prediction and diagnosis of any specific Diseases using multimodal data (Scans, sensor, audio data ,etc)
• App for Handwritten and Character based user Recognition
• Customer focused Ecommerce Site with AI Bot for Retail Shopping (Any Specific type of Shopping) using multimodal data (mobile, social media, location ,context, etc)
• App to Predict the suitable Skilled candidate for job through CV analysis for a Company.
• App for Data Analytics of Medicinal Plants in Dakshina Kannada
• Prediction and diagnosis of Agricultural Crop Disease ( Any one )
• User Context based Search topic recommender for search engine
• Waste Management in College Campus
• Personal Intelligent Virtual Assistants
• Context Aware Intruder/attacker/Hacker detecting system
• Sports Data Analytics (Fitness and Sports Skills assessment for identifying next talent) – For any specific game and country /state
• Financial Advice for Public
• Hacker detecting Tool
• Fraudulent Activity (Anomalies) Recognition in Shopping,


Business and Banking Transactions
• Customize crop growing techniques specific to individual plot characteristics and relevant realtime data (Optimize Pricing, Predict yield, Predict new high value crop, Predict product demand and Product Optimization , Forecasting ,etc)
• Energy Data Analytics and Predicting Energy Demand Trends in given location using multimodal data
• Evaluating the Quality of hospitals and data performance in the specific region
• Predicting the risk of illicit activity or terrorism using historical crime data, intelligence data and other available sources for a given location
• Shopping Centers Data Analytics of Dakshina Kannada
• Simulation model of the operation of the limit order book underlying a virtual currency exchange
• IoT based Home Automation 48. IoT based Smart Irrigation System
• AI based Robot (For any suitable Case Study)
• AI based Self Driving Toy Car

HEALTH CARE

• Drug discovery using Neural Networks
• Tumor detection from Brain MRI images
• Detection and Classification of cancer cells in MRI Images
• Organ Segmentation and Labelling in MRI Images
• Cancer cell detection and segmentation
• Blood flow detection and monitoring using Sensory data
• Diabetic Retinopathy Detection and Segmentation from MRI Images
• Personalized Treatment based on Patient History
• AI System for Prediction and Recommendation of Diabetes
• Recommendation of doctors and medicines using review mining
• Disease Prediction using patient treatment history and health data
• Real-time health monitoring using wearable devices
• Prediction of epidemic outbreaks using Social Media Data
• Design and Implementation of prediction for Medical Insurance
• Activity monitoring and unusual activity detection for elderly homes
• Recognizing exercises for physiotherapy videos
• Detecting Genes responsible for cancer development
• DNA/Gene classification using RNN Sequential analysis


AGRICULTURE

• Plant disease identification using leaf images
• Plants Recognition using Convolutional Neural Networks
• Fruits counting for automatic inventory management
• Weed plant detection from agricultural field images
• Predicting yield, soil moisture and weather using images processing
• Plant Gene Classification and Functionality Prediction
• Automated quality assessment of crops

Space Research & Satellite imagery
• Change detection for deforestation, water reserves from Satellite images
• Detection of Unmanned Vehicles (UMV) and Drones
• Segmenting Satellite Images for detection of road, buildings, natural resources etc.
• Target recognition in SAR images
• Scene segmentation in rural and urban regions from Remote Sensing Data
• Detection of Anomaly in SAR images
• Classification of Terrain from Satellite Imagery
• 3D reconstruction from multimodal satellite data
• Classification of galaxies
• Simulation of Galaxies for Real World Scenario
• Detection and Segmentation of different structures on planet surface images

Cyber Security

• Intrusion detection in networks and servers
• Malware identification using deep learning
• Anomaly detection in network activities
• Virus/Malicious file detection in a shared environment
• Spam SMS filtering Using Machine Learning
• Advertisement Click Fraud Detection
• Webpage classification for safer browsing

Education
• Predicting Student Performance using Regression analysis
• Automatic scientific article summarization
• Feature based opinion mining on student feedback
• Face recognition based attendance system


Video Processing

• Real-time generic object detection & tracking
• Pedestrian Detection from low-resolution videos
• Detection and classification of vehicles
• Vehicle detection and speed tracking
• Detection of signals, and lane for self-driving cars
• Road Crack Detection and Segmentation For Autonomous Driving
• Unusual Activity & Anomaly detection in surveillance
• Human activity detection for surveillance video Compression
• Gesture recognition for Human Computer Interaction
• Real-time video to text transcription for visually challenged person
• Real-time speech recognition for regional languages
• Real-time OCR for Regional Languages
• Face Recognition & expression recognition Mobile App for Visually impaired person
• Action recognition for controlling electronic appliances in homes
• Place recognition app for visually impaired person
• Text to video generation for News Stories
• Salient region detection for targeted advertisements placement in videos
• Video quality Enhancement using super-resolution


Business

• Comparative sales analysis of different stores, customers, demographics
• Customer Classification Based on The Historical Purchase Data
• Personalized marketing and targeted advertising
• Predicting housing prices for real estate companies using Machine Learning or Deep Learning
• Predicting Product Development Time and Cost Using Production Data
• Question answering system for automated customer relationship management
• Sales prediction using Regression Analysis
• Text to Speech Generation for Regional Languages




Insurance

• Fraud/abuse detection for insurance companies
• Predicting risk for new Insurance using customer information


Banking

• Credit card fraud detection using historical transaction data
• Loan Risk Prediction using User transaction information

Crime

• Crime pattern detection using historical crime data
• Geographical crime rate prediction
• Criminal behavior analysis and segmentation
• Crowd counting and monitoring for surveillance videos

Social Media Analytics

• Product opinion mining for competitive market analysis
• Customer requirement analysis using User Generated Content
• Consumer behavior analysis using User Generated Content
• Rumor detection from Social Media
• Political opinion mining for popularity prediction
• Terrorism detection from social media
• Stock prediction using Twitter sentimental analysis
• Restaurant Review Classification And Recommender System
• Fake news detection in online social media
• Detection violent and abusive content in social media

Miscellaneous1

• Automated Machine Translation for Regional Languages
• Virtual Personal Assistant Apps
• Developing a Chatbot using sequence modelling
• Travel route suggestion based on pattern of travel and difficulties

Miscellaneous2

Detecting incidents of cyber bullying

• Input: text feed from social media conversations
• Output: cyber bullying victim and bully identified

Characterizing mental stress and suicidal tendencies

• Input: text feed from online profiles and conversations
• Output: people suspected to have stress or suicidal tendencies are flagged

Detecting click-fraud in online advertising

• Input: click data from online advertisement
• Output: fraudulent clicks and click patterns detected

Detecting fake news in online news media

• Input: news feed from online media
• Output: fake news, rumours, clickbait characterised

Identifying hate crime in online media

• Input: text feed from online conversations
• Output: hate speech, offensive comments, racist comments etc. detected

Malware identification

• Input: executable files (.exe) of several software/apps
• Output: malicious software/apps identified

Intrusion detection in enterprise networks
• Input: network logs from router/switches of enterprise networks
• Output: possible intrusions, botnet activity, DDoS activity, etc. flagged

SMS/IM spam filtering

• Input: messages from SMS or IM apps (WhatsApp, Line, etc.)
• Output: spam messages filtered

Detection of malicious URLs

• Input: Visited URLs
• Output: malicious URLs (hosting exploit kits, malware, etc.) are detected

Detecting phishing websites

• Input: website URL
• Output: phishing websites flagged

Robotics and Automation

• Detecting shapes of common and uncommon objects
• Determining size of boxes
• Personalized greetings
• Optimal path traversal
• Detecting dangerous objects at public places
• Automating indoor weather adjustments
• Detecting flawed packaging
• Monitoring for thieves and intruders



Agriculture

Predicting the crop based upon the soil

Input: images of the soil and climate information
Output: Predicting the suitable crop

Damage assessment of crops because of the bad weather conditions
• Input: images of the crop before and after the damage
• Output: Prediction of damage level and crop insurance

Automatic Health inspection

• Input: Multiple images of a plant.
• Output: Prediction of health of the plant and possible medicines

Automatic soil testing using AI

• Input: Images of the soil
• Output soil health

Weather and crop-based irrigation system.

• Input: Weather condition and crop information (crop age, crop type)
• Output: Irrigation is required or not

Assessment of grain of production

• Input: crop image of the whole field
• Output: prediction of quantity of grain production

Disease Detection in the plants

• Input: image of the plant
• Output: disease and pesticide recommendation

Recommendation of crop based upon the crop history

• Input: crop history
• Output: Recommended crop with fertilizer

Chatter bot for farmers

• Input: Knowledge base of the crops in the regional language.
• Output: Required information through chat bot

Crop Waste management

• Input: Images of the waste
• Output: Companies and their contacts where these wastes are useful.




Social Media

Suggesting Engaging Content for Social Media
• Input: Browsing History and previously watched content
• Output: Customized Content
AI-Powered Image Recognizers from Social Media
• Input: Image of the target (Person)
• Output: Identified person
Smart Messenger Bots by understanding the personality
• Input: Social Media feed of the person
• Output: Messenger bot reply based upon his personality.
Better career and Job Suggestions
• Input: Social Media and LinkedIn feed of the person
• Output: Understanding the skills and job suggestion
Reaching the right audience
• Input: Understanding the search history in social media websites
• Output: Better Product suggestions.
Understanding the content on the social media websites and predicting the possible violence
• Input: Social Media feed
• Output: Predicting the location and probability of violence and prepare accordingly.
Understanding the personality and probability of joining terrorist groups
• Input: Social Media feed and search history
• Output: Predicting a person who might join terrorist group or who is an easy target which can be influenced
Understanding the stress level of students
• Input: Social Media feed
• Output: Predicting the stress level of students and based upon stress level give them consultation.
Predicting the fake locations on social Media
• Input: Image uploaded by user
• Output: Predicting exact location
Predicting the molesters and eve-teasers
• Input: Social Media feed and history
• Output: Predicting the behaviour of a person.
• Healthcare
Physiotherapy exercise monitoring application
• Input: Real time video of user doing physiotherapy exercise
• Output: recognized exercise and its counting, detection of wrong exercise positions
Health monitoring application using wearable devices
• Input: Heart beat rate, blood pressure, motion data from wearable devices
• Output: statistics of average, alerts of health problems
Skin disease detection app
• Input: Photos of skin surface taken in a mobile camera
• Output: Recognized skin disease and its severity
Detecting face features, hair loss, wrinkles, pimples
• Input: face photos of users taken from mobile camera
• Output: Detection of hair loss, wrinkles, pimples and their counts etc
Food recognition and calorie, vitamin detection
• Input: Food phots taken from mobile camera
• Output: Recognized food and its calorie and vitamins
Finger print based disease detection
• Input: Finger print data taken from mobile device
• Output: Detected disease or health status of the user
X-Ray image description app
• Input: Photo of X-Ray taken from the mobile app
• Output: Description and results about the X-Ray and detection of disease or health issue
Assistive app for autism, Parkinson, Alzheimer diseases
• Input: images/video/audio containing sounds/gestures of the user
• Output: assistance, helps, recommendations to the user
Doctor and medical shop recommendation app
• Input: Disease/symptoms and current location of the user, social media, review data
• Output: Recommendation of doctors/hospitals and medical shops, route to the locations
Walking pattern monitoring for arthritis
• Input: Real time video of user walking
• Output: Analysis of walking pattern and further recommendation for improvement

Crowd

Finding people who are lost
• Input: Photo of the lost person and crowd images/videos
• Output: Detections of the lost people
Head counting application
• Input: Photo or video taken from mobile camera
• Output: Number of human heads in the image i.e. people count
Identifying objectionable persons
• Input: Photo or video taken from a mobile or surveillance camera in a highly crowded environment
• Output: Detection of the objectionable person in the crowd images
Weapon detection from crowded environment
• Input: Photo or video taken from a mobile or surveillance camera in a highly crowded environment
• Output: Detections of weapons, type and person who carry
Identifying people group in a curfew/ section 144
• Input: Photo or video taken from a mobile or surveillance camera in a highly crowded environment
• Output: Detections of group of people and their counting and further alerting
Detecting persons with deviated yoga or dance pattern
• Input: Real time video taken from mobile or surveillance camera in a building
• Output: people with different pattern of dance or yoga moves differing from the crowd
Identifying people with unique outfit/getup
• Input: Photo or video taken from a mobile in a highly crowded environment
• Output: individuals with unique outfit/makeup/gesture
Unusual activity, loitering detection in Mall
• Input: Real time video stream from surveillance camera in Malls
• Output: Individuals who are loitering in the Mall having random and suspicious waling patterns
Detecting smokers in no smoking areas
• Input: Real time video stream from surveillance camera
• Output: individuals who violate the no-smoking rule in the public/private places
Counting animals/birds in a farm land or open area
• Input: Real time video stream from drone camera or mobile
• Output: number of total animals/birds in a group category wise

Entertainment

Generating video from photo Gallery of a mobile
• Input: Photos of user from the camera
• Output: Video containing the photos presented in an interesting way with animation and music
Hands-free mobile control using frontal camera app
• Input: Face gestures fed through the frontal camera of a mobile
• Output: Actions on the mobile such as clicks, swipe, long press etc
Face morphing with Indian cultural face make-ups
• Input: Face photo/video taken from the frontal camera of a mobile
• Output: Morphed faces with make-up of Indian regional cultures
Real-time video player for streaming/playing low resolution videos
• Input: Low resolution videos of 144p or 180p
• Output: High resolution videos of 720p or 1080p
Fast painting style transfer app to selfies and other photos
• Input: Photos taken from frontal or back camera
• Output: Stylised painting like photo based on different painting styles
Singing synchronization app for mixing user voice with music
• Input: Audio of song sung by the user
• Output: Mixed song with background music with voice from user audio
Augmented Reality app for animating mobile videos
• Input: Videos taken by the users
• Output: Animated videos with animated 3D characters within it
Personalized music recommendation app for mobile
• Input: song playlist history, likes and dislikes of the user
• Output: Song recommendations based on user interest, mood and timing
Chatbot for stress buster
• Input: User chat comments/questions
• Output: Replies comments that will relieve the stress of the user
Video summarization app for mobile users
• Input: Lengthy video from the user
• Output: Short summary video containing interesting segments


Space Research

Stars recognition using mobile apps
• Input: Sky photos taken from mobile camera
• Output: Labelled Stars/star groups on the photos
Environment conditions detection using mobile camera apps
• Input: Outdoor photos taken from mobile phones
• Output: Air pollution level, cloud, lighting information detected automatically
Traffic control application using satellite images
• Input: Satellite images of roads
• Output: Traffic congestion detection results and recommendations
Detecting popularity of a business venue using satellite images of parking lot
• Input: Satellite images of parking lots
• Output: popularity level of that business venue
Satellite farming using remote sensing images/ drone images
• Input: Satellite images or drone images of farm lands
• Output: Monitoring, inventory estimation, yield prediction, strategies & plans for farming
Automated drone navigation system
• Input: Real time video feed from the drone camera
• Output: Navigation actions and real time physical motion to the target locations
UMV or Drone detection system for border security
• Input: Real time video feed from HQ surveillance cameras
• Output: detections of UMV or drones and their locations

Location recognition apps from Airplanes
• Input: Photos of land taken from airplanes
• Output: Recognized places and their information
Drone based security system
• Input: Real time video stream from drone cameras
• Output: Detected objects, people, animals, activities, accidents, intruders etc.
3D reconstruction of a building or land using Drone cameras
• Input: Aerial video taken from drone camera
• Output: 3D model of the location/buildings
Business
Chatbot development for regional languages
• Input: Chat commands written in regional languages
• Output: Automated responses in regional language
Robust face recognition system for loan/insurance fraud prediction
• Input: Face photos and related information of a loan applicant
• Output: Detection whether specific applicant has committed loan/insurance fraud
Question answering system for automated customer relationship management
• Input: Questions from customers spoken/written in regional languages
• Output: Answers (spoken/written) from the automated system in regional language
Face emotion detection for customer relationship management
• Input: Real-time video of customer in a services/customer care place
• Output: Detections of user emotion such as stress, happy for guidance to the service provide or customer care responder
Salient region detection for targeted advertisements placement
• Input: Image or streaming video of sports/movie etc.
• Output: Location inside video frame where ad will be posted
Customer emotion detection for telephony customer care
• Input: Real-time audio of the customer care call
• Output: Detections of user emotion such as stress, happy for guidance to the customer care responder
Product requirement analysis from social media
• Input: comments, reviews from users of particular topic or need
• Output: Detections of whether particular feature or product is currently needed for the customers
Scheduling and planning apps for sales person
• Input: Schedule, target, location of the sales person
• Output: Reminders, route recommendations, plans for sales execution
Mobile app for quick prediction of production time and cost
• Input: Requested number of quantity and specification of a product
• Output: Production time and cost to make the specific number of products
Work monitoring system for surveillance videos in production environment
• Input: Real time video feed from surveillance cameras in a product production environment
• Output: Detection of events, accidents, people activities, loitering etc


Journalism
News article summarization app
• Input: News article in text format
• Output: Summary of news as a short text
News text to video generation app
• Input: News article with text and images
• Output: Interestingly presented video with news elements, animations and attractive audio
Fake news alert app
• Input: News article with text and images
• Output: Detection results whether article is fake or from trustable source
Provocative article detection for safe surfing
• Input: News article with text and images
• Output: Detection results whether article contain controversial/violent content against religious views/national integrity that will induce violence or riot
Finding famous and relevant Tweets of news articles
• Input: News article with text and images
• Output: Neatly presented famous tweets from celebrities/active twitter users on specific issues that news article deals with
Personalized News Recommendation App
• Input: News articles, previous history of user, ratings etc
• Output: News articles matching interest and history of the user
Multisource news summarization for summarizing news on same topic
• Input: Multiple news articles dealing with same news
• Output: Summary of news content as a short text
User emotion detection for news article impact analysis
• Input: News article, face images of the user while reading news, history of articles read by user
• Output: Prediction of emotions of a user for different articles

News popularity detection in social media
• Input: News article and its relevant social media feed
• Output: Popularity level of a news story

News generation from tweets of certain topic
• Input: Twitter feed related to certain event or topic
• Output: Generated news story related to the famous tweets

Reference : https://www.leadingindia.ai/projects