#EDGETalks: Actively Engaged – Leadership and Innovation in Building Employee Engagement

This post is based on a panel discussion held on June 4, 2018. The 90-minute session focused on panellists’ experiences shaping culture and fostering engagement in their companies. Moderated by Darshan Jain, Head Technology and Operations of The Burnie Group, Norman Bacal, author and former managing partner, Heenan Blaikie LLP delivered the keynote and the panellists were Richard Anton, Senior Vice President and Chief Operations Officer at CIBC Mellon, Cathie Brow, Senior Vice President, Human Resources and Communications, Revera, Christina McClung, Vice President, Human Resources and Chief of Staff, Capital One, and Rob Lokinger, President and Chief Operating Officer, AppCentrica Inc.

#EDGETalks: Actively Engaged – Leadership and Innovation in Building Employee Engagement  Many companies hire the best and the brightest to seize new opportunities and increase profits. Unfortunately, impressive résumés don’t always translate to an engaged employee base or a stimulating and innovative workplace culture. Individual contributors who once brimmed with enthusiasm and new ideas now only raise their heads to check the time. Regardless of systems put in place or reorganizations, teams struggle to get ahead.

Culture and engagement can be forgotten or an afterthought when setting and executing corporate strategies. Leaders should consider the mindsets and behaviours needed to support their company’s vision and goals.

 

How does culture fit within your corporate strategy?

You need to define the workplace culture required for teams to meet targets and create new opportunities. Whether team-centric, focused on high potentials, honed on improving shareholder value, (etc.), a culture strategy needs to be determined as well as its supporting tactics.

“You need to decide what your cultural imperative is, as part of your corporate strategy,” says Norman Bacal. “Once you understand what it is, it ought to put you in the direction of your tactics, day-to-day behaviours, and ultimately whom you recruit to your vision.”

Bacal offers three pieces of advice for leaders looking to set, change or improve corporate culture:

1. Be consistent

Policies, programs, and behaviours must align with culture vision and not vary across your organization regardless of geography or environment. Employee trust grows when words and actions align. If you, your peers, or other leaders say one thing and do another, you risk damaging your and the company’s credibility.

“Never confuse strategy with tactics. If you take those tactics and separate them from your cultural vision, they won’t work. In fact, they do the opposite of what you want and can build a level of cynicism, because you need to be consistent between your vision and execution.”

2. Recognize the importance of your front-line staff on a regular basis

#EDGETalks: Actively Engaged – Leadership and Innovation in Building Employee Engagement  It’s easy to focus attention on only senior management or those with “star” quality. In fact, it’s the public’s or client’s first point of contact—receptionists, service agents, or call centre employees—who can be the linchpin to your organization’s success. They are often your company’s face and voice and some of your most valuable employees. Telling them you recognize this signals you understand their role and you appreciate what they do.

“I’d arrive in the morning and say to the receptionist, ‘You’re the most important person in this firm.’ If you say that once to somebody, they won’t believe you; if you say that to them on a regular basis, they begin to believe it.”

3. Walk the halls

It’s unlikely your staff will proactively tell you what’s happening or their collective mood. The best way to know these is to step outside your office and talk to employees. Have informal chats—saying “hello” and finding out how they’re doing or how their family is will help build goodwill and trust. Ask your leaders to do the same.

“It’s the small things you may consider completely insignificant to your life that have a huge impact on other people’s lives.”

Engaging your employees while building corporate culture

We know a strong corporate culture can be a competitive advantage when attracting employees or securing clients. When a company decides to define or redefine their culture, change doesn’t occur overnight: it takes time to learn and develop traits and behaviours. While organizations can launch campaigns focused on ethics, teamwork, or client-centric service, successful shifts often happen when leaders commit to and model desired behaviours and attitudes.

Who are engaged employees?

Engaged employees go above and beyond so the company realizes its corporate vision and strategies. Working isn’t “just a job” or a paycheque. They are front and centre when needed most. As individuals, they proactively update their skillset to be part of the organization’s future. They are active problem solvers and offer ideas to help shape the company.

“I really think engagement has to do with people’s passion and enthusiasm. We have a really great vision for our company that touches everybody. Employees need to feel connected—if they aren’t, they’re not going to be able to deliver the service we expect from them.”

~ Cathie Brow, Senior Vice President, Human Resources and Communications, Revera

How do you build employee engagement?

Smart strategies and tactics build, maintain, and grow engagement over time. They are rolled out at the corporate level and supported by leaders.

Corporate tactics

Your corporate values should be defined, so everyone understands what they are and how to bring them life. You need to ensure all levels, especially C-suite and other senior leaders, walk the talk. Otherwise, employees will see the disconnection and may assume a double standard.

Find different ways to involve employees in corporate programs. Corporate Social Responsibility projects (such as Habitat for Humanity builds, sorting food bank donations, or registering teams for a charity bike ride) or internal problem-solving competitions for specific issues (ranging from hackathons to projects resolving client pain points) are more than team-building exercises: They reinforce company values and allow staff to participate in corporate projects in fun, meaningful, and non-financial ways.

Take the time to listen to your employees and don’t immediately jump to prescribing remedies. Instead, ask your employees for their ideas and implement solutions that need little lead-time (before putting into place more complex ideas). This way, you signal you hear your staff. Similarly, staff input when setting up a formal recognition program is important—don’t assume a gala or dinner with the CEO would appeal to the majority.

“Alignment around the right goals and cultural imperators yields great benefits. It carries through when people interact with each other and with customers. We founded our company on four principles that really defined our culture. They’re used to make our hiring decisions, evaluate people and make sure we have the right approach within our organization.”

~ Rob Lokinger, President and Chief Operating Officer, AppCentrica Inc.

Individual leaders’ tactics

Your behaviour, attitude, and presence go a long way in shaping corporate culture. Sit with your team to get a feel for their day-to-day environment and issues. Be seen. Have informal chats with specialists and coordinators as well as more senior staff at their desks or in the cafeteria. Encourage peers and people leaders under you to do the same.

Trust in leadership is essential. Employees want to see the genuine you. Façades won’t gain their trust and may make you harder to follow. Your actions need to be consistent, and you must deliver on your commitments.

Celebrate team wins, but also find ways that are personal to you to congratulate or acknowledge staff accomplishments. Equally crucial is being there and supporting your staff in challenging times.

“My role is about fostering the kind of culture and principles we want. It’s about how I handle myself every single day, and also how I expect my management team to handle themselves. I am a firm believer that if I display those characteristics, those traits across the organization, that’s when people start to buy into that idea that it’s not more than a one-off that’s quickly forgotten.”

~ Richard Anton, Senior Vice President and Chief Operations Officer at CIBC Mellon

 

How do you measure culture?

#EDGETalks: Actively Engaged – Leadership and Innovation in Building Employee Engagement  Annual and biannual employee satisfaction and sentiment surveys may not be helpful because they are lagging indicators. Instead, get timely feedback by measuring employee experience after critical points in their tenure: onboarding, first three months, performance reviews (etc.). Ask questions about diversity and inclusion, and track indicators such as employee referrals and attrition rates.

“It’s hard to measure feelings, but we try. I think there’s a lot to be said about the anecdotal feedback—look at what you might measure. I think some measurements that can be found along with the survey scores. Make sure you deep dive into topics where people are feeling engaged and the various contributors, such as enablement to getting work done.”

~ Christina McClung, Vice President, Human Resources and Chief of Staff, Capital One

Working with an experienced partner can help build and improve your employee engagement. Choose a partner who can efficiently lead the project, keep it on track, and who will develop your internal capabilities. The Burnie Group will help you to set the right strategy and build the right foundation. Contact us to learn more about employee engagement.

 


#EDGETalks: Actively Engaged – Leadership and Innovation in Building Employee Engagement


The Most Unusual Uses of Artificial Intelligence

Artificial intelligence (AI) and intertwined concepts such as machine learning and predictive modelling have become indispensable in modern industries. It is often estimated that by 2030, AI will contribute up to $15.7 trillion to the global economy.  AI has the potential to transform a wide number of industries. All over the world, AI is helping people do their jobs more effectively, from doctors who diagnose sepsis in patients to scientists who track endangered animals in the wild. In this article, we explore some of the more unusual uses of AI.

Rather than creating ominous issues for humankind, AI is helping people around the world do their jobs more effectively, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild.

Below are some of the most unusual uses of AI that provide value to our society and go beyond their traditional and widely applied usages across industries.

 

Helping People

Rescue Missions

The Most Unusual Uses of Artificial Intelligence  AI technology is helping first responders find victims of earthquakes, floods and other natural disasters.

Normally, responders need to examine aerial footage to determine where people could be stranded. However, examining a vast amount of photos and drone footage is very time and labour intensive; this is a problem as time is a critical factor for victims’ survival.

AI developed at Texas A&M University permits computer programmers to write basic algorithms that can examine extensive footage and find missing people in less than two hours.

 

Diagnosing Sepsis

The Most Unusual Uses of Artificial Intelligence  Sepsis is a potentially life-threatening complication of an infection, but it is treatable if identified promptly. When not identified in time, patients can experience organ failure or even death. Today, AI algorithms that analyse electronic medical records data can help physicians diagnose sepsis an average of 24 hours earlier than previously used methods, according to the Johns Hopkins Whiting School of Engineering. The AI system, called Targeted Real-Time Early Warning System (TREWScore) can also be used to monitor other conditions, including diabetes and high blood pressure.

 

Better Surgeries and Prosthetics

The Most Unusual Uses of Artificial Intelligence  Surgical robotics today are machine learning-enabled tools that provide doctors with extended precision and control. These robots enable shortening the patients’ hospital stay, positively affecting the surgical experience, and reducing medical costs.

Mind-controlled robotic arms and brain chip implants have begun helping paralyzed patients regain not only mobility but also sensations of touch. Machine learning and AI are further helping these technologies improve the patient experience.

 

Earth and Wildlife

Robot Bees

The Most Unusual Uses of Artificial Intelligence  Bees are indispensable to crop pollination, however, they are very susceptible to pesticides, diseases, and other environmental concerns that lead to their fragile populations dwindling. To ensure that these concerns do not lead to famine, researchers have developed a robot bee drone that incorporates artificial intelligence, GPS, and a high-resolution camera to pollinate in a manner similar to honeybees.

 

Tracking Wildlife Populations

The Most Unusual Uses of Artificial Intelligence  Applications like iNaturalist and eBirds, that collect data from vast circles of experts on the species encountered, are helping to keep track of species populations, favourable ecosystems, and migration patterns. These applications also have an important role in the better identification and protection of marine and freshwater ecosystems.

 

Wildlife Poaching Prevention

The Most Unusual Uses of Artificial Intelligence  Wildlife poaching is a global problem as species get hunted toward extinction. For example, the latest African census showed a 30 per cent decline in elephant populations between 2007 and 2014. Wildlife conservation areas have been established to protect these species from poachers, and these areas are protected by park rangers. The rangers, however, do not always have the resources to patrol the vast areas efficiently. Predictive modelling has been used and tested in Uganda’s Queen Elizabeth National Park to predict poaching threat levels. Such models can be used to generate efficient and feasible patrol routes for the park rangers.

 

Smart Agriculture

The Most Unusual Uses of Artificial Intelligence  Neural networks are starting to be used to deliver smart agricultural solutions. Besides the use of both artificial and bio-sensor driven algorithms to provide a complete monitoring of the soil and crop yield, there are technologies that can be used to provide predictive analytic models to track and predict various factors and variables that could affect future yields.

For example, Berlin-based agricultural tech startup PEAT has developed a deep learning algorithm-based application called Plantix that can identify defects and nutrient deficiencies in the soil. Their algorithms correlate particular foliage patterns with certain soil defects, plant pests, and diseases.

 


The Most Unusual Uses of Artificial Intelligence


 By: Jenya Doudareva, Senior Associate

Understanding AI and Machine Learning

The terms artificial intelligence and machine learning have gained a lot of hype in the news lately, and a lot of articles seem to use both terms interchangeably, even though they are different. The aim of this article is to make more sense of all the technical jargon out there.

The term artificial intelligence (AI) is derived from the phrase, “man-made ability to learn and understand.” This means AI is a broad brand association name which covers all things man-made with respect to the human ability to understand and disseminate information. Data, which is unprocessed information, is the foundation to which all man-made systems learn, hence the rise of the term, “big data.”

Now to data science. The hottest and sexiest job of 2018, as proclaimed by The New York Times, is the science of understanding and interpreting data into meaningful and usable information. The bulk duty performed by a data scientist is called data wrangling, and this means taking the data and trying to find meanings in it. During data wrangling, data is cleansed, regrouped, transformed, and tested with various hypothetical assumptions to better understand the true composition of the data. This data wrangling is required because most machine algorithms are sensitive to outliers, non-normal data distribution, and unscaled or untransformed data input.

A product or service can be said to be powered by AI if the product/service is comprised of a learning path, where the product/service improves or tries to improve over time. The more you use the product /service, the smarter and more optimized the product becomes (e.g. the Nest home thermostat). Therefore, an equation can be formed to represent what AI truly means:

P = programming to get data

I = information from data

(S\NN) = machine learning algorithm (including neural networks)

O = programming to interpret output to client

FB = feedback to data stage to improve model (this can be periodical or instant, depending on the nature of the product or services).

AI = P + I + FB + (S\NN) + O

Where FB starts with initial value as 0

What about predictive modelling? Well, predictive modelling, or analysis, simply means trying to predict the possible outcomes of a scenario or equation. So, yes, machine learning falls under predictive analysis/modelling, but it also falls under non-linear, linear, and integer programming; Monte-Carlos simulations; etc.

The key difference between the mentioned techniques and machine learning is the presence of an objective function. This objective function is an interpretation of the problem at hand. This is the reason why machine learning algorithms have become very popular these days; it gets you the objective functions to any given dataset.

Understanding AI and Machine Learning

Machine Learning Scope

There are three main scopes for machine learning;

  1. Supervised Learning:

This is the most popular type of machine learning scope. It involves using a labeled data set to develop models that define the given dataset. The output of a supervised learning can be continuous (regression), such as the price of a house or product, or categorical (classification), as in,  “yes, I will buy a product, or no, I would not buy a product.”

Regardless of the type of the output required, all supervised learning algorithms try to solve an equation, which is y = f(x) (output is a function of the input). There are many algorithms already packaged in various programming languages.

Examples of supervised machine learning algorithm and three drawbacks:

Regression algorithms: These are algorithms that solve for a real value for the output, as stated earlier. They are often called regressor, and they cover a wide range of algorithm types, from solving a simple line equation to solving polynomial relationships that include regularization. You can find out more about the list of present algorithms at http://scikit-learn.org/stable/supervised_learning.html, where you will see a full description of all regression algorithms, from linear to quadratic, to kernel and support vectors machines.

Classification algorithms: These are algorithms that solve for categorical outputs, such as yes or no (0, 1). Algorithms in this category are called classifiers,  and there are various ways in which classification problems are present. Some classification problem are bi-class in nature, such as predicting yes (0) or no (1), while others are multiclass, predicting yes (0), no (1), or maybe (2). Also, some of the algorithms predict the specific probability of each class, e.g. [0.2, 03, 0.5].

You can find more resources on the http://scikit-learn.org/stable/modules/multiclass.html page. Also check out http://scikit-learn.org/stable/user_guide.html for a comprehensive list of all algorithms supported by the scikit-learn package.

 

  1. Unsupervised Learning:

Unsupervised learning is a machine learning scope that deals with an unlabeled dataset. It is usually employed for cluster analysis where the categories or sub-categories of the dataset is unknown. It is also used for novelty and outlier detections on labelled data sets during preprocessing, before a form of supervised learning is applied. Other types of clustering that are done with unsupervised learning include mixed models and hierarchical clustering.  A collection of samples and documents can be found on the http://scikit-learn.org/stable/unsupervised_learning.html page.

Below is an algorithm cheat sheet that helps in the selection of algorithms using the python’s scikit-learn package.

Understanding AI and Machine Learning

Algorithms below the black line are popularly used for unsupervised learning, while algorithms above the black line are used for supervised learning.

  1. Reinforcement Learning

This is more a advanced and difficult machine learning area to implement. This is an hybrid technique between supervised and unsupervised learning in which an agent/model is trained by supplying  feedback during training, and the agent learns from the feedback. This is mostly used in visual environments where the agent interacts with the environment and learns while the environment changes. This mimics the fundamental way in which humans (and animals) learn. As humans, we have a direct sensor-motor connection to our environment, meaning we can perform actions and witness the results of these actions on the environment. This idea is known as “cause and effect,” and it undoubtedly is the key to building up knowledge of our environment throughout our lifetime.

The “cause and effect” idea can be translated into the following steps for an RL agent:

  1. The agent observes an input state
  2. An action is determined by a decision-making function (policy)
  3. The action is performed
  4. The agent receives a scalar reward or reinforcement from the environment
  5. Information about the reward given for that state/action pair is recorded

By performing actions and observing the resulting reward, the policy used to determine the best action for a state can be fine-tuned. Eventually, if enough states are observed, an optimal decision policy will be generated and we will have an agent that performs perfectly in that particular environment.

There is some research being done with large hedge funds to create smart agents who are better at predicting and anticipating stock price movement.

Summary

AI is not a singular algorithm, programming paradigm, or entity,  but is rather a combination of steps and techniques that build up a product and service. In reading this article, you should now have a good starting point for determining how algorithms are established as well as the concise path that determines how the best algorithm is displayed.

 


Understanding AI and Machine Learning


 By: Tom Adedeji, Senior Associate

29 Cutting Edge Applications of Artificial Intelligence

29 Cutting Edge Applications of Artificial Intelligence  Artificial Intelligence (AI) is the theory and development of computer systems that can perform tasks that normally require human intelligence. These tasks include visual perception, speech recognition, decision making, and language translation. Systems capable of performing such tasks are steadily transitioning from research laboratories into industry usage.

AI technology is unique in that it is flexible in application. It can be used to improve processes, enhance interactions, and solve problems that, until recently, could only be performed by humans. AI’s advanced abilities include natural language processing (NLP), machine learning, machine perception, and enhanced analytics.

The list below details 29 cutting-edge applications made possible by AI technology.

 

NATURAL LANGUAGE PROCESSING

Natural language processing (NLP) is a sub-category of AI that attempts to bridge the gap between human and computer communication. AI-enabled systems such as IBM’s Watson use NLP to understand and respond to the nuances of human language. This allows for more accurate analysis of data sets and communication of insights.

  1. Customer interaction chatbots — Chatbots are computer programs that are commonly used to interact with customers by audio or text. Conversica, for example, is a virtual sales assistant that communicates persistently and politely with prospective sales leads. Conversica uses email to engage, qualify, and follow-up with leads, allowing the sales team to focus their efforts on closing deals.
  2. Financial chatbots — In the financial industry, chatbots such as aLVin are used to interact with brokers. They can answer questions, understand intent, and direct brokers to their desired products and information.
  3. Virtual assistants — Beyond chatbots, AI can power more complicated virtual assistants that can recognize client needs and complete various tasks. Expensify‘s virtual assistant, Concierge, assists in the automation of expense reports and travel arrangements for companies. It can inform clients of real-time price changes and can even file receipts on their behalf.
  4. Communication systems – AI-powered communication systems can also be used to manage relations between peers and stakeholders. CrystalKnows, a personality detection software, uses NLP to evaluate LinkedIn accounts and develop a profile of how to most effectively speak to, work with, or sell to an individual. Crystal can even draft emails to anyone based on the preferred tone suggested by his or her online presence.
  5. Legal assistants — The language processing capabilities of AI assistants can be tailored to a specific industry. LegalRobot’s AI assistants are designed to review legal documents and make suggestions for language clarity and strength.
  6. Cognitive retail – Virtual assistant capabilities can be integrated with other customer relationship management products to provide in-person levels of service via online platforms. The North Face‘s personal web-shopper, XPS, uses NLP to understand customer need as would a human representative. It then uses machine learning to make informed product recommendations based on customer history, location, and other data.
  7. Personal assistants – AI can also be used on a personal device to simplify daily tasks. Gluru technology, for example, is used to power a task management application that uses NLP to analyze a user’s conversational data, such as their email. This app can identify tasks, generate a personalized to-do list, and even provide actionable buttons to complete each task.
  8. Web speech – New language-based technological advancements in AI can improve the web navigation and searching experience. These web speech APIs integrate voice recognition technology, syntactical analysis, and machine learning to seamlessly convert voice to text and vice versa. This click- and typing-free internet interaction can improve information accessibility for those with a disability or low technological acumen.

 

MACHINE LEARNING

Machine learning is an application of AI that allows systems to process data and learn to improve the performance of a specific task without explicit programming.  Deep learning is a form of machine learning that mimics human learning patterns to gain an understanding of unstructured data sets and generate intelligent decisions.

  1. Medical decision making — Deep-learning programs such as Enlitic can optimize physician decision making by analyzing a patient’s past medical history, diagnostic information, and symptoms to provide actionable insights. These programs learn as they process data, improving their ability to identify diseases and provide treatment planning.
  2. Healthcare analytics — Deep learning can also be used to compile, analyze, and interpret collaborative data. Flatiron, for instance, has developed a cloud-based platform for healthcare professionals that compiles insights, empirical data, and patient experiences to improve oncological care on a real-time basis.
  3. Bioinformatics – In the field of bioinformatics, scientists use AI-software to identify patterns in large data sets, such as sequenced genomes and proteomes. This analysis can help in the development of new drugs to tackle diseases by determining which proteins are encoded by a certain gene. Atomnet is a deep-learning technology that analyzes the structure of proteins known to cause disease and designs drugs accordingly.
  4. Emotional detection —Emotional detection systems powered by AI can detect human emotions without visual input. Researchers at MIT have developed EQ Radio, a system that learns to identify human emotions based on heartbeat data collected by wireless signals. This technology may one day be used by smart homes to detect if a resident is experiencing a heart attack.
  5. Fraud detection — Fraud prevention has always been a major challenge for the financial industry. PayPal and other eCommerce companies have started to use deep-learning fraud-detection algorithms to monitor customers’ digital transactions and identify suspicious behaviours. A study by LexisNexis found that this deep-learning approach to security has reduced PayPal’s fraud rate to 0.32% of revenue, a 1% improvement over the industry average.
  6. Cyber Security — AI-enabled cybersecurity programs analyze and organize internal network data to identify potential threats. The security program RecordedFuture uses machine learning and NLP to contextualize information and provide actionable analyses.
  7. Procurement optimization – Companies can use AI internally to enhance business operations such as procurement. Tamr, a data unification software, uses machine learning to clean, analyze, and sort procurement data. It identifies savings opportunities, bundles spending across business units, and exposes supplier risk.
  8. Customer interactions — While NLP allows virtual assistants to interact easily with customers, deep learning allows them to locate and provide the desired information. The AI model DigitalGenius analyzes historical customer service transcripts in order to recognize successful customer interactions. This allows the model to predict meta-data for new cases and provide suggestions for automated customer responses.
  9. Optimized gaming — The gaming industry, previously focused on the level distinctions of “easy,” “medium,” or “hard,” is now using AI technology to develop self-optimizing and -evaluating games.  These new AI-powered games keep players engaged by continuously adjusting difficulty and strategy to suit player ability.
  10. Military planning — In a military setting, AI can be used to increase deployment efficiency. Autonomous machines (including drones and satellites) share data in real time to develop actionable strategies. The U.S. military AI system JADE evaluates historical data, combines information with learned reasoning, and presents suggestions to execute large-scale plans in minimal time.

 

MACHINE PERCEPTION

Machine perception is the ability of a system to simulate human perception of the world. AI uses machine perception to extract information from different data sources. Computer vision is a type machine perception that allows AI to extract information from images.

  1. Medical imaging — Computer vision represents a huge technological advancement for medical imaging and preventative care. The diagnostic program Zebra Medical Vision collects and analyzes medical scans for various clinical identifiers. It then accesses a database of millions of scans, enabling it to provide critical information such as the location of a tumour or a patient’s risk of cardiovascular disease.
  2. Manufacturing — The manufacturing sector is increasingly turning to robotics to speed up repetitive tasks. AI-enabled robots use computer vision to complete tasks and to adapt to changing environmental conditions, broadening the types of tasks available to robots and preventing costly mistakes on the assembly line. Fanuc‘s Gakushu robots use computer vision and a machine learning software to collect and evaluate data for path, speed, and task optimization in aerospace manufacturing.
  3. Service industry – Some AI-enabled robots can not only understand human language but can recognize human emotions. Using computer vision, Softbank‘s humanoid robot Pepper can interpret facial expressions as human emotions and generate responses accordingly. Pepper can also recognize and remember individual faces and preferences. It is primarily used as a greeter in Japanese office buildings, restaurants, banks, and stores.
  4. Financial industry — In the financial industry, AI programs can learn to identify potential high-yield customers, to recognize fraud, and even to forecast changes in stock trends.  To further reduce fraud, the financial application Face++ uses computer vision for facial recognition to secure users’ financial transactions.
  5. Autonomous delivery – Companies are increasingly using AI for commercial navigation purposes. Autonomous delivery systems, such as Amazon‘s delivery drones and Domino‘s Robotic Unit, use computer vision to efficiently navigate obstacles and optimize routes. Beyond commercial delivery, Matternet’s autonomous drone network in Switzerland aims to reduce medical testing times by flying diagnostic materials between hospitals and labs.
  6. Transit safety — AI technology is paving the way for autonomous cars and accident-free transit systems. The combination of deep learning and computer vision allows cars to observe and safely interact with the surrounding environment. Road safety can be further increased by AI-enabled navigation systems, which alert drivers to potential accidents and suggest alternative navigation routes.
  7. Geospatial analytics — Geospatial analytics use computer vision to gather and compare satellite imagery with historical data in order to develop insights. Using these insights, AI-enabled satellites can track economic trends from space. Orbital Insight, for example, predicts retail sales based on satellite images of retail store parking lots.
  8. Childcare — AI devices use computer vision to recognize faces and navigate around obstacles, but a new Google patent suggests they can even be used to provide childcare.  Google’s AI babysitter system learns to recognize different features of a home and to differentiate between family members. The system can recognize when a child has been left alone for too long, or has wandered dangerously close to a socket, and alerts the parents accordingly.

 

PREDICTIVE ANALYTICS

Predictive analytics are used by programs to analyze historical data in order to predict future outcomes. When combined with AI platforms, analytic ability increases in speed, scale, and application.

    1. Marketing — Some predictive models can be used to analyze consumer data and inform marketing decisions. Magnetic, for instance, can predict the most effective advertisement to present a particular consumer and can choose to present the selected advertisement to the consumer without human supervision.
    2. Data extraction — Data-extraction programs use AI to analyze and extract specific information from documents. Xtracta, for example, allows various retail applications to extract data from receipts. This information, combined with predictive analytics, generates useful statistical reports and relevant buying suggestions for the application user.
    3. Social Network Analytics — Social networks can provide valuable marketing data, but also produce linguistically complicated datasets. In order to produce usable information, user profiles must be semantically analyzed using NLP. Companies can then use predictive algorithms to identify a customer’s preferences and web navigation patterns in order to provide targeted web advertising.

Development of AI technologies is actively being encouraged through projects like Soar—a cognitive architecture project aimed at developing computational building blocks for intelligent agents—and OpenCog—an open-source software project intended to create a framework for artificial general intelligence. Through such collaboration, AI capabilities continue to advance, thus expanding application potential.


29 Cutting Edge Applications of Artificial Intelligence


 By: Jenya Doudareva, Associate & Lokesh Patil, Associate