Artificial Intelligence influencing Everyday Life

In recent years, there has been a surge in the development and implementation of virtual personal assistants. Various technologies like voice recognition, text analysis and a few even make decisions for you like automatically scheduling meetings supported incoming emails.

  1. Alexa: Alexa Skills Alexa is built on Skills. Skills give the platform the ability to understand verbal commands and instructions Alexa Developed by Amazon, Alexa is a VPA made popular by the Amazon Each and the Amazon Echo Dot. It was released in two thousand and fourteen and allows you can interact with it simply by speaking it. Alexa is capable of the following: Playing music creating to-do lists Setting alarms Streaming podcasts playing audio books Providing weather updates and other real-time information such as the news. Most devices that have Alexa installed to permit users to activate the functionality via. Wake word??? Like “Echo “or???Alexa “Some of the more recent developments as of May 2017, include home orders! Using Alexa, one can now order takeout food from places such as Domino???S Pizza, Pizza Hut and Just Eat. Starbucks also announced a private beta for placing pick up orders. Reassured customers than Alexa enabled devices only listen to conversation. Completely different product to Amazon’s Alexa. If it could be summed up the cloud meaning there is no installation required from an end-user perspective. In a drive to extend developer adoption of Alexa Skills and enrich the ecosystem, Amazon is offering up free prizes to developers as a part of the Alexa Skills Challenge. In this challenge, developers are tasked with writing an Alexa Skill and can win up to $5,000 in cash! Concerns Alex is popular but it also has its share of skeptics. This technology could listen to private conversations in the home. It has been developed to respond only to the wake up words and respond only to the wake up words. Another point worth mentioning is that Amazon uses past recordings to assist train Alexa and improve the user experience. These recordings can be deleted though. At the top of the day, people that use products like Alexa are ultimately trading privacy for convenience. Internet user’s attitudes to privacy have relaxed within the past 5-10 years (increased adoption of social channels has driven that).
  2. X.AI X.AI’ss  Amy  whilst still falling under the category of VA is a in one sentence it would be: The personal assistant who schedules meetings for you Amy was born out of the Founder’s personal pain point of scheduling one thousand and nineteen meetings in one year alone. As often is that the case, these meetings bounced between the respective parties until an appropriate appointment date and time were found. He figured this pain point must be affecting not just him but other information workers so set out to build a virtual agent that leverages AI to reduce the amount of email ping pong between work colleagues when trying to schedule meetings. Amy’s AI can interrogate communication and determine if humans are talking about arranging meetings when it’s identified this, Amy will then examine each person’s diary and find non-conflicting times and present these to all parties in the email or group message.
  3. Video Games Crude forms of AI in video games have been around for decades. Take the 80s-arcade game Pacman for example, each ghost featured unique forms of AI to try and catch the player as they made their way around the maze. Technology has moved on from the 80s though and non-player-characters (or NPCs) have become so advanced that entire worlds can now be modeled, rendered and explored. Games like GTA, Call of Duty feature-rich digital worlds with numerous NPCs which will, and sometimes do, behave during a human-like manner. All of this is possible due to advancements in artificial intelligence.
  4. Stop! Consider for a moment, AI researcher at Princeton University Arthur Filipowicz. Filipowicz has been trying to develop software for autonomous vehicles; part of the problem is that the software must be able to recognize a stop sign. These signs can vary in their appearance due to weather conditions or may just even just be needing repair. When a car arrives at a stop sign, it must stop, failing to do so could result in a human fatality. The image recognition algorithm, therefore, must be ready to identify multiple sorts of a stop sign. Filipowicz came up with a novel solution for this problem. GTA V. In the game GTA V, the player is immersed in a fictional city Los Santos which is loosely based on the Los Angeles. During the games production, l. a. was extensively researched. The team organized field research trips with tour guides and architectural historians, and captured around 250,000 photographs and lots of hours of video footage. These photographs and pictures naturally made their way into the extent design. Filipowicz was then ready to alter the sport in such how that his autonomous vehicle software could navigate through the graphically rendered roads and more importantly, identify and response to stop signs as if it were in real life. Smart Cars Drivers cars and driverless Lorries having been making headlines recently. Companies such as Google, Uber, Apple, Volkswagen and Mercedes are heavily investing in self-driving automobiles powered by artificial intelligence. In 2016, by leveraging AI, San Francisco startup Otto (owned by Uber) successfully delivered 50,000 cans of Budweiser. From a billboard perspective, integrating AI into end of the day trucking routes will yield cost savings and has the potential to save lives – AI routines don ‘t suffer from fatigue. Gartner predicts that by 2020, there will be approximately two hundred and fifty million cars connected to each other via Wi-Fi. This will be to allow them to communicate with each other on the roads. That‘s not too far away at the time of writing this blog post (2017).
  5. Consumer Analytics and Forecasting Predictive Shipping Machine learning and artificial intelligence have been in use for years to help businesses forecast demand and set prices dynamically. Back in 2013, Amazon patented “predictive stocking”, the idea behind this shipping system is to reduce delivery times by predicting what consumers will want before they have even bought it One example pre-shipping scenario: “ a way may include packaging one or more items as a package for eventual shipment to a delivery address, selecting a destination geographic area to which to ship the package, shipping the package to the destination geographic area without completely specifying the delivery address at the time of shipment, and while the package is in transit phase, completely specifying the delivery address for the package . ” Source: TechCrunch Dynamic Pricing Estimating the price to sales ratio (or price elasticity can be difficult for retailers, artificial intelligence makes price optimization easier however. It does this by correlating pricing trends with sales trends and can also align other variables such as stock levels.
  6. Data Scientist Mohammad Islam wrote an article on this subject which explains this concept in more detail here. Finance Fraud Detection Business rules and reputation lists have existed for decades and many organizations today implement such things to identify fraudulent behavior. A rule contains a statement that is both readable by a human and understandable by a computer. For example, a bank may create a rule that says something like: “If the customer is purchasing a product that costs greater than $1,500, there location in Yemen, and signed up less than twenty-four hours ago, then block the transaction.” Rules like this are static, over time they can be gamed by adopting a brute-forcing approach; criminals can try different combinations of location, monetary value and so on. Artificial intelligence is changing this though. By implementing supervised machine learning or SML, the machine can learn from historical datasets that contain fraudulent transactions. The machine can then identify specific patterns that represent a typical fraudulent transaction, whether it is the location, quantity or type of product. Credit Decisions When applying for credit, whether it is a loan or a credit card, banks must determine whether each customer is creditworthy. Other variables are calculated such as the credit limit, interest rate and maximum amount the customer can obtain. Today‘s consumer expects near-instant decisions and AI and machine learning is helping drive this.
  7. To help banks make more informed credit decisions and determine the risk of lending to a customer, FICO is using machine learning. Researchers at MIT also found that by using machine learning, banks could reduce the number of delinquent customers by up to 25%. Chatbots Historically, chatbots offered rudimentary answers to simplistic questions, most of this was achieved by identifying specific keywords and returning a canned response. This was often frustrating for users but artificial intelligence is transforming this field. Advancements in naive Language Processing and machine learning allow chatbots understand the semantic orientation of each word in a sentence and derive true meaning. Doing this allows the chatbots to create some context of what a customer is talking about and ask relevant questions or provide solutions to customer queries. Bank of America one of the largest U.S banks using a voice and text enabled chatbots called Erica. Erica can send customer’s notifications or help customers make better financial decisions.
  8. JPMorgan Chase Launched a bot called COIN which allows the bank to analyze complex legal contracts faster and more efficiently than a human ever can. COIN can also undertake the following tasks: parse emails for employees, grant access to software systems reset passwords. To date, this has saved the bank 360,000 hours in manpower!
  9. Social Networking Facebook With almost 2 billion users on the platform, Facebook owns one of the largest datasets on the planet. Its users share vast quantities of content whether it’s in text, image and video format. Consider the uploading of a photograph, Facebook will automatically highlight faces and suggests friends to tag that exist within the user‘s social graph. But how can Facebook do this in near real-time? You‘ve guessed it, AI. By leveraging face recognition software and neural networks, Facebook can identify with reasonable accuracy, which everyone is. Facebook acquired an Israeli facial recognition tech firm in two thousand and twelve for $55-60 million which is has helped drive this. Facebook has also been investing in this technology internally. Snapchat In 2015, Snapchat introduced “facial filters”. These track facial movements and permit users to feature digital masks that overlay their faces when moved. It uses AI technology which was originally developed by a Ukrainian company called Looksery which has patents on using machine learning to trace movements in video. Real Estate Elements of AI are utilized in land for quite a while now; property listing platforms can match buyers to new properties within minutes of being shared online. It goes further than simply just matching keywords. Roof. AI sets to vary land by integrating AI into the guts of all land activities. Some of the features include but aren’t limited to: task automation lead generation Facebook Messenger integration “Roof Ai is an AI-powered messaging service that allows smart conversations between real estate businesses and their customers. The messaging service is backed by a proprietary CRM. The CRM is used by the real estate teams to manage the requests and assist the chatbots in case human intervention is needed. It‘s also an analytics tool that helps them monitor everything that is happening on our messaging channels.
  10. Most land websites struggle to realize an II Chronicles conversation rate. And the main reason for that is the lack of engagement with the visitors on these sites. Roof Ai helps increase conversion by a factor of 8.” Broker vs. Bot Inman, a real estate publication launched a challenge affection ally titled “Broker vs. Bot”. The challenge, which was conducted in Denver, asked a local real estate journalist to play the role of “buyer” and select three homes that he like from local real estate listings. Then, on three separate dates, Inman asked three separate real estate brokers to compete against a bot “Find More Genius” to recommend homes like one of the homes selected by the buyer. The “buyer” was then asked which one of the recommended homes he preferred. On all three dates, Find More Genius‘choices were selected. Does this mean that AI will replace agents? We can ‘t say for sure, one thing is certain however is that business who adopt emerging technologies like AI will stay one step ahead of the curve.
  11. Drones you‘re probably familiar with pilotless drones, they‘ve been used by the military for years now. In recent years, drones have also made the switch from the defense military world to the civilian world. Let‘s explore some samples of how drones are using AI. Life Saving An engineer at the Technical University of Delft, one of the world‘s leading drone research hubs in the Netherlands, started to investigate if drones could reach a heart attack patient faster than an ambulance. By working with ambulance services in Amsterdam, Alec Momont established that the standard reaction time for an asystole call is approximately ten minutes Momont when onto build a drone prototype that ships with a DIY defibrillator and is aiming to get there in six minutes. Momont‘ s vision is for drones to be part of a wider emergency services response team and that someone witnessing a heart attack could call one hundred and twelve (the equivalent of nine hundred and eleven in the USor nine hundred and ninety-nine in the UK) and the call handler would dispatch a drone. Using a two-way video connected to the drone, a medic could talk the witness through the required steps of using the defibrillator. One can see the apparent advantage to having such technology in rural areas or difficult to succeed in locations
  12. Hover Camera Zero Zero Robotics‘“Lily Camera” which pitches itself as a “throw and shoot” camera hovers within the air and is powered by bespoke AI which has been coined “Embedded AI”. The firm developed proprietary technology that fuses a set of state-of-the-art AI with a PCB the dimensions of two US quarters! Weighing only two hundred and thirty-eight grams, the self-flying camera can be carried around, it‘s like having your own self-flying personal photographer. Once in the air, the done automatically finds and follows you (its owner), while recording your everyday life from a completely new angle. It‘s all possible because of advanced AI face recognition algorithms. Logistics in December 2016, a British farmer Richard Barnes receive an order placed on Amazon for a bag of popcorn and an Amazon Fire TV Stick. What was different about this delivery? It only took 13 minutes for the goods to be delivered from the point of order and was fulfilled by using an autonomous drone! Text Analytics and NLP Text Analytics and NLP are intertwined, without Natural Language Processing (NLP), the machine can‘t determine the semantic orientation of the words (make sense of the order of the words and what they mean). NLP allows humans to communicate with the machine in natural language, let‘s explore some examples of text analytics and NLP. Customer Reviews and Sentiment Analysis Consumers often leave comments or reviews on specific products or services that they purchase. Quite often, the quantity of user-generated data being created is vast and easily can’t be reviewed but a person’s at scale. This sort of text is also unstructured which only adds to the problem.
  13. Text analytics, NLP and AI are ideal for these kinds of tasks, however. By applying techniques such as sentiment analysis and POS Tagging (Part of Speech) business can find out how consumers feel about their product, brand or service. In recent years, we have seen the democratization of sentiment analysis in that it‘s now being offered as-a-service. Some of the companies that offer this sort of functionality include, but aren’t limited to: Microsoft IBM Monkey Learn Social Opinion They offer REST APIs that integrate easily with your existing software applications. For example, using the following publicly available Sentiment Analysis REST API from UK start-up Social Opinion, we pass in the text, “this phone is awesome” : phone%awesome&token=00000 The REST API then returns the following response: In the response, we can see the text has been identified as expressing positive emotion with a 64% probability of that being true. AdTech this is probably one of the more mature forms of artificial intelligence and machine learning in operation online. Have you ever looked at products in Amazon then moments later noticed similar products being displayed in your Facebook or Twitter feed? By tracking what you “ Like” and what you have viewed and therefore the comments you post and share, machine learning can, with relative accuracy place marketing creative’s in your news prey on social channels thereby improving conversion rates for business . AdTech is such a lucrative vertical that companies such as Twitter have launched developer initiatives like # Promote to encourage the development of AI-based software to drive online sales.

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