ARTIFICIAL INTELLIGENCE;- ALL YOU NEED TO KNOW:- FACTS

ARTIFICIAL INTELLIGENCE;- ALL YOU NEED TO KNOW:- FACTS

Vaibhav Patil- vaibhav0222@gmail.com

INTRODUCTION

Human have memory of 10 lacs GB capacity

John McCarthy  it was the father of artificial intelligence

Machine can learn like human or people 

Artificial intelligence definition

Make computers aur machine do or perform things which human are doing better

If if you prepared machine then same work ok machine can do with lot of perfection human cannot do do that work with precision perfection that's why artificial intelligence is important to work more in less time and less resources

Machine can learn demonstrate explain an advice to the user as well as machine can think behave and adjust automatically

Artificial intelligence solve generic questions

New modification can be added or adopted

Quick and easy modification is allowed and accepted

Maximum thing you will see in artificial intelligence are intangible

How it works-

In artificial intelligence computer or machine uses their own power to learn

When recognising image computer check pixel to pixel and keep it in mind for image processing

In artificial intelligence pattern and image recognition language recognition play important role

Artificial intelligence also play important role in in startups

  

In artificial intelligence objectives to create expert system and put human intelligence in machine

What makes human being different from machine is

Human being can ask question use logic search answer find possibilities search shortcuts that how they are intelligent than machine so in artificial intelligence we want to implement all such qualities of of human being in machine

Following fields play ported role in artificial intelligence

Robotic

Machine learning machine language

Computer vision

Natural language processing

Computer science

 linguistic

neuroscience

 psychology

engineering

maths

Products of artificial intelligence

AMbiclimate

This is the product where machine automatically identify the temperature humidity sunlight and accordingly e changes the temperature of the room as per your requirement and as per your your daily pattern

Face detection

In artificial intelligence machine can recognise face and facial expression to use and process further decisions

China is using face detection artificial intelligence for the surveillance and breaking of traffic rules so that effective governance is easy

Self driving car is one more example of of artificial intelligence with the help of GPS

Banjour

This is the product where all morning stuff is taken care air like waking up alarm preparing tea reading of news appointments except

There are many products which will help you in day to day working and discussion

Write Amazon echo & Alexa

Examples

Google assistant

Siri

Voice assistant

Face recognition app face app

Socritic

which will find answer in matter of seconds

Replica

For the cosmetic help

Google duplex help in sales and marketing work

Netflix also uses artificial intelligence in video suggestion to their customers

BBC is using artificial intelligence in writing article they don't write everything they just tell the software air important things and software automatically prepare article

In Windows we have Cortana

In Apple we have Siri

In smartphone we have speech recognition

We have intelligent toys and robots

Tesla automatic car is using artificial intelligence

Amazon Eco is also using artificial intelligence

Google assistant can take appointment from doctor or or salon

Facebook  suggest you two friends depending on artificial intelligence

If you search any product on Amazon you keep on getting advertisement related to that product on all the websites

Sofia humanoid robot is another example of of artificial intelligence manufactured by Hanson robotics in Hong Kong

In artificial intelligence machine keep learning understanding and improving themselves like Sophia robot

She can speak accordingly as per your question and intention of speaking and modify her answers day by day 

Because of artificial intelligence machine can distinguish between Police vehicle and other vehicles

Different types of fishes can be recognised by their images

In one of the milestone IBM computer defeated word chess champion

Calculation is not intelligence seen by the the chase example

Artificial intelligence can help in preparing satellite station on its own in this space

In artificial intelligence can be used to study DNA and cure diseases

Google translator can translate Chinese boards in English language after showing in front of camera

Image processing is another example image recognition is another example

Speech recognition is one more example 

Artificial intelligence is helpful in following fields

Banking and finance

Retail e-commerce

Security agent cyber security

Data analysis and customer segmentation

HR management

Healthcare management

Supply chain and logistic

Machine learning vs Artificial intelligence

How much learning is different from artificial intelligence

If you teach any machine pattern of of face of dogs then that machine can identify dogs but when you show them cat it will confuse and it will not work so that is machine learning

If you teach any machine to play chess then that machine can play only chess and if you show football for hockey that machine will not work so again that is machine learning

In artificial intelligence in above two examples machine will try to identify cat and after similar encounters keep on updating their experience and finally e machine will identify cat in same way machine will also learn to play chess football and hockey after some time on their own

We give access to the data to machine and say learn and process on your own

In machine learning machine can perform better action in just one field and it cannot be generalize and performs in other field

Like if there is a machine for washing clothes that machine cannot perform the action of of cooking

In artificial intelligence future results are depend on past results

Machine learning from the pattern of of use aur performance

Search engine optimisation is also example of artificial intelligence in which demographic characters are studied and proper reserves are suggested for user or customer

Artificial intelligence we are preparing machine which will work like human being

  

Artificial intelligence can create jobs in following fields

Computer analytic network analytic cloud engineer database administration 

  

Danger

There is a possibility like robots may rebel against human being and fight against them and destructor human race

Market size

A study by Accenture in December said AI could add $957 billion to the Indian economy or increase the country’s income by 15% by 2035 by changing the nature of work to create better outcomes.

Example 1

Kroger

Kroger plans to leverage its data, shopper insights and scale to help it remain a leader in the marketplace of the future.

The grocer already delivers 3 billion personalised recommendations each year, but they will enhance the personalization efforts to "create different experiences for customers

Kroger is testing the delivery of the future—grocery delivery by an autonomous vehicle

A partnership between Kroger and British online-only grocer Ocado is expected to help Kroger automate its warehouses

Kroger’s in-house analytics firm 84.51 deployed Kroger Precision Marketing that uses customer purchase data from Kroger’s 60 million shopper households to launch marketing campaigns across a digital spectrum.

Smart shelves

When a Kroger customer walks down the aisle with the Kroger app open, sensors identify the shopper and provide personal pricing and highlight products the customer might be interested in via smart shelves technology.

TESLA:-

The Amazing Ways Tesla Is Using Artificial Intelligence And Big Data

Tesla CEO Elon Musk publicly announced it is working on its own AI hardware.

 Tesla will process the “thinking” algorithms for the company’s Autopilot software which currently gives Tesla vehicles limited (“level 2”) levels of autonomous driving capability. Musk has said that he believes his cars will be fully autonomous (level 5 autonomous) by 2019.

But as a business decision, it is hoping its pushy tactics will pay off, with experts concluding that the company has trumped its rivals in the data-gathering department. All the vehicles Tesla have ever sold were built with the potential to one day become self-driving, although this fact was not made public until 2014 when a free upgrade was rolled out. This means the company has had a lot more sensors out on the roads gathering data than most of its Detroit or Silicon Valley rivals, many of which are still at the concept stage. Having just launched its first mass-market car, the Model 3 with a price tag of $35,000, the company is expecting the number of its vehicles on the road to increase by almost two thirds to around 650,000 in 2018 – and that’s a lot of extra sensors.

In fact, all Tesla vehicles – whether or not they are Autopilot enabled – send data directly to the cloud. A problem with the engine operation meaning that components were occasionally overheating was diagnosed in 2014 by monitoring this data and every vehicle was automatically “repaired” by software patch thanks to this.

Tesla effectively crowdsouces its data from all of its vehicles as well as their drivers, with internal as well as external sensors which can pick up information about a driver’s hand placement on the instruments and how they are operating them. As well as helping Tesla to refine its systems, this data holds tremendous value in its own right. Researchers at McKinsey and Co estimate that the market for vehicle-gathered data will be worth $750 billion a year by 2030.

The data is used to generate highly data-dense maps showing everything from the average increase in traffic speed over a stretch of road, to the location of hazards which cause drivers to take action. Machine learning in the cloud takes care of educating the entire fleet, while at an individual car level, edge computing decides what action the car needs to take right now. A third level of decision-making also exists, with cars able to form networks with other Tesla vehicles nearby in order to share local information and insights. In a near future scenario where autonomous cars are widespread, these networks will most likely also interface with cars from other manufacturers as well as other systems such as traffic cameras, road-based sensors or mobile phones.

Nvidia state that “In contrast to the usual approach to operating self-driving cars, we did not  programme any explicit object detection, mapping, path planning or control components into this car. Instead, the car learns on its own to create all necessary internal representations necessary to steer, simply by observing human drivers.”

Whatever new tech it develops may veer away from this by stepping back into the more tested waters of supervised learning, where algorithms are trained beforehand about right or wrong decisions. However, it is possible that the theoretically greater gains achievable by truely unsupervised learning may keep them on this track.

Tesla has clearly always been a company which has put data collection and analysis at the heart of everything it does. It isn’t just design and manufacturing either, with the company processing customer data with AI and even parsing it’s online forum for text insights into common problems.   

John Deere

Pesticides are currently an essential ingredient of big agriculture in order to ensure we can continue to feed the ever-growing global population of our planet.

The Incredible Ways John Deere Is Using Artificial Intelligence To Transform Farming

Computer vision specialist Blue River Technology has developed a solution for exactly that, using advanced machine learning algorithms to enable robots to make decisions, based on visual data (just as we would do ourselves) about whether or not a plant is a pest, and then deliver an accurate, measured blast of chemical pesticides to tackle the unwanted pests. Given that traditionally such decisions are made on a field-by-field basis, rather than plant-by-plant basis, the opportunities for efficiency are clear.

Farm equipment and services giant John Deere saw the potential of this development and acquired the start-up late last year and added it to the catalogue of high tech, data-powered services it already offers its customers.

It is just the latest move in John Deere’s push to put data-driven analytical tools and automation in the hands of farmers. With the rate of global population growth, the company – established in 1837 as a tool manufacturer – understands that they serve an industry where small efficiencies quickly add up to big competitive advantages.

Already the firm enables automated farm vehicles to plough and sow, under the control of pinpoint-accurate GPS systems. On top of that its Farmsight system is designed to enable data-driven insights to inform agricultural decision making, based on shared user data from subscribers all around the world.

Luckily infrastructure for gathering data which can be used to predict the effects of these influences is increasingly available. Satellite imagery – previously often prohibitively expensive – is more affordable than ever with one person I spoke to recently comparing the cost of launching a satellite to launching an app. Visual data is also available from unmanned aerial vehicles such as quadcopter drones, which can be used to monitor the growth and spread of pest through crops in real-time.

One company specialising in analysis of satellite imagery last year came within 1% of accurately predicting corn and soya yields by applying machine learning algorithms to their data. It has already released its predictions for this year’s season, which it claims will be more accurate.

The large-scale mechanisation of agriculture means that accurate data is available from the machines which spread seeds and harvest crops. Robots - such as those developed by Google Funded Abundant Robotics which suck ripe fruit from branches with vacuums -naturally record everything they do and every parameter of their operation. This structured machine data meshes well with unstructured data from meteorological or satellite imagery, and when filtered through AI algorithms will provide insights that more accurately predict yields and losses.

Google -2

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do.

Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to “learn” by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains.

The Amazing Ways Google Uses Artificial Intelligence And Satellite Data To Prevent Illegal Fishing

These techniques include “computer vision” – training computers to recognise images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal. And if it has prominent whiskers too, it’s more likely to be a cat than a horse. When fed thousands, or millions of images it will become increasingly good at deciding what an image represents.

Another is “natural language processing”. This is used in Google’s online real-time language translation service to understand nuances of human speech in any language, allowing more accurate translation between human languages.

Google also uses machine learning in its Nest “smart” thermostat products – by analysing how the devices are used in households they become better at predicting when and how their owners want their homes to be heated, helping to cut down on wasted energy.

However, besides these everyday uses Google has developed many more specialised applications of the technology, which today are in use helping to solve a variety of environmental problems around the world.

Google’s sustainability lead, Kate E Brandt spoke to me about some of these ambitious use cases where artificial intelligence is being deployed today.

She said “We’re seeing some really interesting things happen when we bring together the potential of cloud computing, geo-mapping and machine learning.”

One great example is an initiative which is already helping to protect vulnerable marine life in some of the world’s most delicate eco-systems. Using the publicly broadcast Automatic Identification System for shipping, machine learning algorithms have been shown to be able to accurately identify illegal fishing activity in protected areas.

This works in much the same way as the “cat or horse?” example for image recognition I gave above. By plotting a ship’s course and comparing it to patterns of movement where the ship’s purpose is known, computers are able to “recognise” what a ship is doing.

Brandt told me “All 200,000 or so vessels which are on the sea at any one time are pinging out this public notice saying ‘this is where I am, and this is what I am going.”

This results in the broadcasting of around 22 million data points every day, and Google engineers found that by applying machine learning to this data they were able to identify the reason any vessel is at sea – whether it is a transport ferry, container ship, leisure vessel or fishing boat.

“With that dataset, and working with a couple of wonderful NGOs – Oceana and Sky Truth – we were able to create Global Fishing Watch – a real-time heat map that shows where fishing is happening,” says Brandt.

The initiative has already led to positive outcomes in the fight against illegal fishing in protected marine environments. For example, the system identified suspicious activity in waters under the jurisdiction of the Pacific island nation of Kiribati – which include the world’s largest UNESCO heritage marine site. When intercepted by Kiribati government vessels, the captain of the fishing vessels denied any wrongdoing. But after being presented with evidence gathered by Google’s machine learning algorithms, he realised he had been caught red-handed and admitted the violation of international law.

“What’s really exciting is that this creates tremendous opportunities for governments and citizens to protect our marine resources. Fishing in those marine reserves is illegal and Global Fishing Watch has been used to protect those reserves.”

Machine learning-driven image recognition is also used for a very different purpose, on land this time, and across the United States as well as Germany.

Project Sunroof, launched in 2015, involves training Google’s systems to examine satellite data and identify how many homes in a given area have solar panels mounted on their roofs. As well as that, it can also identify areas where the opportunity to collect solar energy is being missed, as no panels are installed.

“This started with one of our engineers living in Cambridge, Massachusetts, who wanted to put solar panels on his roof but was finding it hard to figure out if he was living in a good location – did he have enough sunlight to work with?” Brandt tells me.

This resulted in the development of a machine learning system which took Google Earth satellite images, and combined it with meteorological data, to give an instant assessment of whether a particular location would be a good candidate for solar panels, and how much energy – as well as money – a householder might save.

“Then we realised this was not only really useful for individual home owners, but it could be very useful for communities – at county, city or state level – to assess their potential.”

Google’s image recognition algorithms were trained to recognise how to spot solar arrays in satellite images. This system was quickly put to use by the city of San Jose in California as part of an initiative to identify locations where 1 gigawatt of solar energy could be generated from new panels.

Both of these initiatives are great examples of how machine learning – powered by publicly available datasets - are enabling new solutions to problems of the modern age. As more data becomes available, and computers become increasingly powerful, who knows what other challenges artificial intelligence will help us to overcome?

McDonald's

Personalised and improved customer experience

Not only can customers order and pay through the McDonald’s mobile app and get access to exclusive deals, but when customers use the app, McDonald’s gets vital customer intelligence about where and when they go to the restaurant, how often, if they use the drive thru or go into the restaurant, and what they purchase. The company can recommend complementary products and promote deals to help increase sales when customers use the app. In Japan, customers who use the app spend an average of 35% more thanks in part to the recommendations they are provided at the time they place an order. favourite orders are then saved by the app and offer a way to encourage repeat visits. App users can avoid the lines at the drive thru and at the counters, reason enough for many to share their buying data in exchange for convenience and perceived perks.

Digital menus that use data

McDonald’s continues to roll out new digital menus. These aren’t just fancier versions of the old menus, these menus can change based on the real-time analysis of data. The digital menus will change out the options based on time of day and even the current weather. For example, on a cold, blustrey day, the menu might promote comfort foods while refreshing beverages might be highlighted on a record heat day. They’ve been used in Canada and resulted in a 3% to 3.5% increase in sales.

Trends analytics

Embracing a data-driven culture is also important to help McDonald’s better understand performance at each individual restaurant as well as uncover best practices that can be shared with other restaurants in the chain. Since McDonald’s uses a franchise business model, consistency of food and experience is important across the franchise. It’s important from the customer’s perspective to experience the same food and offerings from one restaurant to another no matter where they are located or who it’s owned by. The company looks at multiple data points in the customer experience. For example, when they look at the drive-thru experience they not only assess the design of the drive-thru, but they review the information provided to the customer and what’s happening for customers waiting in line to order. They analyse the patterns in an effort to make predictions and alter design, information and people practices if necessary.

Kiosks and interactive terminals

As one solution to the increasing costs of labour, McDonald’s is replacing cashiers in some locations with kiosks where customers can place their order on a digital screen. Not only are labour costs reduced, but the error rates go down. By the end of 2018, you can expect an ordering kiosk to be available at a McDonald’s near you. McDonald’s France is also testing out interactive terminals. Once a customer places an order they take a connected RFID card associated with the order to their table. When the order is ready, a McDonald’s staff person locates the customers through the RFID card and then delivers their meal to them.

Refreneces:

https://bernardmarr.com/default.asp?contentID=1683