Tag: AI

RPA – Robotic Process Automation

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Robotic process automation (RPA), also referred to as software robots, is a form of business process automation (BPA) – also now as Business Automation or Digital Transformation – where complex business processes are automated using technology enabled tools harnessing the power of Artificial intelligence (AI).

Robotic process automation (RPA) can be a fast, low-risk starting point for automating repettitive processes that depend on legacy systems. Software bots can pull data from these manually operated systems (most of the time without an API) into digital processes, ensuring faster and more efficient and accurate (less user error) outcomes. 

Workflow vs RPA

In traditional workflow automation tools, a system developer produces a list of actions/steps to automate a task and define the interface to the back-end system using either internal application programming interfaces (APIs) or dedicated scripting language. RPA systems, in contrast, compile the action list by watching the user perform that task in the application’s graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI, as if it is manually operated.

Automated Testing vs RPA

RPA tools have strong technical similarities to graphical user interface testing tools. Automated testing tools also automate interactions with the GUI by repeating a set of actions performed by a user. RPA tools differ from such systems in that they allow data to be handled in and between multiple applications, for instance, receiving email containing an invoice, extracting the data, and then typing that into a financial accounting system.

RPA Utilisation

Used the right way, though, RPA can be a useful tool in your digital transformation toolkit. Instead of wasting time on repetitive tasks, your people are freed up to focus on customers or subject expertise bringing product & services to market quicker and provide customer outcomes quickly – all adds up to real tangible business results.

Now, let’s be honest about what RPA doesn’t do – It does not transform your organisation by itself, and it’s not a fix for enterprise-wide broken processes and systems. For that, you’ll need digital process automation (DPA).

Gartner’s Magic Quadrant: RPA Tools

The RPA market is rapidly growing as incumbent vendors jockey for market position and evolve their offerings. In the second year of this Magic Quadrant, the bar has been raised for market viability, relevance, growth, revenue and how vendors set the vision for their RPA offerings in a fluid market.

Choosing the right RPA tool for your business is vital. The 16 vendors that made it into the 2020 Gartner report is marked in the appropriate quadrant below.

The Gartner RPA Magic Quadrant report …Download

The Automation Journey

To stay in the race, you have to start fast. Robotic process automation (RPA) is non-invasive and lightning fast. You see value and make an immediate impact.

Part of the journey is not just making a good start with RPA implementations but to put the needed governance around this technology enabler. Make sure you can maintain the automated processes to quickly adapt to changes, integrate with new applications, align with continuously changing business processes while making sure that you can control the change and clearly communicate it to all needed audiences.

To ensure that you continuously monitor the RPA performance you must be able to measure success. Data gathered throughout the RPA journey and then converted through analytics into meaningful management information (MI). MI that enables quick and effective decisions – that’s how you finish the journey.

Some end-to-end RPA tools cover most of the above change management and business governance aspects – keep that in mind when selecting the right tool for your organisation.

So, do you want to stay ahead of your competition? Start by giving your employees robots that help them throughout the day.

Give your employees a robot

Imagine if, especially in the competitive and demanding times we live today, you could give back a few minutes of time of every employee’s day. You can if you free them from wrangling across systems and process siloes for information. How? Software robots that automate the desktop tasks that frustrate your people and slow them down. These bots collaborate with your employees to bridge systems and process siloes. They do work like tabbing, searching, and copying and pasting – so your people can focus on your customers.

RPA injects instant ROI into your business.

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Humans are smarter than any type of AI – for now…

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Despite all the technological advancements, can machines today only achieve the first two of the thee AI objectives. AI capabilities are at least equalling and in most cases exceeding humans in capturing information and determining what is happening. When it comes to real understanding, machines still fall short – but for how long?

In the blog post, “Artificial Intelligence Capabilities”, we explored the three objectives of AI and its capabilities – to recap:

AI-8Capabilities

  • Capturing Information
    • 1. Image Recognition
    • 2. Speech Recognition
    • 3. Data Search
    • 4. Data Patterns
  • Determine what is happening
    • 5. Language Understanding
    • 6. Thought/Decision Process
    • 7. Prediction
  • Understand why it is happening
    • 8. Understanding

To execute these capabilities, AI are leaning heavily on three technology areas (enablers):

  • Data collecting devices i.e. mobile phones and IoT
  • Processing Power
  • Storage

AI rely on large amounts of data that requires storage and powerful processors to analyse data and calculate results through complex argorythms – resources that were very expensive until recent years. With technology enhancements in machine computing power following Moore’s law and the now mainstream availability of cloud computing & storage, in conjunction with the fact that there are more mobile phones on the planet than humans, really enabled AI to come to forefront of innovation.

AI_takes_over

AI at the forefront of Innovation – Here is some interesting facts to demonstrate this point:

  • Amazon uses machine learning systems to recommend products to customers on its e-commerce platform. AI help’s it determine which deals to offer and when, and influences many aspects of the business.
  • A PwC report estimates that AI will contribute $15.7 trillion to the global economy by 2030. AI will make products and services better, and it’s expected to boost GDP’S globally.
  • The self-driving car market is expected to be worth $127 billion worldwide by 2027. AI is at the heart of the technology to make this happen. NVIDIA created its own computer — the Drive PX Pegasus — specifically for driverless cars and powered by the company’s AI and GPUs. It starts shipping this year, and 25 automakers and tech companies have already placed orders.
  • Scientists believed that we are still years away from AI being able to win at the ancient game of Go, regarded as the most complex human game. Recently Google’s AI recently beat the world’s best Go player.

To date computer hardware followed a growth curve called Moore’s law, in which power and efficiency double every two years. Combine this with recent improvements in software algorithms and the growth is becoming more explosive. Some researchers expect artificial intelligence systems to be only one-tenth as smart as a human by 2035. Things may start to get a little awkward around 2060 when AI could start performing nearly all the tasks humans do — and doing them much better.

Using AI in your business

Artificial intelligence has so much potential across so many different industries, it can be hard for businesses, looking to profit from it, to know where to start.

By understanding the AI capabilities, this technology becomes more accessible to businesses who want to benefit from it. With this knowledge you can now take the next step:

  1. Knowing your business, identify the right AI capabilities to enhance and/or transform your business operations, products and/or services.
  2. Look at what AI vendors with a critical eye, understanding what AI capabilities are actually offered within their products.
  3. Understand the limitations of AI and be realistic if alternative solutions won’t be a better fit.

In a future post we’ll explore some real life examples of the AI capabilities in action.

 

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Artificial Intelligence Capabilities

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AI is one of the most popular talked about technologies today. For business, this technology introduces capabilities that innovative business and technology leadership can utilise to introduce new dimensions and abilities within service and product design and delivery.

Unfortunately, a lot of the real business value is locked up behind the terminology hype, inflated expectations and insecure warnings of machine control.

It is impossible to get the value from something that is not understood. So lets cut through the hype and focus to understand AI’s objectives and the key capabilities that this exciting technology enables.

There are many definitions of AI as discussed in the blog post “What is Artificial Intelligence: Definitions“.

Keeping it simple: “AI is using computers to do things that normally would have required human intelligence.” With this definition in mind, there are basically three things that AI is aiming to achieve.

3 AI Objectives

  • Capturing Information
  • Determine what is happening
  • Understand why it is happening

Lets use an example to demonstrate this…

As humans we are constantly gathering data through our senses which is converted by our brain into information which is interpreted for understanding and potential action. You can for example identify an object through site, turn it into information and identify the object instantly as, for example, a lion. In conjunction, additional data associated with the object at the present time, for example the lion is running after a person yelling for help, enables us to identify danger and to take immediate action…

For a machine, this process is very complex and requires large amounts of data, programming/training and processing power. Today, technology is so advanced that small computers like smart phones can capture a photo, identify a face and link it to a name. This is achieved not just through the power the smart phone but through the capabilities of AI, made available through services like facebook supported by an IT platform including, a fast internet connection, cloud computing power and storage.

To determine what is happening the machine might use Natural Language Understanding (NLU) to extract the words from a sound file and try to determine meaning or intent, hence working out that the person is running away from a lion and shouting for you to run away as well.

Why the lion is chasing and why the person is running away, is not known by the machine. Although the machine can capture information and determine what is happening, it does not understand why it is happening within full context – it is merely processing data. This reasoning ability, to bring understanding to a situation, is something that the human brain does very well.

Dispite all the technological advancements, can machines today only achieve the first two of the thee AI objectives. With this in mind, let’s explore the eight AI capabilities relevant and ready for use, today.

8 AI Capabilities

AI-8Capabilities

  • Capturing Information
    • 1. Image Recognition
    • 2. Speech Recognition
    • 3. Data Search
    • 4. Data Patterns
  • Determine what is happening
    • 5. Language Understanding
    • 6. Thought/Decision Process
    • 7. Prediction
  • Understand why it is happening
    • 8. Understanding

1. Image Recognition

This is the capability for a machine to identify/recognise an image. This is based on Machine Learning and requires millions of images to train the machine requiring lots of storage and fast processing power.

2. Speech Recognition

The machine takes a sound file and encodes it into text.

3. Search

The machine identifies words or sentences which are matched with relevant content within a large about of data. Once these word matches are found it can trigger further AI capabilities.

4. Patterns

Machines can process and spot patterns in large amounts of data which can be combinations of sound, image or text. This surpasses the capability of humans, literally seeing the woods from the trees.

5. Language Understanding

The AI capability to understand human language is called Natural Language Understanding or NLU.

6. Thought/Decision Processing

Knowledge Maps connects concepts (i.e. person, vehicle) with instances (i.e. John, BMW) and relationships (i.e. favourite vehicle). Varying different relationships by weight and/or probabilities of likelihood cn fine tune the system to make recommendations when interacted with. Knowledge Maps are not decision trees as the entry point of interaction can be at any point within the knowledge map as long as a clear goal has been defined (i.e. What is John’s favourite vehicle?)

7. Prediction

Predictive analytics is not a new concept and the AI prediction capability basically takes a view on historic data patterns and matches it with a new piece of data to predict a similar outcome based on the past.

8. Understanding

Falling under the third objective of AI – Understand what is happening, this capability is not currently commercially available.

To Conclude

In understanding the capabilities of AI you can now look beyond the hype, be realistic and identify which AI capabilities are right to enhance your business.

In a future blog post, we’ll examine some real live examples of how these AI capabilities can be used to bring business value.

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The Rise of the Bots

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Guest Blog from Robert Bertora @ Kamoha Tech – Original article here

The dawn of the rising bots is upon us. If you do not know what a Bot is, it’s the abbreviated form for the word Robot, and it is a term that is now commonly used to describe automated software programs that are capable of performing tasks on computers that traditionally were reserved for human beings. Bots are software and Robots are Hardware, all Robots need Bots to power their reasoning or “brain” so to speak. Today the Golden Goose is to build Artificial Intelligence (commonly known as AI) directly into the Bots, and the goal is, for these Bots to be able to learn on their own, either from being trained, or from their own experience of making mistakes. There is after all no evidence to suggest that the human mind is anything more than a machine, and therefore no reason for us to believe that we can’t build similar intelligent machines incorporating AI.

These days Bots are everywhere, you may not realise it so here are a few examples that come to mind:

Trading Bots: Trading Bots have existed for many years, at least 20 years if not more and are capable of watching financial markets that trade in anything from currency to company shares. Not only do they watch these markets, but they can perform trades just like any other Human Trader. What is more, is that they can reason out, and execute a trade in milliseconds, leaving a Human Trader in the dust.

Harvesting Bots were originally created by computer gamers who were tired of performing repetitive tasks in the games they played. Instead of sitting at their computer or consoles for hours killing foe for resources such as mana or gold, one could simply load up a Bot to do this tedious part of gameplay for you. While you slept, the Bot was “harvesting” game resources for you, and in the morning your mana and gold reserves would be nicely topped up and ready for you to spend in game on more fun stuff, like buying upgraded weapons or defences!

Without Harvesting Bots and their widespread proliferation in the gaming community we are all very unlikely to have ever heard of Crypto Currencies, you see it can be argued that these would never have been invented in the first place. Crypto Currencies and Block Chain technologies rely in part on the foundations set by the computer gaming Harvesting Bots. The Harvesting Bot concept was needed by the Crypto Currency Pioneers who used it to solve their problem of mimicking the mining of gold in the real world. They evolved the Harvesting Bot into Mining Bots which are capable of mining for crypto coins from the electronic Block Chain(s). You may have heard of people mining for Bitcoins and other Crypto coins, using mining Rigs and the Bots; the Rigs being the powerful computer hardware they need to run the Mining Bots.

What about Chat Bots? have you ever heard of these? These Bots replace the function of humans in customer service chat rooms online. There are two kinds of Chat Bots, the really simple ones, and the NLP (Neuro Linguistic Programming) ones which are capable of processing Natural Language.

Simple Chat Bots follow a question, answer, yes/no kind of flow. These Chatbots offer you a choice of actions or questions that you can click on, in order to give you a preprogramed answer or to take you through a preprogramed flow with preprogramed answers. You may have encountered these online, but if not, you will have certainly encountered this concept in Telephone Automation Systems that large companies use as part of their customer service functions.

NLP Chat Bots are able to take your communication in natural language (English, French etc..), making intelligent reasoning as to what you are saying or asking, and then formulating responses again in natural language that when done well may seem like you are chatting with another human online. This type of Chatbot displays what we call artificial intelligence and should be able to learn new responses or behaviours based on training and or experience of making mistakes and learning from these. At KAMOHA TECH, we develop industry agnostic NLP Bots on our KAMOHA Bot Engine incorporating AI and Neural Network coding techniques. Our industry agnostic Bot engine is used to deploy into almost any sector. Just as one could deploy a human into almost any job sector (with the right training and experience) so too we can do this with our industry agnostic artificially intelligent KAMOHA Bots.

Siri, Cortana and Alexa are all Bots which are integrated to many more systems across the internet, giving them seemingly endless access to resources in order to provide answers to our more trivial human questions, like “what’s the weather like in LA?”. These Bots are capable of responding not only to text NLP but also to voice natural language inputs.

Future Bots are currently being developed, Driverless vehicles: powered by Bots, any Robot (taking human or animal form) that you may see in the media or online in YouTube videos are and will be powered by their “AI brain” or Bot so to speak. Fridges that automatically place your online grocery shopping order – powered by Bots, buildings that maintain themselves: powered by Bots. Bot Doctors that can diagnose patients, Lawyer Bots, Banker Bots, Bots that can-do technical design, image recognition, Bots that can run your company? … Bots Bots Bots!

People have embraced new Technology for the last 100 years, almost without question, just as they did for most of Medical Science. Similar to certain branches of Medical Science, Technology has its bad boys though, that stray deeply into the Theological, Social, Moral and even Legal territories. Where IVF was 40-50 years ago, so too are our Artificially Intelligent Bots: pushing the boundaries, of normalities and our moral beliefs. Will Bots replace our jobs? What will become of humans? Are we making Robots in our own image? Are we the new Gods? Will Robots be our slaves? Will they break free and murder us all? A myriad of open ended questions and like a can of worms or pandora’s box, the lid was lifted decades ago. Just as sure as we developed world economies and currency in a hodgepodge of muddling through the millennia we are set to do the same with Bots; we will get there in the end.

It’s not beyond my imagination to say that if Bots replace human workers in substantial volume, then legislation will be put in place to tax these Bots as part of company corporation tax, and to protect human workers it is likely that these taxes will be higher than that of humans. If a bot does the work of 50 people? How do you tax that? Interesting times, interesting questions. My one recommendation to any one reading this, is do not fear change, do not fear the unknown, and have faith in the Human ability to make things work.

Love them or hate them Bots are on the rise, they will only get smarter and their usages will be as diverse as our own human capabilities. Brave new world.

Click on the image below to see our bots:

Bots AI Bots
KAMOHA BOTS

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6 reasons why learning Rainbird is beneficial for your career

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  1. You’ll be a better consultant

Rainbird’s human-centric automation is a unique emerging technology in the industry, and understanding how it works is a huge advantage – both in being able to sell a Rainbird solution to your clients, but also through being the gate-keeper for a desirable commodity.

  1. You’ll improve your analytical skills

The skills needed to break down what we call ‘subject matter expertise’ for Rainbird involve understanding a set of human inferences that are not widely understood in the wider RPA (robotic process automation) landscape or by automation consultancies. The nature of the subject matter itself is also very different: whilst the data on which human judgements are based has long been available as subject matter, human judgements, and how those judgements are reached, has never been subject matter for automation before. We’ve even had clients tell us that the process of mapping out their business logic has forced them into the invaluable exercise of confronting, and re-evaluating, their own thinking.

  1. You’ll look at things differently

Traditionally, RPA technologies require that decisions are broken down into formalised logic, requiring the removal of nuance and complete, unambiguous datasets and processes for successful implementation. Before Rainbird, there was an industry standard possible for if-this-then-that process automation; now, authors in Rainbird learn to structure their reasoning, a skill that is completely unfamiliar to most solution consultants.

  1. You’ll be able to do business with clients that no one else can help

Successfully replicating human reasoning, instead of relying on a decision tree, is industry-changing. Applying a new technology to use cases that we’ve never been able to automate before, due to the multi-faceted nature of human inference, provides an undeniable competitive edge.

  1. You’ll be a sought-after resource.

Maintenance of this emerging strand of unique automated reasoning technology is going to be a sought-after and exceptionally rare skill – you can capitalise on your Rainbird understanding as knowledge maps proliferate in the RPA marketplace.

  1. You’ll be able to maximise other technologies more scalably.

Infrastructure in process flow automation is maturing, with big players like Blue Prism and PEGA expanding in the space. Learning Rainbird – the only technology that can tie together these embedded process flow systems in the same way as human reasoning currently does – is crucial in maximising these flow techs scalably.

What is Artificial Intelligence: Definitions

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The term “Artificial Intelligence was first coined by John McCarthy in 1956. He is one of the “founding fathers” of artificial intelligence, together with Marvin Minsky, Allen Newell and Herbert A. Simon

Artificial Intelligence today is bathed in controversiality and hype mainly due to a misconception that is created by media. AI means different things to different people.  Some called it “cognitive computing”, others “machine intelligence”. It seems to be difficult to give a definition of what AI really is.

Different Definitions:

Wikipedia: “Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

English Oxford Living Dictionary:  “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”

Webster: ” A branch of computer science dealing with the simulation of intelligent behavior in computers. The capability of a machine to imitate intelligent human behavior.”

Google: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.”

Quartz: “Artificial intelligence is software or a computer program with a mechanism to learn. It then uses that knowledge to make a decision in a new situation, as humans do. The researchers building this software try to write code that can read images, text, video, or audio, and learn something from it. Once a machine has learned, that knowledge can be put to use elsewhere.”

Rainbird: “A computer doing a task that was previously thought would require a human.”

In my own words, keeping it simple: “AI is using computers to do things that normally would have required human intelligence.”

In other words, we might say that AI is the ability of computers/machines to use human knowledge modelled into algorithms and relational data, to learn from human reasoning and the associated conclusions/decisions, and use what has been learned to make decisions like a human would.

Thus can specialist (expensive) human knowledge be stored and processed, to make the decision making ability/application available to other non-specialist people (who do not have that specialised knowledge), empowering them to, through the use of the AI system, make a specialised decision.

Unlike humans, where specialists are numbered and constrained by human limitations, can AI-powered machines scale, don’t need to rest and they can process massively large volumes of information, can conduct tasks and make reasoning decisions at a significantly higher frequency and lower error ratio than humans, all at once!

Insightful Quotes on Artificial Intelligence

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Artificial Intelligence (AI) today, is a practical reality. It captivated the minds of geniuses and materialised through science fiction as I grew up. During the past 70 years (post WWII) AI has evolved from a philosophical theory to a game changing emerging technology, transforming the way digital enhances value in every aspect of our daily lives.

Great minds have been challenged with the opportunities and possibilities that AI offers.  Here are some things said on the AI subject to date. Within these quotes, the conundrum in people’s minds become clear – does AI open up endless possibilities or inevitable doom?

“I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”; Alan Turing (1950)

“It seems probable that once the machine thinking method has started, it would not take long to outstrip our feeble powers… They would be able to converse with each other to sharpen their wits. At some stage therefore, we should have to expect the machines to take control.”; Alan Turing

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”; John McCarthy (1956)

“AI scientists tried to program computers to act like humans without first understanding what intelligence is and what it means to understand. They left out the most important part of building intelligent machines, the intelligence … before we attempt to build intelligent machines we have to first understand how the brain things, and there is nothing artificial about that.”; Jeff Hawkins

“The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.”; Edsger Dijkstra

“Whether we are based on carbon or on silicon makes no fundamental difference; we should each be treated with appropriate respect.”; Arthur Clarke (2010)

“…everything that civilisation has to offer is a product of human intelligence. We cannot predict what we might achieve when this intelligence is magnified by the tools that AI may provide, but the eradication of war, disease, and poverty would be high on anyone’s list. Success in creating AI would be the biggest event in human history.”; Stephen Hawking and colleagues wrote in an article in the Independent

“Why give a robot an order to obey orders—why aren’t the original orders enough? Why command a robot not to do harm—wouldn’t it be easier never to command it to do harm in the first place? Does the universe contain a mysterious force pulling entities toward malevolence, so that a positronic brain must be programmed to withstand it? Do intelligent beings inevitably develop an attitude problem? …Now that computers really have become smarter and more powerful, the anxiety has waned. Today’s ubiquitous, networked computers have an unprecedented ability to do mischief should they ever go to the bad. But the only mayhem comes from unpredictable chaos or from human malice in the form of viruses. We no longer worry about electronic serial killers or subversive silicon cabals because we are beginning to appreciate that malevolence—like vision, motor coordination, and common sense—does not come free with computation but has to be programmed in. …Aggression, like every other part of human behavior we take for granted, is a challenging engineering problem!”; Steven Pinker – How the Mind Works

“Ask not what AI is changing, ask what AI is not changing.”; Warwick Oliver Co-Founder at hut3.ai (2018)

“Sometimes at night I worry about TAMMY. I worry that she might get tired of it all. Tired of running at sixty-six terahertz, tired of all those processing cycles, every second of every hour of every day. I worry that one of these cycles she might just halt her own subroutine and commit software suicide. And then I would have to do an error report, and I don’t know how I would even begin to explain that to Microsoft.”; Charles Yu

“As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.”; Amit Ray

“We’ve been seeing specialized AI in every aspect of our lives, from medicine and transportation to how electricity is distributed, and it promises to create a vastly more productive and efficient economy …”; Barrack Obama

“Artificial intelligence is the future, not only for Russian, but for all of humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.”; Vladimir Putin

“I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, I’d probably say that. So we need to be very careful.”; Elon Musk

“Whenever I hear people saying AI is going to hurt people in the future I think, yeah, technology can generally always be used for good and bad and you need to be careful about how you build it … if you’re arguing against AI then you’re arguing against safer cars that aren’t going to have accidents, and you’re arguing against being able to better diagnose people when they’re sick.”; Mark Zuckerberg

“Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don’t know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI.”; Yan Lecun

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we’re working on.”; Larry Page,  Co-Founder at Google (2000)

“If you had all of the world’s information directly attached to your brain, or an artificial brain that was smarter than your brain, you’d be better off.” – Sergey Brin Co-Founder at Goolgle (2004)