The AI Times Monthly Newspaper

Curated Monthly News about Artificial Intelligence and Machine Learning
What Watson and Einstein Mean for B2B Sales Teams

What Watson and Einstein Mean for B2B Sales Teams

What Watson and Einstein Mean for B2B Sales Teams

The introduction of AI-based capabilities into CRM systems such as IBM Watson and Salesforce Einstein is set to dramatically increase the effectiveness of B2B sales and marketing teams.

Machines are rising; and, therein lies an opportunity for sales leaders.

In March this year, Salesforce and IBM announced a deal that would integrate Salesforce Einstein with IBM Watson, a form of AI geared toward understanding unstructured information such as human language as opposed to computer language. For Marc Benioff, Salesforce’s CEO, the partnership caps a series of high profile acquisitions in the AI and workplace productivity spaces. His counterpart, IBM CEO Ginni Rometty, announced IBM was strategically investing in Bluewolf through a new practice to help clients deploy the combined IBM Watson and Salesforce Einstein solution.

AI for Everything versus AI for Everyone

While the depth and scope of IBM Watson’s capabilities have been described as “AI for everything”, Salesforce Einstein has been positioning itself as “Artificial Intelligence for everyone”. Within a year of its launch, the adoption of Salesforce Einstein has grown at a staggering pace and AI usage is increasing rapidly, in part thanks to its strategic collaboration with IBM. A study conducted by IDC suggests that over 80% of B2B sales teams are currently considering using artificial intelligence to improve the way they score leads and opportunities while 87% are looking at AI to enhance their reporting and forecasting. Einstein and Watson, though very different in their core functionalities, can act as a pair of virtual data scientists that connect to transform customer engagement across marketing, sales and customer service.

Currently a number of enterprises deploying AI in their sales and marketing processes are often placing “account-based everything” at the core of their automation. As CRM platforms incorporate AI into their automation capabilities, sales teams should expect their lead-to-revenue management tools to gain more teeth.

For example, the Einstein High Velocity Sales Cloud is frequently used by B2B sales teams to automate the capture of activity data or score leads. Einstein Analytics helps businesses make more informed decisions on campaign performance.  And this appears to be only the beginning. “AI is the next platform,” claimed Salesforce CEO Marc Benioff. “All future applications, all future capabilities for all companies will be built on AI.”

But IBM and Salesforce are not the only ones surfing the AI wave. An entire new generation of data-driven sales and marketing software vendors are leveraging AI to change the way businesses operate.

AI Assistants to Streamline Sales

Sales managers may choose from a variety of AI tools to improve or expedite their sales, from gatherings the latest news on a prospect, or detecting an event that may impact the course of a deal.

Toronto-based Nudge leverages machine learning to source news and social updates on both the individual and account-level decision makers by filtering data from across the web. This in turn helps sales reps have more informed conversations with their prospects and relate more effectively to a customer’s specific context.

Other AI assistants have made a niche for themselves by simplifying the tedious process of updating a CRM or facilitating sales training and coaching.  Gong.io for instance allows sales managers to record their teams sales calls. Moreover, San Mateo-based People.ai helps teams manage their time more effectively by applying natural language processing to emails, meetings and calls.

AI for Sales Leaders

Beyond automating workflows, simplifying tasks or recording activities, one, if not the most promising aspects of new AI platforms like Einstein and Watson are the way the allow sales leaders to leverage the predictive and prescriptive capabilities of AI. Sales leaders can derive valuable action-based insights on anything from resource allocation to compensation management and budget planning. For example Cien – a sales productivity app that complements Salesforce Einstein – goes beyond automating the capture of activity data or scoring leads. The app, which looks and behaves more like a fitness tracker for sales teams rather than a traditional business analytics tool, can calculate the optimal balance between inside and outside sales reps or measure the intangible, human factors that impact sales, feeding teams with personalized suggestions and actions that help a team achieve its goals.  

Beyond the Scope of Human Intelligence

As  Ray Wang, Principal Analyst and Founder of Constellation Research, points out, AI and machine learning demonstrate “the most potential in not only democratizing decisions, but also augmenting sales teams with actionable insight.” 

And whether it’s through platforms like Einstein and Watson or standalone apps like Nudge or Cien,  AI is poised to dramatically increase the effectiveness of B2B sales and marketing teams by going beyond the scope of human intelligence.

Neurons.AI (Australia – Melbourne) – First Event

Join Dr Andy Pardoe at the Neurons.AI Melbourne Chapter Meetup.

We are pleased to announce that the founder of Informed.AI will be joining the Melbourne Chapters first event held at the Honey Bar on the 27th September.

Speaking at the AI in the Enterprise Summit and the RPA Melbourne 2017 Conference, Dr Andy Pardoe is a regular international speaker on AI topics, and will be hosting the networking event on Wednesday 27th September at the Honey Bar 6pm to 9pm.

Come and chat with Andy and learn more about what is happening with AI and Machine Learning in London, UK.

For more details, check out Chapters.Neurons.AI or RSVP directly at the Meetup Group 

 


Link to Full Article: Read Here

Biohacking Meets Predictive Analytics

Biohacking Meets Predictive Analytics

If a biohacker can use data to predict when he’ll get the flu, then what are the possibilities for society?

Dressed entirely in black, Arina could be Neo from The Matrix or Locutus of Borg (when wearing a microphone). His appearance suggests he’s of another planet or a parallel universe. But the reality is that he’s visiting us from the future.Data speaksArina may be the world’s ultimate biohacker. With every breath he gathers data. He sleeps with a headset to track sleep patterns. Upon awakening he dons a biometric shirt. His morning coffee is a concoction of ten ingredients designed for an optimal start. His kitchen resembles a chemistry lab, allowing him complete control of what enters his body.

Read more

Neurons.AI London September Meetup

Neurons.AI London September Meetup

The next meet up of the London Chapter of Neurons.AI will be on Tuesday September 12th at our new venue hosted by Cocoon Networks.

Join us and meet fellow professionals working in AI, Machine Learning and Data Science. Discuss how AI and ML is and going to change the business world, and understand what enterprises need to do to adapt to this changing landscape.

New Bigger Venue thanks to Cocoon Networks
(with a capacity for 300 people)
Cocoon Networks – 4 Christopher St, EC2A 2BS – Downstairs

We will have two 30min talks

Agenda for the Evening

6:00 to 7:00 – Arrive and Networking
7:00 to 7:10 – Welcome and Intro from Cocoon Networks
7:10 to 7:50 – Bill Wong is Chapter Lead of DataKind UK
7:50 to 8:30 – Galiya Warrier is a Data Solution Architect at Microsoft
8:30 to 9:00 – More Networking (and Drinks)

>>>>>> Get your ticket now at EventBrite <<<<<<<

For more information visit http://Chapters.Neurons.AI

 


Link to Full Article: Read Here

Save £100 with homeAI.info at AI & Robotics THE MAIN EVENT

AI & Robotics THE MAIN EVENT provides exclusive insights into the impact of AI and robotics on business performance and working environment. This is your opportunity to cut through the hype and uncover the most effective ways to manage the impact of AI and automated technology on your clients, customers and employees.

Save £100 with homeAI.info

Use promo code HOMEAIINFO
Pay £95 – £395 (full prices £195 – £495)
Buy and secure your place here > 

Join homeAI.info on 14 September and find out what can be applied now and how to plan for upcoming developments.  Enjoy unmatched networking and knowledge share with business leaders from IBM, Ocado, London Stock Exchange, Imperial College London, and many more…

Key info & shortcuts

Tickets: £95 – £395 (+VAT)
Date: 14 September 2017
Venue: London | Victoria Park Plaza
Video: insights & highlight

Download the programme PDF if you’d like to have the key info in one file.


Link to Full Article: Read Here

Forums Fixed – Forums for Chapters Created

All,

We had a slight issue with our forums caused by the site migration. This temporary outage has now been fixed.

In addition we have added forums for our meet up chapters to support discussions and planning for each of our meet up groups

We are also looking for volunteers to help various chapters, contact us for more details.

r


Link to Full Article: Read Here

Build up your competitive position. Join us in our inaugural Y4iT PRO Lectures on Artificial Intelligence and Cybersecurity

Build up your competitive position. Join us in our inaugural Y4iT PRO Lectures.
The UP SYSTEM INFORMATION TECHNOLOGY FOUNDATION, INC. would like to invite you to participate in our Y4iT PRO Lectures, an event for professionals, set for September 6, 2017 at the Hall 1 of the SMX Convention Center Manila, Mall of Asia Complex in Pasay City, Philippines.
Y4iT PRO is geared toward data scientists, professors, web developers, mobile developers, researchers, security analysts, network and server administrators, postgraduate students, and other IT professionals interested in applying best practices and solutions on Artificial Intelligence and Cybersecurity.
The two half-day lectures will provide a development opportunity intended to augment the skills of IT professionals and to make them more effective and competitive in their profession.
Morning lecture 9:00AM – 12:00NN: Artificial Intelligence (Php 3,000.00)
§ Amazon Web Services AI
§ Harnessing the Power of IBM Watson in Your Applications
§ Innovating with Microsoft AI
§ Introduction to TensorFlow
Afternoon lecture 1:00PM – 4:00PM: Cybersecurity (Php 3,000.00)
§ Coding PHP Applications Like a Paranoid
§ Mobile Security
§ Next Generation Cyber Threat Intelligence
§ Website Hacking
To register, go to https://pro.y4it.org/register. Our official website is https://pro.y4it.orgRegister and pay now to secure your slot and be able to receive ₱10,000+ worth of giveaways after the event if you join both AI and Cybersecurity tracks.
Join us in these lectures and learn the essential tools and techniques.


Link to Full Article: Read Here

Free Individual Membership for Life

Free Individual Membership for Life

We are very pleased to announce that we are now offering free membership for individuals for life at Neurons.AI

Existing members will also benefit from this, and have had your membership changed to not have an expiry date for renewal.

If you would like your company’s staff to have corporate membership then please contact us at join@Neurons.AI

We hope everyone appreciated this commitment to supporting the AI community and hope you help spread the word and let your friends and colleague know about this great platform for collaboration and knowledge sharing.

 


Link to Full Article: Read Here

Announcing our Exclusive Stories

Announcing our Exclusive Stories

Over the next few months we will be sharing multiple sets of exclusive stories. Each series will have a theme.

Our exclusive stories will be visible on our new feed, but also permanently viewable via our AI magazine at homeAI.info/magazine

Our first series has the theme of Human interactions with AI applications and systems.

What If We Live Too Long? Life Extension and the Problem of Population

5 Ethical Quandaries Posed By The Rise of the Robots

Augmenting The Self: Enhancement, Evolution, and Ethics

Human Computer Interfaces: The Future Or Our Demise?

While we do this to enhance our News Stories on AI, we very much encourage user contributions to our platform, it is very easy to submit a News Story, Article or Press Release via homeai.info/news-stories/submit-story/

Our Exclusive Stories can also be viewed via homeai.info/blog/topics/exclusive-stories/

As always we welcome feedback and suggestions, particularly on themes for future exclusive stories.

7 AI Programming Languages To Choose From

7 AI Programming Languages To Choose From

Artificial intelligence hasn’t developed its own language yet, but even with using existing programming languages humanity has achieved great results. Just recollect the 2015 breakthrough of AlphaGo. It was the first time when a machine managed to beat a human being in the most difficult board game Go, which demands a high level of abstract thinking.
Let’s have a closer look at means that make artificial intelligence real.

Python

Homepage: https://www.python.org/
Initial release: 1991, latest release: 2017
OS: cross-platform

Python takes the first place in the list of AI development languages due to its simple and seamless structure. Simple syntax and rich text processing tool allowed it to become a perfect solution for NLP problems. Programmers can build neural networks in Python, and machine learning with Python is also much easier.

Features:
– short development time (as compared to Lips, Java or C++);
– large variety of libraries;
– high level sytax;
– supposrts object-oriented, functional and procedural styles of programming;
– good for testing algorithms without implementing them.

 

C++

Homepage: https://isocpp.org/
Initial release: 1983, latest release: 2104
Influenced: Java, Python

The major advantage of C++ for AI is its speed, and one can find C++ among the fastest programming languages in the world. Since AI development demands lots of calculation fast-running programs are of ultimate importance. C++ is highly recommended for machine learning and neural network building.

Features:
-high level of abstraction;
– good for high performance;
– organize data according to object oriented pricniples;
– STL collection.

 

Lisp

Homepage: http://lisp-lang.org/
Initial release: 1959
Influenced: Python

Lisp, being the second oldest programming language in the world (after Fortran), still holds a top position in AI creating due to its unique features. For example, Lisp has a special macro system which makes possible to develop a domain specific level of abstraction and build the next level on it. Lisp in artificial intelligence development is known for its unique flexibility as it adapts to the problem you need to solve on the contrary to the other languages that are chosen because they can complete this or that task. Developers opt for Lisp in machine learning and inductive logic projects.

Features:
– fast prototyping capabilities;
-support for symbolic expressions;
– automatic garbage collection which actually was invented for the Lisp language;
– library of connection types including dynamically-sized lists and hastables;
– efficient coding due to compilers;
– interactive evaluation of components and recompilation of files while the program is running.

 

Prolog

Initial release: 1972
Influenced: Mercury, XSB
Dialects: Edinburgh Prolog, ISO Prolog

The name of Prolog speaks for itself; it’s one of the oldest logic programming languages. If we compare it with other languages, we can see it is declarative. It means that the logic of any program will be represented by rules and facts. Prolog programming for artificial intelligence can create expert systems and solving logic problems. Some scholars claim that an average AI developer is bilingual – they code both Lisp and Prolog.

Features:
– pattern matching;
– tree-based data structuring;
– good for rapid prototyping;
– automatic backtracking.

 

Java

Homepage: https://www.oracle.com/java/index.html
First release: 1995, latest release: 2014
OS: cross-platform

Java is an object-oriented programming language that follows the principle of WORA (“write once, read everywhere”). It runs on all platforms without any additional recompilation due to Virtual Machine Technology. Some more advantages of Java is that this language is easy to use and easy to debug. However, in term of speed, it loses against C++. Java AI programming is a good solution for neural networks, NLP and search algorithms.

Features:
-in-build garbge collection;
– portable;
– easy to code algorithms;
– scalability.

 

Haskell

Homepage: https://www.haskell.org/
Initial release: 1990, latest release: 2010
OS: cross-platform

Haskell is a purely functional programming language that can boast about its lazy evaluation and type interface features. LogicT monads facilitate expressing non-deterministic algorithms, and algorithms can be expressed in a compositional way.

Features:
– major algorithms available via cabal;
– CUDA binding;
– compiled to bytecode;
– can be executed on multple CPU in cloud.

 

AIML

Homepage: http://www.alicebot.org/aiml.html
Initial release: 2001, latest release: 2011
Extended from: XML

AIML (Artificial Intelligence Markup Language) is a dialect of XML used to create chatbots. Due to AIML one can create conversation partners speaking a natural language.
The language has categories showing a unit of knowledge; patterns of possible utterance addressed to a chatbot, and templates of possible answers. To know how it works check out this article about building a chatbot.

 

So, the matter of best-something is rather philosophical in any sphere, and AI development is not an exception. There are a lot of factors influencing the choice of programming languages for an AI project. It depends on functions you need to create, usage and even your taste in some cases. However, more and more AI programmers are using Python as it’s a simple and powerful tool, while C++, Prolog and Lisp can be called runners-up in this race.

The article was originally posted at https://qubit-labs.com/ai-programming-languages-choose/.

The Reality of the Artificial Intelligence Revolution

The Reality of the Artificial Intelligence Revolution

According to Gartner, over 85% of customer interactions will be managed without a human by 2020.

We have seen a machine master the complex game of Go, previously thought to be the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks and all of this has given rise to a flurry of people claiming that the Artificial Intelligence revolution is already upon us.

However, while there is no doubt that there have been significant advancements in the field of AI, what we have seen is only a start on the path to what could be considered full AI.

Understanding the growth of Artificial Intelligence capability is crucial for understanding the advances we have seen. Full AI, that is to say complete, autonomous sentience, involves the ability for a machine to mimic a human to the point that it would be indistinguishable from them (the so-called Turing test). This type of true AI is still a long way from reality. Some would say the major constraint to the future development of AI is no longer our ability to develop the necessary algorithms, but, rather, having the computing power to process the volume of data necessary to teach a machine to interpret complicated things like emotional responses. While it may be some time yet before we reach full AI, there will be much more practical applications of basic AI in the near term that hold the potential for significantly enhancing our lives.

With basic AI, the processing system, embedded within the appliance (local) or connected to a network (cloud), learns and interprets responses based on “experience.” That experience comes in the form of training through using data sets that simulate the situations we want the system to learn from. This is the confluence of Machine Learning (ML) and AI. The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. It is this type of AI that is getting the most attention. In the next ten years, the use of this kind of ML-based AI will likely fall into two categories:

  1. Improvement and automation of daily life: Managing household tasks, self-driving cars and trucks and the general automation of tasks that robots can perform significantly faster and more reliably than humans
  2. Exploration and development of new trends and insights: Artificial intelligence can help accelerate the rate discovery and science happening worldwide every day. The use of Artificial Intelligence to automate science and technology will drive our ability to discover new cures, technologies, tools, cells, planets, etc., ultimately pushing artificial intelligence itself to new heights.

There is no doubt about the commercial prospects for autonomous robotic systems in the commercial market for aspects such as online sales conversion, customer satisfaction, and operational efficiency.   We see this application already being advanced to the point that it will become commercially viable, which is the first step to it becoming practical and widespread. Simply put, if revenue can be made from it, it will become self-sustaining and thus continue to grow. The Amazon Echo, a personal assistant,  has succeeded as a solidly commercial application of autonomous technology in the United States.

In addition to the automation of transportation and logistics, a wide variety of additional technologies that utilize autonomous processing techniques are being built. Currently, the artificial assistant or “chatbot” concept is one of the most popular. By creating the illusion of a fully sentient remote participant, it makes interaction with technology more approachable. There have been obvious failings of this technology (the unfiltered Microsoft chatbot, “Tay,” as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. This is also a hugely important application of AI as it will bring technology to those who previously could not engage with technology completely for any number of physical or mental reasons. By making technology simpler and more human to interact with, you remove some of the barriers to its use that cause difficulty for people with various impairments.

The use of Artificial Intelligence for development and discovery is just now beginning to gain traction, but over the next decade, this will become an area of significant investment and development. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered.

There is also the tantalizing possibility that as we increase the capability of our AI systems, they could actually perform research and discover new avenues to explore. While this is still a long way away, it could greatly accelerate the discoveries needed for many advancements that could improve and extend our lives.

The dystopian vision of robots assuming complete control of society is unlikely; the nuances of perception, intuition, and plain old “gut-check reactions” still elude machines. Learning from repetition, improving patterns, and developing new processes is well within reach of current AI models, and will strengthen in the coming years as advances in Artificial Intelligence – specifically machine learning and neural networking – continue. Rather than being frightened by the perceived threat of AI, it would be wise to embrace the possibilities that AI offers.

Announcing our Facebook Pages

Announcing our Facebook Pages

All,

We wanted to take this opportunity to announce the launch of our Facebook pages which align to a number of our websites, including;

Major Server Upgrade for all Informed.AI sites

All,

We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including homeAI.info, Neurons.AI, Showcase.AI, Awards.AI, Events.AI and Informed.AI

This has been a major upgrade for us and has been executed very smoothly with zero downtime.

You should all experience much faster response and performance to all our websites making the user experience much more enjoyable.

As always we welcome your feedback

The Informed.AI Team


Link to Full Article: Read Here

Major Server Upgrade for all Informed.AI sites

Major Server Upgrade for all Informed.AI sites

All,

We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including homeAI.info, Neurons.AI, Showcase.AI, Awards.AI, Events.AI and Informed.AI

This has been a major upgrade for us and has been executed very smoothly with zero downtime.

You should all experience much faster response and performance to all our websites making the user experience much more enjoyable.

As always we welcome your feedback

The Informed.AI Team

 

What If We Live Too Long? Life Extension and the Problem of Population

It’s no secret that the world’s population is reaching crisis point – and still growing fast. Most developed nations are also encountering the problem of an ageing population, where there are more elderly people than those of working age. The fact is that people are living longer, and, if technology is to have its way (and it will), we will all be living even longer ourselves.

What Technology Is This, Precisely?

Well, as you’d expect, the main body of work in the anti-ageing space is in finding drugs that will stop the process of ageing in its tracks. From Google’s Calico, whose mission is to reverse engineer the biology that controls lifespan and “devise interventions that enable people to lead longer and healthier lives”, to other Silicon Valley players such as Human Longevity, which, in conjunction with StartUp Health, launched the Longevity Moonshot, and many more besides, intrepid warriors against death are working hard to reverse, reduce, and eliminate the effects of ageing and causes of death. All of the above centre on medical prevention through drugs.

There are, however, more mechanistic methods on the cards. Nanobots that are injected into your bloodstream that can identify health problems and mend them as they go along, infusing affected areas with drugs or performing tiny surgeries as part of their day-to-day lives, for example.

Then, of course, there’s the mind upload thing – the idea that we could all live forever as virtual avatars, in a world where disease would be eliminated because we would be free of biology altogether.

Artificial intelligence and robotics, as the technologies progress, have the potential for radical life extension by making us incrementally bionic until the faulty biological body is subsumed to the stronger, healthier bionic ideal. We can add bits on, chop bits off, until we’re happy with a body that will keep our conscious minds alive indefinitely. With the integration of robotic parts and artificial intelligence add-ons, we could also experience what renowned futurist, Ray Kurzweil, calls “life expansion” – where we don’t just live longer, we live in ways we had never before conceived, capable of doing any single thing we could possibly imagine. Sounds good, right?

The Drawbacks to Life Extension

Firstly, if we are all living longer and being healthier, then where on earth are we going to put everyone? In the UK alone, we are currently struggling to build enough new homes to meet the growing population – we fall short by several hundred thousand year on year. So what happens when we end up with more people than we can house or to provide the necessary infrastructure to support?

With more people living longer, the retirement age must necessarily go up. So, we’ll all need to be working for longer, too. We are also in the midst of an automation revolution. Artificial intelligence and robotics are threatening to destroy existing jobs. The solution proposed is that different roles will develop as a result, but will we be able to retrain such high numbers of workers for the new working landscape?

Not only will we have lots more people to house and to build infrastructure for, we will also have a large surplus workforce.

If we are talking about a future in which we can all live a terrifically long time, but few of us able to afford to support ourselves or to even live in a suitable home, then is it really worth it?

If a long life becomes a pleasure to be enjoyed by those with the means to do so, and the rest of us suffer and die just as we have always done, does that mean that financial wealth make one’s life more valuable? If wealth could be correlated positively with intelligence, creativity, innovation, morality, or even (arguably) beauty, then perhaps there would be an argument in favour of this eventuality. Unfortunately, the world just does not work like that.

So, What’s The Answer?

If we look back to the early days of the smartphone or the personal computer, we see that prices were phenomenally high. But things change. Tech becomes cheaper and more people have access over time. So there’s reason to believe that this will be the case for life-extension drugs and technology.

This does not address the problem of what we do with our excess population. Elon Musk reckons that colonising Mars is the solution – so we have two planets to spread ourselves over. And as our tech continues to evolve, we may be able to then colonise further into the galaxy. Sounds legit.

Feeding, housing, and employing a considerably large excess population, even if we are to colonise other planets, won’t be without its drawbacks, either.

Sure, we can automate food production even further than we already do, but we may have to make drastic changes to our diet in order to do so. One of the key aspects to this, regardless of tech or life extension, is to begin eliminating meat from our diets. It is a well-known statistic that 95% of Amazon rainforest destruction is down to cattle farming. Combined with the general health risks associated with red meat, we will need to stop eating cows.

This will bring cattle populations down, but will also allow these animals a better quality of life. There are lots of people hard at work on strategies to replace mammal meat in our diets with that of insects. Whilst that may gross a lot of people out, there is essentially no real problem with it. If we are to continue to thrive on this planet, we need to change our perceptions about how we live; how we eat is a central part of that. And insects may allow us to feed more people with less environmental impact.

As for housing, part of the problem is – arguably – our insistence on national borders. Rather than cosseting ourselves off from one another, a more even distribution of humanity should be considered. This will require another massive overhaul in how we live, but again, if we are to continue to thrive and to find a solution to overpopulation, we will need to let go of stubborn, xenophobic, outdated ideas and work together as a species.

The employment issue is perhaps the toughest one to answer. Whilst, invariably, new roles will emerge, we will need to have a radical rethink of how people sustain themselves. Universal basic income is one solution being posed, championed by Bill Gates and discussed at length by others.

However, all these solutions require us to completely revolutionise how we have been living for centuries. The world is still largely run by privileged, older, white people who are reluctant to see a world beyond that which they are used to. If we are to take the idea of living longer and better seriously, we are going to need a lot more innovative, broad-minded ideas to come to fruition.


Link to Full Article: Read Here

Is Artificial Intelligence Really the Next Technological Revolution?

Is Artificial Intelligence Really the Next Technological Revolution?

Is Artificial Intelligence Really the Next Technological Revolution?

A comparison of AI with previous technological breakthroughs

There’s no shortage of hype around artificial intelligence. Fueled by recent scientific advances in the field, AI is now characterized as the “new electricity”—a technological breakthrough that will revolutionize the world.

But are we sure that’s the case?

Many booms and busts have punctuated AI’s nearly half century of history. Excessive expectations and promises, which drove the first AI bubble in the 1980s, have been followed by decreased funding and interest — the so-called “AI winters.” But this time feels different. Five billion in venture capital was funneled into AI last year. Coupled with recent acquisitions of AI startups by tech companies such as Facebook, Google, and Apple, and the exploding interest by other companies — reflected in the skyrocketing mentions of AI in company earnings calls — it seems rather obvious that AI is here to stay.

But is AI indeed the next major technological revolution? Is there a generic structure of technological revolutions that can be identified historically? If so, can the insights of previous technological revolutions be applied to AI? And if AI represents a major technological breakthrough that is comparable to electricity and steam, in which phase of its development do we currently find ourselves?

In her work on the economics of innovation and technological change, socio-economist Carlota Perez has traced the discontinuities and regularities in the process of innovation. Similarly to Thomas Kuhn’s work on the nature of scientific discoveries — in which scientific revolutions disrupt the process of science and trigger the formation of new scientific paradigms — Perez identifies a sequence of technological revolutions and “techno-economic paradigms” that have disrupted our industries and societies.

A technological revolution — which locally disrupts a specific market or industry in terms of new inputs, methods, and technologies — becomes a techno-economic paradigm when it starts to globally transform organizational structures, business models, and strategies in markets and sectors beyond which the technological breakthrough had initially erupted. Techno-economic paradigms, in other words, represent a collectively shared best practice model of the most successful and profitable uses of the new innovations. By enabling the wide-spread diffusion and adoption of the emerging technologies across economies and societies, techno-economic paradigms will fundamentally affect our socio-institutional frameworks.

As Perez has shown, two distinct phases can be identified in each technological revolution. There is an “installation” phase, in which innovators and entrepreneurs explore the potential of the new technology. In this phase, the diffusion of a breakthrough technology is often driven by a financial bubble. The installation phase is followed by a “turning point” or phase of readjustment — in which the bubble bursts — and the “deployment” period, which diffuses the new technological system across industries, economies, and societies.

Each technological revolution can be characterized further in terms of a specific life cycle, which, as Perez documents, tends to last around half a century (see image 1). Perez identifies four distinct phases within such a life cycle: an initial period, which is characterized by explosive growth and innovations and new products; a phase of constellation, in which new industries, infrastructures, and technology systems are built out; the full expansion of innovation; and the last phase, which is defined by technological maturity and market saturation.

Perez defines a technological revolution as a set of interrelated radical breakthroughs — that is, singular innovations — that form a constellation of interdependent technologies. A technological revolution, in other words, is a cluster of clusters, or a system of systems of technological innovations. The recent major breakthrough in information technology, for example, formed such a technology system around microprocessors and other integrated semiconductors, from which new technological trajectories opened up: personal computers, software, telecommunication, and the internet emerged from the initial technological system. These new technological systems subsequently created strong inter-dependence and feedbacks between technologies and markets. The defining features of technological revolutions — as opposed to a random collection of singular innovations — are thus the following: (1) they are interconnected and interdependent in their technologies and markets, and; (2) they have the disruptive potential to radically transform the rest of the economy and society.

Historically, Perez identifies five such major technological meta-systems, which were initially triggered by a technological (or scientific) breakthrough and, then, expanded across industries and economies. The first such disruption of the late 18th century was organized around the mechanization of factories, water power, and the canal networks. This was followed by the second revolution, which initiated the age of steam and railways. In the late 19th century, electricity, steel, and heavy engineering intensified international trade and globalization. In the last century, two technological revolutions transformed our economic and industrial system: the age of oil, mass production and the automobile was followed by the era of information and communication technology.

What made these technological disruptions revolutionary were not only the new interrelated technologies, industries, and infrastructures but their transformative potential defined in terms of extraordinary increases in productivity that they enabled. When a technological revolution propagates across industries and economies, it radically transforms the cost structure of production by providing new powerful inputs (such as steel, oil, or microelectronics). Thereby, it unleashes new innovations and interrelated technological systems, which renew existing industries and create new ones.

Perez provides a powerful framework that can be applied to the current state of AI. Given Perez’ conceptual model of the diffusion of technological innovations, the new AI industries and systems that are forming now can be located between phase one, the period of “paradigm configuration,” and the phase of “full constellation,” in which new industries emerge and infrastructures get installed. The explosive growth and innovation we are experiencing at the moment typically characterizes phase one. While new industries, technological systems, and infrastructures emerge in phase two — which results in intensified investment and market growth — the technological revolution is transforming its core industries, but has not yet permeated economies and societies as a whole.

While the recent extraordinary investments in AI might lead to another bubble — which might indicate the “turning point” or phase of readjustment in Perez’ model — it seems that the economic space today is, indeed, different than during the last AI bubble (or, perhaps, the bursting of the first AI bubble in the 1980s already marked the “turning point” — over-inflated expectations crashed when cheap UNIX workstations triggered the fall of over-priced expert systems running on LISP and the Dreyfus brothers published their Mind over Machine, which undermined some of the pretentious and flawed assumptions of the first generation of AI research). Not only has there been massive growth in computation, GPUs, storage, datasets, user demand, high levels of R&D, and VC investment, but governments have also started novel AI initiatives. The UK government recently announced increased funding for AI research; the Chinese government gave AI priority status in R&D and commercialization; and the US government has funded AI research last year with more than $1 billion.

The role of public R&D is singularly important for technological revolutions as the previous five major technological surges have all been, to some extent, government-sponsored (such as the canals and railways networks, or the Internet, which has been heavily funded by government agencies such as DARPA). Historically, the synergistic financing of governments and financial capital, such as venture capital, has been crucial for the diffusion and adoption of technological breakthroughs and their consolidation into techno-economic paradigms.

But in what sense, then, does AI share the features of the previous technological revolutions that can be historically identified? The emerging AI technology systems clearly exhibit the interconnectedness and interdependence in their technologies and markets, which characterize the previous technological revolutions. AI represents not just another new dynamic industry that is added to the existing production structure; rather it provides the means to modernize almost all existing industries and activities. New AI-powered industries and infrastructures are forming at the moment that not only fundamentally re-organize existing industries, but have started to deeply affect organizational structures, business models, and strategies. As it was the case with steam and electricity, these technological and scientific breakthroughs are not only productivity-enhancing in the core industries but are beginning to permeate various peripheral sectors and markets.

In this sense, AI has all the features of what economists call a general purpose technology (GPT). In economics, a GPT is defined as a generic technology, which (1) can be improved, (2) can be widely used and applied, and (3) expands the space of possible innovations and investments. Similar to historical GPTs, such as the steam engine, electricity, or microelectronics, these new interconnected and interdependent AI-based technology systems and markets have not only the potential to enable innovations in products, processes, and organizational structures — as previous GPTs did — but also to radically transform our economic, social, political structures.

AI has all the defining features of previous technological revolutions — it is becoming a cluster of interrelated generic technologies and organizing principles that are starting to spread far beyond the confines of a specific industry. At the core of all the previous technological revolutions has been an all-pervasive low-cost input, often a new material or energy source combined with novel products, processes, and infrastructures. Similar to electricity, steam, or microelectronics, AI — fueled by GPU-accelerated computing, massive increases in available data, and drastically reduced costs — seems to be on the cusp of becoming such a cheap and ubiquitous new input. Similar to steam in the 18th century or electricity today, distributed AI could soon power almost all products and processes and deeply permeate existing and novel infrastructures and industries.

Indeed, AI could become the “new electricity.” We are not there yet. But given Perez’ model of diffusion and adoption of technological innovation, we may indeed be at the cusp of a revolution.

AI STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

AI STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

STARTUP SHERPA WINS THREE PRESTIGIOUS AWARDS IN TWO MONTHS

  • SHERPA has been awarded three major awards in the past two months. The Red Herring 100 Winner Europe 2017, the White Bull Award and the Best Mobile App Award
  • SHERPA was a finalist at CognitionX, an awards ceremony in London which recognises excellence in Artificial Intelligence, where it was surpassed only by Deep Mind (Google)
  • SHERPA has also been featured by the technology consultancy GARTNER as one of the top three intelligent apps of the moment

 

Bilbao, June 29th, 2017 – SHERPA, the predictive Artificial Intelligence platform, has been recognised as one of the best European startups, winning three major awards in the past two months. SHERPA, a personal assistant which integrates with smart devices, has been awarded the Red Herring Europe Winner 2017, the White Bull Award 2017 and the Best Mobile App Award.

The Red Herring Top 100 Europe celebrates the top private companies in the European region. Red Herring’s editorial team analyses hundreds of cutting edge companies and technologies and selects those that are positioned to grow at an explosive rate.

The White Bull Awards, held in association with the multinational Qualcomm, bring together Europe’s top technology and media leaders, entrepreneurs, innovators, investors, and visionaries. They select the best startups in Europe based on the criteria of innovation, leadership, growth and potential for growth.

CognitionX, in association with the Alan Turing Institute, awards the best and most innovative contributions to Artificial Intelligence of the year in a ceremony in London. SHERPA was shortlisted in the Best Innovation in Artificial Intelligence category, surpassed only by Google Deep Mind.

Following on from these successes, SHERPA’s mobile app has won the Best Mobile App Interface Award.

As a result of the above and because of SHERPA’s new algorithms, SHERPA’s app has also been recognised by GARTNER (the prestigious technology consultancy) as one of the three intelligent apps of the moment.

These aren’t the only awards SHERPA has been given recently. In December 2016, SHERPA won the prestigious Digital Top 50 Award. The Digital Top 50 Awards were founded by Google, McKinsey y Rocket Internet to recognise and reward bold talent, cutting-edge innovation and sharp business acumen amongst the most promising European startups.

 

About SHERPA

SHERPA is the start-up responsible for creating the Predictive Personal Assistant that directly competes with those developed by Google and Apple. With headquarters in Bilbao, the platform is based on the most advanced technology and algorithms in Artificial Intelligence.

In 2015, SHERPA reached an agreement with Samsung to install its software in the Korean company’s devices, last year SHERPA raised $6.5 million in a Series A round of financing.

 

You can find more information on SHERPA here: http://sher.pa

 

Related links:

 

For more information (press): Text100 – Virginia Huerta (Virginia.huerta@text100.es) +34 91.561.94.15 / press@sher.pa

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