The AI Times Monthly Newspaper

Curated Monthly News about Artificial Intelligence and Machine Learning

Please Support Neurons.AI – The Professional Network for AI Practitioners

Neurons.AI is part of the Informed.AI Group and is focused on supporting the AI community with both online and offline activities. Informed.AI,established 3 years ago, is the AI Knowledge & Community Platform that runs, Events.AI and the Global Achievement Awards for AI, Awards.AI.

Neurons started as a purely an online network, allowing members to build their own profiles with specific details of the areas within AI they are most interested in, plus members can create their own blog articles and share with the rest of the community as well as participating in the online forums too. We also provide a messaging capability allowing direct contact with other members too.

The Neurons platform has recently grown to include offline meet up groups for a number of different Cities around the globe. Just launched the Melbourne chapter in September, we have volunteer leaders for a number of other chapters which will be having their first meetings soon, including Los Angeles, San Francisco, Bangalore, Singapore and others.

We believe through this platform, bring people together with shared interests and knowledge helps to support each other and provide a rich environment in which we can all be successful. Our membership is diverse, from business professionals, startups, academics, students and researchers. We find value in such diversity and see opportunities coming from such mixed groups.

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With such a fast paced industry we feel it is most important to provide a platform for sharing, and an opportunity for people to talk and exchange ideas. Help us build this into the best AI knowledge and community platform, by inviting your colleagues and friends, and sharing it on social media.

The Neurons platform is for its members, and mostly run by its members, and we are always looking for people to help run one of our chapters. If you would like to start a Chapter in your City please contact us and let us know. Visit the Chapters link at the top of your Neurons.AI homepage.

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Link to Full Article: Read Here

Humane Humanoids vs. Mercantile Homo Sapiens 

The fear of intelligent machines draws analogies with luddites’ rejection of 19th century industrial methods, increasing production at the cost of human labour, and 21st century concerns over foreigners taking jobs.

The 4th Industrial Revolution’s intelligent technologies will execute many jobs we humans do, but much better, in all sorts of industries including financial, legal, education and transportation. Machines will observe the results of their behaviour, modify their own programmes “so as to achieve some purpose more effectively” (Turing, 1950, p.449). Such intelligent machines are not the biggest existential risk to our species. The mercantile human is and has been throughout history.

The human species is a mercantile breed for whom trade has eclipsed benevolence over the centuries. Past great empires were enriched on the backs of human slaves (Frankopan, 2016). Vegetarian Leonardo Da Vinci’s ingenuity included military designs that could throw inflammable materials causing serious injury to enemies of the 15th century Milanese (White, 2001).

Humanity’s penchant for personal pleasure through cruelty to others is viscerally captured in the new TV series Westworld (2016). It depicts a future game world in which wealthy guests, the outsiders are hosted by humanoids on whom the vilest acts can be perpetrated through depraved scenarios designed by the narrative department at the adventure park. Human visitors act out their desires inflicting pain, after which each robot’s memories are wiped to erase the suffering in a session: “these violent delights lead to violent ends” (Shakespeare, quoted in Westworld, 2016). We need only open a newspaper on any given day and learn what horrors we humans wreak on each other.

Nonetheless, “limitations of the human intellect” (Turing, 1950, p.445) require us to develop smart machines. Guidelines on future and emerging technologies (Palmerini et al., 2014), applied by multidisciplinary teams learning from and reducing mistakes of the “sufficiently elaborate” machines (Turing, 1951, p. 473; Shah, 2013), even if they do “outstrip our feeble powers” could ensure they do not “take control” (p. 475). Advances in intelligent technologies, through following instruction and learning from experience, will produce driverless vehicles reducing road casualties, enhance student engagement through deployment of bots in pedagogy, medics diagnosing accurately and sooner, surgical operations increasingly error-free, deep learning programmes improving performance for our savings and investments, conversational humanoids attending the elderly and the unwell for dignified living, sophisticated programmes helping us to harness nature and prevent damage from weather-related disasters, and all seeing machines (Person of Interest, 2015), monitoring to protect our privacy and secure us from cybercriminals. Why would we not want this future?

Anxiety over future machines intent on malfeasant behaviour is valid, but so is the dread of the Homo sapiens who have put aside humane actions to ensure competitive trade advantages for their own particular group. Cooperative interdisciplinary human teams constructing social, cultural, ethical, moral and legally-binding technologies could lead to equitable sharing of the planet’s natural resources. Artificial Intelligence does not have to be a threat to humankind, it can help us to preserve our species for longer.



Frankopan, P. 2016. The Silk Roads: A New History of the World. Bloomsbury Paperback, London UK.

Palmerini, E., Azzarri, F., Battaglia, F., Bertolini, A., Carnevale, A., Carpaneto, J., Cavallo, F., Di Carlo, A., Cempini, M., Controzzi, M., Koops, B.J., Lucivero, F., Mukerji N., Nocco, L., Pirni, A., Shah, H., Salvini, P., Schellekens, M. and Warwick, K. 2014. Guidelines on Regulating Robotics. Deliverable D6.2 EU FP7 RoboLaw project: Regulating Emerging Robotic Technologies in Europe-Robotics Facing Law and Ethics, SSSA-Pisa. Report accessible from

Person of Interest (2015).

Shah, H. 2013. Conversation, deception and intelligence: Turing’s question-answer game. Chapter in Cooper, S.B. and van Leeuwen J. (Eds), Alan Turing: His Work and Impact, pp. 614-620. Elsevier.

Turing, A.M. 1951. Intelligent Machinery, A Heretical Theory. In Copeland, B.J. (Ed). The Essential Turing: The ideas that gave birth to the computer age. Oxford University Press, UK

Turing, A.M. 1950. Computing Machinery and Intelligence. Mind, Vol 59(236), 433-460

Westworld. 2016. HBO:

White, M. 2001. Leonardo Da Vinci: The First Scientist. Abacus paperback edition, London, UK.


Huma Shah is an AI Research Scientist and Senior Lecturer in the School of Computing, Electronics and Mathematics at Coventry University. She has a PhD in ‘Deception-detection and machine intelligence in practical Turing tests’ earned from Reading University. She has designed and conducted original experiments based on the ideas of 20th century mathematician/codebreaker Alan Turing to explore the intellectual capacity of machines through question-answer interviews. She has over 30 peer-reviewed articles in fundamental machine intelligence published in journals and presented at international conferences. Huma has co-organised two Loebner Prizes for Artificial Intelligence (2006 at UCL-UK; 2008 at Reading University-UK). She organised and chaired a ½ day public workshop on the ‘Benefits of Artificial Intelligence’ in July 2016. Huma collaborated on the EU FP7 funded RoboLaw project: Regulating Emerging Robotic Technologies in Europe: Robotics Facing Law and Ethics. The project’s dissemination included a final report, Guidelines on Regulating Robotics ( Huma feels deep learning machines could assist humans with the biggest challenges facing the Homo sapiens. Widening the stakeholder group to ensure ethical, legal, moral and social issues are embedded in intelligent machines could ensure we don’t sleep-walk into a world of smart technologies enslaving or annihilating humanity.

Link to Full Article: Read Here

First Melbourne Meetup – 27th Sept.

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

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. for instance allows sales managers to record their teams sales calls. Moreover, San Mateo-based 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 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

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

Join 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


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.


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 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

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

Our Exclusive Stories can also be viewed via

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.


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.

– 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.



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.

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



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.

– 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.



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.

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



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.

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



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.

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



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

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


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


We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including, 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


We are very pleased to announce a significant migration of website hosting and server upgrade for all of our websites, including, 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


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