AI News, Differences Between AI and Machine Learning, and Why it Matters artificial intelligence

How artificial intelligence boosts site conversions at Trans World Entertainment

Deep learning, an advanced form of artificial intelligence (A.I.), is all around us, and it’s growing increasingly ingrained in how we live and work.

Neural networks are excellent at rapidly processing and understanding unstructured data such as video, images, audio or large amounts of text without requiring intervention from human engineers, who could not possibly keep pace with the A.I.

Some practical examples of the predictive power of deep learning in action include the ability to increase online ad response without increasing overall advertising spend, delivery of more personalized offers for shoppers based on browsing history or habits, or instant, deep insights about a particular consumer based on their recent purchases or shopping behaviors.

recent global study co-sponsored by IBM and the National Retail Federation found that two in five retailers are already using intelligent automation (via deep learning or machine learning), and adoption is expected to double in the retail industry by 2021.

In contrast, deep learning is even more advanced than machine learning, and can handle much larger quantities of data very quickly for common retail applications, such as delivering a personalized offer to an online shopper in milliseconds that’s more likely to elicit a response.

When it comes to fashion sections of ecommerce sites, for example, deep learning can review and classify garments into different colors, styles, textures and seasons, and deliver the most likely “best fit” quickly and accurately enough, based on customers’ preferences, almost like a personal shopper.

As covered by Forbes this spring, “the greatest challenge of the 21st century so far for the retail industry has been adapting to the online world.” After consulting with more than 200 business leaders and experts, Kristina Rogers, global consumer leader for EY, the global professional services firm, predicts that A.I.

AI vs Machine learning

The internet today is booming with cloud, big data, machine learning, deep learning, IoT and AI (Artificial intelligence).

The reason is that these technologies are bringing the world closer by integrating various systems and performing human-like tasks improving productivity and saving loads of money and time!

You browse through various models, collect the data, read reviews – and leave it at that thinking of continuing back later.

Good machine learning systems should be able to prepare and interpret different types of data, have intelligent algorithms, follow iterative and automated processes and be scalable.

Machine learning can be of various types – supervised learning, unsupervised learning, partially supervised learning and active learning.

For example, a tennis game on Xbox, where you play against the computer, or human-AI interaction virtual assistants like Alexa, Cortana or Siri, who understand your questions and respond accordingly.

For example, two decades back, a game of chess with the computer was considered to be AI, but later on, every OS could implement it using a tree search algorithm which became quite predictable and dull.

We can thus safely infer that while the definition of machine learning is constant and limited, AI seems to have a definition that’s evolving with time and technological advancements.

However, it wasn’t until the 1990s that the scientists actually began creating programs to analyze huge amounts of data and learn from it by the way of statistical analysis.

Now that we have a fair understanding of what each term means, let us do a detailed comparison of both – From the differences, we find that machine learning has evolved from AI just like many other goals of AI.

It has become an essential part of all the businesses and currently, technologists are looking for ways to blend IoT and machine learning to find another break-through.

For example, when an AI system controls your car, you might want to think about how safe it will be for a system to completely handle a car that needs the human sensory organs to work accurately.

Artificial general intelligence

cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in)[11]

see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent), but not yet at human levels.

The most difficult problems for computers are informally known as 'AI-complete' or 'AI-hard', implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.[17]

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[18]

In the 1990s and early 21st century, mainstream AI has achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks, computer vision or data mining.[31]

Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various sub-problems using an integrated agent architecture, cognitive architecture or subsumption architecture.

Hans Moravec wrote in 1988: 'I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs.

for example, Stevan Harnad of Princeton concluded his 1990 paper on the Symbol Grounding Hypothesis by stating: 'The expectation has often been voiced that 'top-down' (symbolic) approaches to modeling cognition will somehow meet 'bottom-up' (sensory) approaches somewhere in between.

A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).'[34]

Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The Singularity is Near[1]

A 2017 survey of AGI categorized forty-five known 'active R&D projects' that explicitly or implicitly (through published research) research AGI, with the largest three being DeepMind, the Human Brain Project, and OpenAI based article.[45]

A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system or another computational device.

The computer runs a simulation model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.[46]

An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS).[49]

In 1997 Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps).[50]

(For comparison, if a 'computation' was equivalent to one 'floating point operation' – a measure used to rate current supercomputers – then 1016 'computations' would be equivalent to 10 petaFLOPS, achieved in 2011).

He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.

The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.

In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.[51]

The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 108 synapses in 2006.[53]

A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: 'It is not impossible to build a human brain and we can do it in 10 years,' Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford.[54]

Hans Moravec addressed the above arguments ('brains are more complicated', 'neurons have to be modeled in more detail') in his 1997 paper 'When will computer hardware match the human brain?'.[56]

The actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total).

fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence.

The first one is called 'the strong AI hypothesis' and the second is 'the weak AI hypothesis' because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test.

Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level.[70]

Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do[example needed].[70]

The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers from emulating the function of the human brain into a computer hardware.[73]

Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously can people then overlook solutions to problematic questions.[39]

possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance.[73]

There are other aspects of the human mind besides intelligence that are relevant to the concept of strong AI which play a major role in science fiction and the ethics of artificial intelligence:

It is also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent.

Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century because it would require 'unforeseeable and fundamentally unpredictable breakthroughs' and a 'scientifically deep understanding of cognition'.[79]

Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight.[80]

Four polls conducted in 2012 and 2013 suggested that the median guess among experts for when they'd be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081.

A growing population of intelligent robots could conceivably out-compete inferior humans in job markets, in business, in science, in politics (pursuing robot rights), and technologically, sociologically (by acting as one), and militarily.

For example, robots for homes, health care, hotels, and restaurants have automated many parts of our lives: virtual bots turn customer service into self-service, big data AI applications are used to replace portfolio managers, and social robots such as Pepper are used to replace human greeters for customer service purpose.[84] created to create a better world with AI that will precede any unleashed AI from militaries which are unfortuneately disproportunately overfunded in comparison with compassionate robots programmed by actual humanity.

A University Leader’s Glossary for AI and Machine Learning

These networks take a structural, layered approach to processing information, based on the way the human brain works: each layer of the network (made up of artificial “neurons”) provides the input for the next layer.

These AI programs range in sophistication from relatively simple and rule based (e.g., providing a canned response to a specific question) to more complex and AI-enabled programs, which can parse human language and learn from previous conversations to improve accuracy.

Because they can respond to a nearly limitless number of students at once, chat bots have the potential to provide real-time support at unprecedented scale -- which can streamline processes like admissions and enrollment and enable advisers to focus on students who need more hands-on, personalized guidance.

By using contextual clues, NLP can help machines make sense of what humans are trying to say -- for example, parsing the difference between trying to reach the accounting department and finding out the requirements for the accounting major.

In fact, admissions offices, beset by a torrent of questions when email became popular in the late ’90s, were among the first to use NLP to streamline operations -- and ensure rapid response to Gen X students who eschewed once-popular phones and call centers.

Albeit nascent, advances in NLP hold the potential to parse almost any form of communication, meaning that virtual assistants or chat bots can understand a question even if it’s asked in a string of emoji.

In order to realize the promise of technology to help students navigate and complete their college education, it’s critical for institutional leaders to understand how that technology works -- and how they can use it to meet their students’

Building a strong foundation in the language of emerging technologies should be any decision maker’s first step in exploring how these tools can fulfill the mission of all higher education institutions: to prepare students for a bright and successful future.

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