World growth is stalling. Trade wars hit manufacturers from Shanghai to Stuttgart to Seattle. But, as frightening as it may seem today, Industry 4.0 is alive and well, its greatest supporters say.
Industry 4.0 is a catch-all term for businesses running big data, enhanced robotics, and artificial intelligence systems. It is still expected to be a major driver of global growth over the next decade and beyond. Yes, even in preparation.
By 2035, this A.I.-powered push will provide the TR 14 trillion boost to the global economy, consulting giant Accenture estimates.
This is the prediction of Mark Carell-Billiard, Global Senior Managing Director at Accenture Labs, who broke these numbers during his keynote presentation at World Summit A.I. In Amsterdam on Wednesday. For example, he cites research that found progress in the growing area of A.I-powered automation: call centers. Five years ago, A.I. Bots can successfully resolve one out of every ten customer phone call. Today, it is 60%, he said.
In addition, he cited that this pressure to automate is not a job-killer, which economists out there fear.
Before the technicians take the victory lap, there is one exception.
They are not a threat to jobs, because "these systems are not very intelligent." AI - and its many iterations: machine learning, natural language processing, machine vision, image, and voice-recognition are highly specialized tasks. It can tell you what the weather will be like tomorrow or order movie tickets or find a way to get home faster during the evening commute. All types of businesses use A.I. Growing at the firm level to understand the vast flows of structured and unstructured data they collect to eliminate inefficiencies and save costs.
But, as Carell-Billiard puts it, A.I. There is still a blind spot. It is trained to understand certain data sets, without compromising meaning or context from the complex world. A.I. A specialist, not a generalist, he says. Therefore, a lot of work is needed to make these systems really intelligent.\
Gary Marcus, professor of psychology and neural science and rebooting AI at New York University, was more explicit in his assessment. He calls deep learning — a subset of A.I. It makes sense to have large amounts of data without supervision from human minds — a false name. While it is good for narrow-focus work, he questions its ability to revolutionize transportation (self-driving cars) and medicine (analyzing large-scale MRI scans for signs of cancer). “Deep learning is not an alternative to deeper understanding,” he says.
"The number of radiologists replaced by deep learning systems?" He asks. "Zero."
Carell-Billiard, for one, A.I. For systems to be truly effective they must be designed to be accountable, transparent and non-biased — not just super-fast task rabbits. Then only that systems reach their full potential?
World Summit A.I. On the first day off, much of the initial discussion was about the so-called moral A.I. Systems. Marcus and Carell-Billiard have challenged the development community to build A.I. Accountable, transparent and non-partisan systems.
If it is not responsible, Carell-Billiard says, “No one believes it and no one uses it.
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We, humans, are pretty good at sugar coating, but what if we only built an algorithm or bot brand or company for the purpose of marketing?
It does the most amazing work!In the early 2000s, finding a product would be a nightmare if we searched online stores without knowing the exact name of the product.
But now when we search for an item in any e-commerce store, we get all the results of that item.
Eva can gather knowledge from thousands of sources and provide simple answers in less than 0.4 seconds.Artificial Intelligence Applications - AI in BankingAI in bankingUsing AI to prevent fraud is not a new concept.
This has saved millions of dollars.
Issues such as climate change, population growth, and food security have pushed the industry to seek more innovative approaches to improving crop yields.Early-based agricultural tech called Peat has developed an app called Start-Up Planets, which identifies potential deficiencies and nutrient deficiencies in the soil through images.
Resolving the problem of bias and variation is only about coping with over-fitting and under-fitting.
Bias is minimized, and the variance concerning the complexity of the model is increased.Read Full Article: https://bit.ly/2NsIaF4
Artificial intelligence matches human performance by learning, deriving conclusions on its own, and executing a clearly defined task.
Machine Learning is an artificial intelligence technology that uses data to automatically discover patterns and trends, resulting in deriving more accurate predictions of future events.Many organizations are leveraging ML and AI to advance their product development, sales, and overall customer experience.
Industries like manufacturing, construction, energy etc.
Most companies understand the value of a safety culture and have ideologies about what a hyper-vigilant safety culture looks like — where employees are on high alert of their surroundings, perpetually evaluating risks for the task at hand and taking precautions accordingly.
Algorithms can identify objects, edges, and velocity because of the advancements of Machine Vision.Workplace Safety Solutions through AI Computer Vision:Process and Production Safety - Majority of the workplace facilities have a CCTV system installed on the premises.
Vision-based object detection and tracking use feature such as shape, color, motion etc.
IntroductionArtificial Intelligence and data wisdom are good fields of operations, systems, and further that end at replicating mortal intelligence through machines.
AI represents action-planned feedback of perception.Data Science uses different corridors of this pattern or circle to interrupt specific problems.
Perception, data scientists attempt to identify patterns with the assistance of the info.
planning, there are two aspects Artificial Intelligence Artificial Intelligence (AI) is that the reproduction of human knowledge measures by machines, particularly PC frameworks.
Explicit utilizations of AI contain master frameworks, normal language preparing, discourse acknowledgment, and machine vision.Artificial Intelligence is critical because it can give endeavors experiences into their activities that they could not have known about already and since, now and again, AI can execute assignments better than people.
Especially with regards to dull, thorough assignments like examining enormous quantities of authoritative archives to ensure significant fields are filled in appropriately, AI apparatuses regularly complete positions rapidly and with somewhat a few blunders.Data scienceData science, analytics, and machine learning demand are growing at an astronomical rate and top companies are now trying to find professionals in Data science who can sift through the goldmine of knowledge and help them to require business decisions efficiently.Data science may be a concept wont to tackle big data and includes data cleansing, preparation, and analysis.
The process of data annotation, for speech, image, video, audio, or text, is a highly specialized task that needs expertise.
When we talk about high quality data, it means that the labels are both accurate and consistent.
By outsourcing data annotation services, companies save time and effort freeing up their in-house data scientists to focus on areas they are experienced in instead of annotating data.Here are a few of data annotation techniques used for specific AI initiatives.Audio Transcription for Speech Recognition Audio transcription is used to train speech recognition models.
High-accuracy audio transcription enables interactions to accurately happen between human speech and AI-based models like smart TVs, phones, virtual assistants, watches, computers, or other in-home or on-the-road technology.Human speech must be accurately recognized to understand not only what words are being spoken but also what they mean.
Text annotation detects and labels words depending on a predetermined set of categories required by an AI company.
Text annotation for Natural Language Processing or NLP helps machines predict and understand the human language more easily.
