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Valuing the Artificial Intelligence Market, Graphs and Predictions

In order to put together an executive brief for market size and projected growth of AI, I’ve molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes.

The goal of this article is do provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade, and to (as always) provide amble references for further exploration for those of you who aim to go deeper. We’ve aimed to stick to sources whose reputation rests on their objectivity, rather than on excited statements of companies whose incentive is to see the future their way (such as IBM’s CEO claiming a potential $2 trillion dollar market for “cognitive computing”).

(The following profiles and forecasts are updated on a quarterly basis) Select quotes: In 2017, these technologies will increase businesses’ access to data, broaden the types of data that can be analyzed, and raise the level of sophistication of the resulting insight.

The vast majority of firms believe that having an organizational model that supports analytics is critical to breaking down the silos of customer knowledge that exist throughout the enterprise…Enterprises are starting to show signs of elevating the priority of, and investment in, initiatives to get rid of existing silos.

3) Leading CI practices will be the poster child for business transformation Select quotes: Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025.

The chart below shows Tractica’s top 10 AI use cases in terms of revenue in 2025: Tractica has assumed a somewhat conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven.

Spiderbook’s current data visualization of companies investing in AI: Study context: Accenture, in association with Frontier Economics, modeled the potential impact of AI for 12 developed economies that together generate more than 50 percent of the world’s economic output.

and To fulfill the promise of AI as a new factor of production that can reignite economic growth, relevant stakeholders must be thoroughly prepared—intellectually, technologically, politically, ethically, socially—to address the challenges that arise as artificial intelligence becomes more integrated in our lives.

“The average business expects to spend $8 million on big data-related initiatives this year,” according to the Kearney report, which also says each IT job created in the process of upgrading will create three additional jobs outside IT.

Forrester says that in 2016, machine learning will begin to replace manual data wrangling and data governance dirty work, and vendors will market these solutions as a way to make data ingestion, preparation, and discovery quicker.

Autonomous Agents and Things Machine learning gives rise to a spectrum of smart machine implementations — including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors — that act in an autonomous (or at least semiautonomous) manner.

Select quotes: 1)    By 2018, 20 percent of business content will be authored by machines. Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content…

2)    By 2018, six billion connected things will be requesting support. In the era of digital business, when physical and digital lines are increasingly blurred, enterprises will need to begin viewing things as customers of services — and to treat them accordingly…

6)    By 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines. Gartner believes the initial group of companies that will leverage smart machine technologies most rapidly and effectively will be startups and other newer companies…

There are likely to be leaps and bounds in the next decade in gleaning insights from “unstructured data”, while applying predictive analytics and building business models is a more oft-used approach to implementing machine learning and data mining technologies at present.

Unconventional Ways Artificial Intelligence Drives Business Value

Demonstrating what is possible with Idemandu, a Forbes article quotes this conversation: You: “Hey Idemandu, could you ask my massage therapist to come to my place tonight preferably after 8?

A survey conducted at the EmTech Digital conference revealed that respondents saw AI affecting these top three business outcomes: We can see this impact in nearly every sector in business.

Here are three sectors where the impact of AI has been seen the most:   86% of healthcare provider organizations, life science companies, and health technology vendors are using AI technology, says a 2016 report from CB Insights.

Few areas where AI is being used in healthcare are: 1) Data management – Medical records and other patient data can be accurately analyzed, stored and used to provide healthcare businesses with the right information at the right time.

   3) Precision Medicine and Drug Discovery – By screening complex compounds and existing medicines for specific attributes, drug candidates for pre-clinical drug discovery and development can be rapidly identified.

AI can also help detect diseases and predict hereditary health issues more accurately and help design precision medicines for specific genetic make-ups.

For more applications of AI in the healthcare industry: The assimilation and analysis of financial data is where AI shows its true potential, but there is much more that AI can do in the financial sector.

Here are a few applications: Read more about how predictive algorithms and AI will rule financial services:  AI is being used widely in the transportation sector and these are a few areas where it is making an impact: Read more about how connected transportation will disrupt the world: As you can see, the implications for AI in business is tremendous.

5 Artificial Intelligence Trends To Watch Out For In 2019

During 2018, we witnessed a dramatic rise in the platforms, tools, and applications based on Machine learning and artificial intelligence.

While inferencing, the model needs additional hardware to perform complex mathematical computations to speed up tasks such as object detection and facial recognition.

These chips will be optimized for specific use cases and scenarios related to computer vision, natural language processing and speech recognition.

2019 will also be the year where hyperscale infrastructure companies like Amazon, Microsoft, Google, and Facebook will increase the investments in custom chips based on field programmable gate arrays (FPGA) and application specific integrated circuits (ASIC).

Industrial IoT is the top use case for artificial intelligence that can perform outlier detection, root cause analysis and predictive maintenance of the equipment.

They will be capable of dealing with video frames, speech synthesis, time-series data and unstructured data generated by devices such as cameras, microphones, and other sensors.

3) Interoperability among neural networks becomes key One of the critical challenges in developing neural network models lies in choosing the right framework.

To address this challenge, AWS, Facebook and Microsoft have collaborated to build Open Neural Network Exchange (ONNX), which makes it possible to reuse trained neural network models across multiple frameworks.

From researchers to edge device manufacturers, all the key players of the ecosystem will rely on ONNX as the standard runtime for inferencing.

It will empower business analysts and developers to evolve machine learning models that can address complex scenarios without going through the typical process of training ML models.

When dealing with an AutoML platform, business analysts stay focused on the business problem instead of getting lost in the process and workflow.

The massive data sets obtained from the hardware, operating systems, server software and application software can be aggregated and correlated to find insights and patterns.

How AI and tech are digitising the supply chain in the trucking industry

With the advent of e-commerce harnessing new technologies, it was only a matter of time before the implementation of AI makes its way further down the entire supply chain on which it relies.

From freight shipping and sea cargo transport to warehousing and inventory management right across to last mile delivery, businesses in the logistics sectors today are will need to double down on their logistics infrastructures to keep up with the increasing expectations for fast and efficient shipping and transport.

Read here Daily operations in many sectors such as manufacturing and transport still largely depend on low-tech solutions such as physical Proofs of Delivery (PoDs) and excessive, slow correspondence via calls or emails.A recent study from PwC on Digital Operations specifically focused on manufacturers, found that on average across the entire survey 10% of manufacturing companies are ‘digital champions’ and around two-thirds have not even started their digital transformation, or have been doing so ineffectively.

Algorithms are now playing a major role in bringing the trucking industry forward, at Ontruck for example they power three important areas which are dynamic pricing calculations, routing taking into account events in real time and the assignment of loads to drivers according to load type and the driver’s profile.

Finally, the platform uses payment gateways to ensure maximum security at the time of making transactions Ensuring route optimisation is a critical factor for our drivers and deliveries as such this is an area we are focusing on the most as it guarantees the offer of loads which best match a driver’s profile to optimise the price offered to the Shipper through the route analyses of loads available in order to try offer several chained orders — minimising empty kilometres.

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