Companies are tapping artificial intelligence to automate the care of their operations and information-technology infrastructure, finding that AI can identify and fix problems more quickly than humans.

Called “self-healing” or “self-driving” IT, the innovation has been made possible by advanced algorithms, more data about networks and IT infrastructure, and increased computing power.

Software company Adobe Inc. uses an AI-based program to automate about 25 core IT tasks that were previously done by employees.

The company spent about nine months developing the program using open-source technology, said Chief Information Officer Cynthia Stoddard. It has been in use for about a year.

“We wanted to look at issues that we could automate and get the human element out,” Ms. Stoddard said.

One thing the new system does: It automatically fixes failures in data-batching. The method is an efficient way to process and transfer large amounts of data, but it often results in errors. Adobe’s self-healing software was able to reduce the average time to correct a data-batching failure to about three minutes from 30 minutes, Ms. Stoddard said.

The software can also detect whether a specific business application an employee is using is close to crashing and automatically increase the computing or storage capacity so the application continues to run. “It not only shortens the time to fix [a problem] but proactively fixes it,” Ms. Stoddard said.

Within three to five years, AI-enabled networks are predicted to become mainstream at big companies, said Rohit Mehra, vice president of network infrastructure at research firm International Data Corp.

AI will be integral to company networks in the future, Mr. Mehra said, as they continue to expand with the influx of the Internet of Things, virtual reality, and fifth-generation wireless technology.

Networks are critical to companies because they make it possible for employees to access critical applications and exchange information in real time. But they can break down as they get bigger because of increased traffic related to emails, file transfers, videos and business applications.

About half of IT staff surveyed recently said the most important thing an AI-enabled network would bring is improving the availability and performance of applications, which would help improve user experience. The IDC survey released in February polled 301 IT professionals from medium and large businesses.

International Business Machines Corp. sells IT automation tools to customers under its AI OpenScale suite of software products. The company is now researching AI algorithms that can proactively monitor networks, predict a network failure or performance issue, and fix it automatically.

This will be especially important for “mission critical” applications, which could be at risk of going down for as many as four hours until IT staff can fix the issue, said Ruchir Puri, chief scientist at IBM Research.

“The goal is to be proactive so that systems monitor themselves and correct themselves, and with AI, it is really becoming possible,” he said.

Write to Sara Castellanos at This email address is being protected from spambots. You need JavaScript enabled to view it.

Source: The Wall Street Journal

The AI renaissance of recent years has led many to ask how this technology can help with one of the greatest threats facing humanity: climate change. A new research paper authored by some of the field’s best-known thinkers aims to answer this question, giving a number of examples of how machine learning could help prevent human destruction.

The suggested use-cases are varied, ranging from using AI and satellite imagery to better monitor deforestation, to developing new materials that can replace steel and cement (the production of which accounts for nine percent of global green house gas emissions).

But despite this variety, the paper (which we spotted via MIT Technology Review) returns time and time again to a few broad areas of deployment. Prominent among these are using machine vision to monitor the environment; using data analysis to find inefficiencies in emission-heavy industries; and using AI to model complex systems, like Earth’s own climate, so we can better prepare for future changes.

The authors of the paper — which include DeepMind CEO Demis Hassabis, Turing award winner Yoshua Bengio, and Google Brain co-founder Andrew Ng — say that AI could be “invaluable” in mitigating and preventing the worse effects of climate change, but note that it is not a “silver bullet” and that political action is desperately needed, too.

“Technology alone is not enough,” write the paper’s authors, who were led by David Rolnick, a postdoctoral fellow at the University of Pennsylvania. “[T]echnologies that would reduce climate change have been available for years, but have largely not been adopted at scale by society. While we hope that ML will be useful in reducing the costs associated with climate action, humanity also must decide to act.”

In total, the paper suggests 13 fields where machine learning could be deployed (from which we’ve selected eight examples), which are categorized by the time-frame of their potential impact, and whether or not the technology involved is developed enough to reap certain rewards. You can read the full paper for yourself here, or browse our list below.

  • Build better electricity systems. Electricity systems are “awash with data” but too little is being done to take advantage of this information. Machine learning could help by forecasting electricity generation and demand, allowing suppliers to better integrate renewable resources into national grids and reduce waste. Google’s UK lab DeepMind has demonstrated this sort of work already, using AI to predict the energy output of wind farms.
  • Monitor agricultural emissions and deforestation. Greenhouse gases aren’t just emitted by engines and power plants — a great deal comes from the destruction of trees, peatland, and other plant life which has captured carbon through the process of photosynthesis over millions of years. Deforestation and unsustainable agriculture leads to this carbon being released back into the atmosphere, but using satellite imagery and AI, we can pinpoint where this is happening and protect these natural carbon sinks.
  • Create new low-carbon materials. The paper’s authors note that nine percent of all global emissions of greenhouse gases come from the production of concrete and steel. Machine learning could help reduce this figure by helping to develop low-carbon alternatives to these materials. AI helps scientists discover new materials by allowing them to model the properties and interactions of never-before-seen chemical compounds.
  • Predict extreme weather events. Many of the biggest effects of climate change in the coming decades will be driven by hugely complex systems, like changes in cloud cover and ice sheet dynamics. These are exactly the sort of problems AI is great at digging into. Modeling these changes will help scientists predict extreme weather events, like droughts and hurricanes, which in turn will help governments protect against their worst effects.
Rising Temperatures And Drought Conditions Intensify Water Shortage For Navajo Nation
Better climate models would help governments mitigate the worse effects of droughts and other extreme weather events. 
Photo by Spencer Platt/Getty Images
  • Make transportation more efficient. The transportation sector accounts for a quarter of global energy-related CO2 emissions, with two-thirds of this generated by road users. As with electricity systems, machine learning could make this sector more efficient, reducing the number of wasted journeys, increasing vehicle efficiency, and shifting freight to low-carbon options like rail. AI could also reduce car usage through the deployment of shared, autonomous vehicles, but the authors note that this technology is still not proven.
  • Reduce wasted energy from buildings. Energy consumed in buildings accounts for another quarter of global energy-related CO2 emissions, and presents some of “the lowest-hanging fruit” for climate action. Buildings are long-lasting and are rarely retrofitted with new technology. Adding just a few smart sensors to monitor air temperature, water temperature, and energy use, can reduce energy usage by 20 percent in a single building, and large-scale projects monitoring whole cities could have an even greater impact.
  • Geoengineer a more reflective Earth. This use-case is probably the most extreme and speculative of all those mentioned, but it’s one some scientists are hopeful about. If we can find ways to make clouds more reflective or create artificial clouds using aerosols, we could reflect more of the Sun’s heat back into space. That’s a big if though, and modeling the potential side-effects of any schemes is hugely important. AI could help with this, but the paper’s authors note there would still be significant “governance challenges” ahead.
  • Give individuals tools to reduce their carbon footprint. According to the paper’s authors, it’s a “common misconception that individuals cannot take meaningful action on climate change.” But people do need to know how they can help. Machine learning could help by calculating an individual’s carbon footprint and flagging small changes they could make to reduce it — like using public transport more; buying meat less often; or reducing electricity use in their house. Adding up individual actions can create a big cumulative effect.

Source: The verge,  the autonomous vehicle tech startup once valued at $200 million, has been acquired by Apple  as it prepared to shut down after four years, according to a state regulatory filing.

The closure was first reported by the San Francisco Chronicle. The company is not responding to media inquiries, a PR rep told TechCrunch.

The company’s Mountain View headquarters will close down on Friday, according to WARN documents filed with the Employment Development Department of California. A company must file a WARN document ahead of a mass layoff or plant closure.

Rumors have been swirling for weeks that Apple was looking to snap up the startup. Earlier this month, The Information reported that Apple was pursuing an acqui-hire, a term that typically means a smaller, targeted acquisition aimed at bringing on specific talent. Apple has confirmed that it acquired the company, but it’s unclear if this included assets like IP and if all employees are making the move.

It appears to have panned out for at least some of the company’s 90 employees. At least five employees changed their LinkedIn  profiles to show they started employment at Apple’s special projects division this month, according to SF Chronicle and confirmed by TechCrunch’s own review. was founded in 2015 by former graduate students working in Stanford University’s  Artificial Intelligence Lab run by Andrew Ng, the renowned artificial intelligence expert. Ng is on the board of’s board and is married to co-founder Carol Reiley.

The company, which originally focused on self-driving software systems and intelligent communications systems, received a lot of attention and investment in those first years. It later raised more money as it tweaked its business model with a plan to combine deep learning software with hardware to make self-driving retrofitted kits designed for business and commercial fleets. In all, the company has raised about $77 million, according to Pitchbook data. It was last valued at $200 million in 2017.

The startup ramped up operations in 2017 and 2018. Last year it launched a pilot program in Frisco, Texas to test an on-demand service using self-drivings. But even as it expanded, the executive team appeared to be constantly in flux with several people holding the CEO spot.

Correction: Andrew Ng served on the board of He was not chairman.

Source: Techcrunch

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Avoid biases and inaccuracies in your artificial intelligence-based business decisions with these tips from KPMG.

As more organizations adopt artificial intelligence (AI) and machine learning into daily workflows, they must consider how to govern these algorithms to avoid inaccuracies and bias, according to KPMG's Controlling AI report, released last week. 

Organizations that build and deploy AI technologies are using various tools to gain insights and make decisions that exceed human capabilities, the report noted. While this is a large opportunity for businesses, the algorithms used can be destructive if they produce results that are biased or incorrect. For this reason, many company leaders remain hesitant to allow machines to make important decisions without understanding how and why those decisions were made, and if they are fair and accurate, according to KPMG. 

SEE: Artificial intelligence: A business leader's guide (free PDF)(TechRepublic)

To make AI a useful and accurate tool, KPMG developed the AI in Control framework, to help organizations drive greater confidence and transparency through tested AI governance constructs. The framework addresses the risks involved in using AI, and includes recommendations and best practices for establishing AI governance, performing AI assessments, and integrating continuous AI monitoring, the report noted. 


"Transparency from a solid framework of methods and tools is the fuel for trusted AI—and it creates an environment that fosters innovation and flexibility," the report stated. 

Here are six tips for improving AI governance in your organization, according to KPMG:  

  1. Develop AI design criteria and establish controls in an environment that fosters innovation and flexibility.
  2. Assess current governance framework and perform gap analysis to identify opportunities and areas that need to be updated.
  3. Integrate a risk management framework to identify and prioritize business-critical algorithms and incorporate an agile risk mitigation strategy to address cybersecurity, integrity, fairness, and resiliency considerations during design and operation.
  4. Design and implement an end-to-end AI governance and an operating model across the entire lifecycle: strategy, building, training, evaluating, deploying, operating, and monitoring AI.
  5. Design a governance framework that delivers AI solutions and innovation through guidelines, templates, tooling, and accelerators to quickly, yet responsibly, deliver AI solutions.
  6. Design and set up criteria to maintain continuous control over algorithms without stifling innovation and flexibility.

"The power and potential of AI will fully emerge only when the results of algorithms
become understandable in clear, straightforward language," the report stated. "Companies that
don't prioritize AI governance and the control of algorithms will likely jeopardize their overall AI strategy, putting their initiatives and potentially their brand at risk."


Artificial Intelligence. Everybody wants it. Everybody knows they need to invest in pilots and initial projects. Yet getting those projects into production is hard, and most companies still aren't in with both feet.

If you aren't hands on with the projects yourself, you may have heard a lot of different terminology. You may be wondering what it all means. Is AI the same as machine learning? Is machine learning the same as deep learning? Do you need them all? Sometimes the first steps of understanding whether a technology is a fit for your organization's challenges and problems is understanding the basic terminology behind that technology.

Let's start with a basic definition of artificial intelligence. The term means a lot of things to a lot of different people, from robots coming to take your jobs to the digital assistants in your mobile phone and home -- Alexa, Siri, and the rest. But those who work with AI know that it is actually a term that encompasses a collection of technologies that include machine learning, natural language processing, computer vision, and more.

Artificial intelligence can also be divided into narrow AI and general AI. Narrow AI is the kind we run into today -- AI suited for a narrow task. This could include recommendation engines, navigation apps, or chatbots. These are AIs designed for specific tasks. Artificial general intelligence is about a machine performing any task that a human can perform, and this technology is still really aspirational.

With AI hype everywhere today, it's time to break down some of the more common terms and technologies that make up AI, and a few of the bigger tools that make it easier to do AI. Take a look through the terms and technologies you need to know -- some components that make up AI, and a few of the tools to make them work.



Image: besjunior -
Image: besjunior -


Jessica Davis has spent a career covering the intersection of business and technology at titles including IDG's Infoworld, Ziff Davis Enterprise's eWeek and Channel Insider, and Penton Technology's MSPmentor. She's passionate about the practical use of business intelligence, ... View Full Bio

 Source:Information week

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