Knowledge Base Economy – Dr. Manahel Thabet

04 Aug 2019

Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

AI system ‘should be recognised as inventor’

An artificial intelligence system should be recognised as the inventor of two ideas in patents filed on its behalf, a team of academics says.

The AI has designed interlocking food containers that are easy for robots to grasp and a warning light that flashes in a rhythm that is hard to ignore.

Patents offices insist innovations are attributed to humans – to avoid legal complications that would arise if corporate inventorship were recognised.

The academics say this is “outdated”.

And it could see patent offices refusing to assign any intellectual property rights for AI-generated creations.

As a result, two professors from the University of Surrey have teamed up with the Missouri-based inventor of Dabus AI to file patents in the system’s name with the relevant authorities in the UK, Europe and US.

‘Inventive act’

Dabus was previously best known for creating surreal art thanks to the way “noise” is mixed into its neural networks to help generate unusual ideas.

Unlike some machine-learning systems, Dabus has not been trained to solve particular problems.

Instead, it seeks to devise and develop new ideas – “what is traditionally considered the mental part of the inventive act”, according to creator Stephen Thaler

The first patent describes a food container that uses fractal designs to create pits and bulges in its sides. One benefit is that several containers can be fitted together more tightly to help them be transported safely. Another is that it should be easier for robotic arms to pick them up and grip them.

The second describes a lamp designed to flicker in a rhythm mimicking patterns of neural activity that accompany the formation of ideas, making it more difficult to ignore.

Law professor Ryan Abbott told BBC News: “These days, you commonly have AIs writing books and taking pictures – but if you don’t have a traditional author, you cannot get copyright protection in the US.

“So with patents, a patent office might say, ‘If you don’t have someone who traditionally meets human-inventorship criteria, there is nothing you can get a patent on.’

“In which case, if AI is going to be how we’re inventing things in the future, the whole intellectual property system will fail to work.”

Instead, he suggested, an AI should be recognised as being the inventor and whoever the AI belonged to should be the patent’s owner, unless they sold it on.

However, Prof Abbott acknowledged lawmakers might need to get involved to settle the matter and that it could take until the mid-2020s to resolve the issue.

A spokeswoman for the European Patent Office indicated that it would be a complex matter.

“It is a global consensus that an inventor can only be a person who makes a contribution to the invention’s conception in the form of devising an idea or a plan in the mind,” she explained.

“The current state of technological development suggests that, for the foreseeable future, AI is… a tool used by a human inventor.

“Any change… [would] have implications reaching far beyond patent law, ie to authors’ rights under copyright laws, civil liability and data protection.

“The EPO is, of course, aware of discussions in interested circles and the wider public about whether AI could qualify as inventor.”

The UK’s Patents Act 1977 currently requires an inventor to be a person, but the Intellectual Property Office is aware of the issue.

“The government believes that AI technology could increase the UK’s GDP by 10% in the next decade, and the IPO is focused on responding to the challenges that come with this growth,” said a spokeswoman.

Source: https://www.bbc.com/news/technology-49191645

26 Jun 2019

Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

Here’s how AI can help fight climate change according to the field’s top thinkers

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.*

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: https://www.theverge.com/2019/6/25/18744034/ai-artificial-intelligence-ml-climate-change-fight-tackle

26 May 2019

Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

What makes someone a great leader in the digital economy?

What will great leadership look like in five years? What about in 10? Douglas Ready, a senior lecturer in organizational effectiveness at MIT Sloan and an expert on executive development, has lately been considering these questions as part of a Big Ideas research initiative with MIT Sloan Management Reviewand Cognizant. Ready has been thinking, too, about why they matter: we are becoming an ever more digital economy, and leadership must adapt.

Percentage of managers who strongly agree their leaders have the skills to transition to the digital economy.

Ready asserts that a handful of leadership characteristics will endure no matter what. Integrity comes to mind, as do courage and the ability to execute. But other contextual characteristics, as he describes them, must be responsive to the evolving world of business.

“So, whereas crafting a vision and a strategy is an enduring leadership characteristic, doing so in a transparent, inclusive, and collaborative manner is a contextual characteristic, given the expectations of the new workforce,” Ready writes in a recent article in MIT Sloan Management Review. “Great leaders will need to more artfully merge the ‘what’ with the ‘how’ to thrive in tomorrow’s world.”

“Leading Into the Future” is the first in a yearlong exploration of the future of leadership in the digital economy. The research team is tackling a broad range of subjects related to this issue through a global survey and in-depth executive interviews with those most heavily involved in digital transformation. Below are three insights offered from the series so far.

Mind the mindset gap

In partnership with MIT Sloan Management Reviewand Cognizant, Ready surveyed more than 4,000 managers and leaders from over 120 countries on their preparedness for the transition to a digital economy. Only 12% of respondents strongly agreed that their organizations’ leaders had the right mindset and 9% strongly agreed that their leaders had the proper skills to lead in the digital economy.

To Ready, this “mindset gap” is more concerning than the skills deficit. “We can train for the digital skills that are important for future success,” he writes. “But developing a digital mindset is a more complex challenge because it is a less tangible one to address.” And as long as the mindset gap exists, so do critical blind spots about how the digital economy is eroding old ways of doing business.

13 May 2019

Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

A new AI acquired humanlike ‘number sense’ on its own

Artificial intelligence can share our natural ability to make numeric snap judgments.

Researchers observed this knack for numbers in a computer model composed of virtual brain cells, or neurons, called an artificial neural network. After being trained merely to identify objects in images — a common task for AI — the network developed virtual neurons that respond to specific quantities. These artificial neurons are reminiscent of the “number neurons” thought to give humans, birds, bees and other creatures the innate ability to estimate the number of items in a set (SN: 7/7/18, p. 7). This intuition is known as number sense.

In number-judging tasks, the AI demonstrated a number sense similar to humans and animals, researchers report online May 8 in Science Advances. This finding lends insight into what AI can learn without explicit instruction, and may prove interesting for scientists studying how number sensitivity arises in animals.

Neurobiologist Andreas Nieder of the University of Tübingen in Germany and colleagues used a library of about 1.2 million labeled images to teach an artificial neural network to recognize objects such as animals and vehicles in pictures. The researchers then presented the AI with dot patterns containing one to 30 dots and recorded how various virtual neurons responded.

Some neurons were more active when viewing patterns with specific numbers of dots. For instance, some neurons activated strongly when shown two dots but not 20, and vice versa. The degree to which these neurons preferred certain numbers was nearly identical to previous data from the neurons of monkeys.

Dot detectors

A new artificial intelligence program viewed images of dots previously shown to monkeys, including images with one dot and images with even numbers of dots from 2 to 30 (bottom). Much like the number-sensitive neurons in monkey brains, number-sensitive virtual neurons in the AI preferentially activated when shown specific numbers of dots. As in monkey brains, the AI contained more neurons tuned to smaller numbers than larger numbers (top).

To test whether the AI’s number neurons equipped it with an animal-like number sense, Nieder’s team presented pairs of dot patterns and asked whether the patterns contained the same number of dots. The AI was correct 81 percent of the time, performing about as well as humans and monkeys do on similar matching tasks. Like humans and other animals, the AI struggled to differentiate between patterns that had very similar numbers of dots, and between patterns that had many dots (SN: 12/10/16, p. 22).

This finding is a “very nice demonstration” of how AI can pick up multiple skills while training for a specific task, says Elias Issa, a neuroscientist at Columbia University not involved in the work. But exactly how and why number sense arose within this artificial neural network is still unclear, he says.

Nieder and colleagues argue that the emergence of number sense in AI might help biologists understand how human babies and wild animals get a number sense without being taught to count. Perhaps basic number sensitivity “is wired into the architecture of our visual system,” Nieder says.

04 May 2019

Digital Economy, Disruptive Technology, Knowledge Base Economy Author: Manahel Thabet 0 comment

Turbocharging India’s Digital Economy

New digital ecosystems are springing up across India’s economy, transforming business models and delivering huge productivity, efficiency, and growth benefits. And sectors that have not traditionally had technology at their core – such as agriculture, banking, health care, and logistics – are among those with the most potential.

MUMBAI – India is taking a great digital leap. Having reaped substantial rewards from building up its core digital sectors, such as information technology and business process management, the country is now seizing new digital opportunities in many more sectors, such as agriculture, education, energy, financial services, health care, and logistics. These opportunities could deliver up to $500 billion of economic value by 2025.

India’s digitization process has been the second-fastest among the 17 mature and emerging economies we studied. Admittedly, it started from a low base, but in the last five years alone, the number of Internet subscribers has almost doubled, reaching 560 million.

Last year, Indians downloaded 12.3 billion apps, second only to the Chinese, and they spent an average 17 hours per week on social media, more than Americans. As a result, Indians used more than 54 times as much data, on average, in 2018 than in mid-2016.

Both the public and private sectors have played an important role in driving digitization. Many public services are now accessible only when linked to the government’s Aadhaarbiometric digital-identification program, in which over 1.2 billion people are now enrolled. Aadhaar has thus helped to propel the development of many other digital services. About 80% of Indians now have digital bank accounts, with the vast majority of government benefits paid directly into Aadhaar-linked accounts. The Goods and Services Tax Network – a government platform for taxing wholesale and retail sales – has likewise created a powerful incentive for businesses to digitize their operations.

The private sector has facilitated this process, as competition has helped to reduce data costs by 95% from 2013 to 2017 and to make smartphones affordable. Falling costs have fueled rising data use: last year, Indian data subscribers used 8.3 GB of data per month, on average, compared to 5.5 GB used by Chinese subscribers. Together with rapid growth in telecom infrastructure, lower costs have also helped to reduce the digital divide: in the last four and a half years, India’s middle- and low-income states have accounted for 45% of the 293 million new Internet subscribers.

Digital business leaders are now spearheading even more innovative ways to reach and serve customers. New digital ecosystems are springing up across the economy, transforming business models and delivering huge productivity, efficiency, and growth benefits.

25 Apr 2019

Digital Economy, Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

Elon Musk: Brain-Computer Interface Update “Coming Soon”

Neuralink

SpaceX and Tesla CEO Elon Musk hinted at what could be the announcement of a brain-machine interface that could one day hook human brains up to computers on Sunday. In response to a question asking for an update on Neuralink, a neurotechnology startup he founded in 2016, Musk replied that new information would be “coming soon”.

A “direct cortical interface,” according to Musk, could allow humans to reach higher levels of cognition —and give humans a better shot at competing with artificial intelligence, the Wall Street Journal reported in 2017. It’s unclear, though, whether Neuralink’s main objective is to do just that or to connect human brains to computers for consumer applications.

AI Overlords

Musk has repeatedly warned of evil AI overlords in the past, saying that AI could become “an immortal dictator from which we could never escape” in a 2018 documentary called “Do You Trust This Computer?”

Most of what Neuralink is working on, including any plans for a brain computer interface, are still tightly under wraps. In one tantalizing clue, Bloomberg recently reported on a still unpublished academic paper by five authors who have been employed by or associated with Neuralink — though it’s unclear whether Musk’s tweet referred to their work.

Sewing Machine For The Brain

The paper describes a “sewing machine” for the brain in the form of a needle-like device that is inserted into a rat’s skull to implant a bendable polymer electrode in the brain that would read the brain’s electrical signals.

Of course, human trials are still a long time out. Neuralink has yet to comment on any possible timelines or announcements.

18 Apr 2019

Digital Economy, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

AI could change the way humans interact

In the future, artificial intelligence will probably reshape our economy, society and our lives. But achieving AI’s full potential will not only require many technological innovations but research into some societal difficulties, including ethical issues, workplace disruptions, and changing human interactions.

Sci-fi movies portray artificial intelligence as self-aware computers, evil robots, and digital armageddon. Some fear that in the future, almighty superintelligent AI will far surpass human intelligence, posing an existential threat to humanity. But, the real threat to humanity, said Prof. Christakis, is that “for better and for worse, robots will alter humans’ capacity for altruism, love, and friendship.”

Major innovations have long had an impact on the ways that people interact with each other and the printing press, telephone, radio, TV, and the internet are such technologies.

“As consequential as these innovations were, however, they did not change the fundamental aspects of human behavior that comprise what I call the social suite: a crucial set of capacities we have evolved over hundreds of thousands of years, including love, friendship, cooperation, and teaching…” he wrote.

“But adding AI to our midst could be much more disruptive. Especially as machines are made to look and act like us and to insinuate themselves deeply into our lives, they may change how loving or friendly or kind we are –not just in our direct interactions with the machines in question, but in our interactions with one another.”

Artificial intelligence can both improvise how humans relate to one another as well as make them behave less ethically.

Experiments with hybrid groups of people and robots working together have shown that the right kind of AI can help improve the group’s overall performance. But, in other experiments, he found that by adding a few bots posing as selfish humans, the same groups that previously behaved in an unselfish, generous way toward each other were now driven by the bots to behave in a selfish way.

This shouldn’t be surprising, as over the last few years we’ve seen how the spread of false information by malicious bots over social media can have a highly negative, polarizing impact on large groups of people.

“As AI permeates our lives, we must confront the possibility that it will stunt our emotions and inhibit deep human connections, leaving our relationships with one another less reciprocal, or shallower, or more narcissistic,” he writes.

We will be needing rules and policies overlooking to help us deal with potentially negative impacts of AI on society, not unlike how we’ve stopped corporations from polluting our water supply or individuals from spreading harmful cigarette smoke.

“In the not-distant future, AI-endowed machines may, by virtue of either programming or independent learning (a capacity we will have given them), come to exhibit forms of intelligence and behavior that seem strange compared with our own,” concludes Prof. Christakis. “We will need to quickly differentiate the behaviors that are merely bizarre from the ones that truly threaten us.”

Source: https://cio.economictimes.indiatimes.com/news/strategy-and-management/ai-could-change-the-way-humans-interact/68923262

16 Apr 2019

Digital Economy, Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

How AI is Revolutionizing Global Logistics and Supply Chain Management

Artificial intelligence is taking up the pace when it comes to global logistics and supply chain management. As per a number of executives from the transportation industry, these fields are expected to go through a more significant transformation. The on-going evolution in the areas of technologies like artificial intelligence, machine learning, and similar new technologies is said to possess the potential to bring in disruption and lead innovation within these industries.

Artificial intelligence comes with computing techniques which helps to select large quantities of data that is collected from logistics and supply chain. You can put such methods to use, and they can be analyzed to get results which can initiate processes and complex functions.

Many organizations have now been benefitted with investments in artificial intelligence. As per Adobe, currently, 15% have already started to use AI while other 31% plans to have them implemented in 2019. Some of the areas from which revenue can be generated are research and development, product innovation, supply chain operations, and customer service.

Impact of artificial intelligence in Logistics

Predictive capabilities will rise.

The efficiencies of the company in the areas of network planning and predictive demand are getting improved with AI capabilities. Companies get to become more proactive by having a tool which can help with capacity planning and accurate demand forecasting. When they know what the market expects, they can quickly move the vehicles to the areas with more demand and thereby bring down the operational costs.

To avoid risks, anticipate events and come up with solutions, now techs are using data. The data helps companies to use their resources in the right way for maximum benefits, and artificial intelligence helps them with it more accurate and faster manner.

Robotics.

You cannot talk about artificial intelligence without mentioning robotics. Even though robotics is considered as a futuristic technology concept, the supply chain already makes use of it. They are used to track, locate and move inventory within the warehouses. Such robots come with deep learning algorithms which helps the robots make autonomous decisions regarding the different processes that are performed in the warehouse.

Big data.

Apart from robots, artificial intelligence is also about big data. For the logistics companies, Big Data helps to optimize future performance and forecast accurate outlooks better than ever. When the insights of Big Data are used along with artificial intelligence, it helps to improve different areas of supply chain like supply chain transparency and route optimization.

For AI in the logistics industry, coming up with clean data is a huge step, and they cannot implement without having such usable figures. It is not easy to measure efficiency as data comes from different sources. At the source level it is not possible to improve such data, and so algorithms are used to analyze data, enhance the quality of data, identify issues to attain transparency which can be used for business benefits.

Computer vision.

When you are moving cargo across the world, it is always good to have a pair of eyes to monitor, and it can be best when it comes with state-of-the-art technology. Now you can see things in a new way by using computer vision which is based on artificial intelligence for the logistics.

Autonomous vehicles.

Autonomous vehicles are the next big thing that artificial intelligence offers the supply chain. Having driverless trucks can take a while, but the logistics industry is now making use of high-tech driving to increase efficiency and safety. The significant change is expected in this industry in terms of assisted braking, lane-assist, and highway autopilot.

In order to achieve lower fuel consumption, better-driving systems are coming up which works on to bring together multiple trucks to have formations. Computers control such formations and they are connected with one another too. Such kind of configuration is said to help the trucks save fuel distinctively.

Impact of artificial intelligence (AI) in Supply Chain.

AI offers contextual intelligence.

AI provides the supply chain with contextual intelligence which can be used by them to reduce the operating costs and manage inventory. The contextual information helps them to get back to the clients quickly.

Companies make use of AI along with machine learning to get new insights into different areas which include warehouse management, logistics and supply chain management. Some of the technologies used in these areas are AI-powered Visual Inspection to identify damage and carry out needed correction by taking photos of the cargo by using special cameras and Intelligent Robotic Sorting to sort palletized shipments, parcels and letters.

18 Mar 2019

Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

Overcoming the challenges of digital transformation

Digital transformation is one of the steps on the way to a business being able to adopt artificial intelligence (AI), blockchain, augmented reality (AR) and other advanced technologies that provide it with a competitive advantage. It is also seen as necessary for survival.

According to two recent reports from Gartner, there are indications that digital transformation is a priority in 2019:

“Growth is the number one priority for CEOs, and many see digital business as the primary means of achieving its growth objectives. Only 33% of enterprises have managed to reach the scaling stage of digital business.” Getting to the Details of the Digital Platform: A Gartner Theme Insight Report, Bill Swanton, December 7, 2018

“Only 4% of organisations have no digital initiative at all, which signals a shift from digital as an option to digital as a mainstream platform.” Smarter with Gartner, CIO Agenda 2019: Digital Maturity Reaches a Tipping Point, October 16, 2018

Digital transformation is forcing organisations to look at IT from more perspectives than ever before, as an effective strategy spans many different, previously disconnected, technology disciplines and investments.

Leading organisations today are looking for complete sets of solutions to achieve greater speed and agility, supported by a high level of protection and an advanced analytics ecosystem, to balance managing the top and bottom lines without the added risk of starting from scratch.

Helping enterprises succeed with their digital transformation is a key opportunity for channel players.

Why digital transformation is hard

According to Harvard Business Review writers, Thomas H. Davenport and George Westerman: “Digital transformation is an ongoing process of changing the way you do business. It requires foundational investments in skills, projects, infrastructure, and, often, in cleaning up IT systems. It requires mixing people, machines, and business processes, with all of the messiness that entails.”

In other words, a digital transformation project is a massive, complex project one that affects the whole of an organisation’s business, and needs to be approached with precision and a clear change management plan.

An enterprise will first look to its technology partners to understand the business outcomes of the planned transformation project, and then work closely with them to develop a solution that meets the technology needs in a holistic manner. Often a transformation project requires resources to be pulled together from many different sources. Only a channel partner is equipped to architect such a complete solution and needs to take their customer on a linear and carefully structured journey.

The components of a successful transformation

The more that an organisation can source its transformation technology from a single vendor, the simpler the overall process will be, as components will already be operating harmoniously with one another.

By combining industry-leading software and domain expertise in four core areas Micro Focus is now uniquely positioned to deliver on all critical aspects of enterprise digital transformation to help customers innovate faster with less risk.

Micro Focus has built solutions that target the four areas most critical to lasting digital transformation success:

1. Enterprise DevOps — Build and deliver better software faster
Winning the race to innovate requires a high-speed approach. Micro Focus solutions unleash the power of DevOps across the hybrid IT landscape, quickly bringing innovative ideas to life at the pace of business to securely deliver high quality software and services faster.

2. Hybrid IT management — Run and transform
IT Infrastructure, services and even purchasing models are rapidly evolving. Maximising business value and accelerating outcomes compels organisations to find new ways to extend existing investments and take advantage of new platforms – from containers to public clouds to Internet of Things (IoT). Micro Focus solutions help organisations run IT Ops at the speed of DevOps, delivering services on demand and generating operational and business insights, all while helping organisations address security, compliance and governance requirements.

3. Predictive analytics — Analyse in time to act
Data lakes are valuable only if the business can surface the insights hidden within its depths. Micro Focus helps leverage machine learning to transform unlimited volumes of data into accurate, actionable, automated insights at the speed of the business. Be in a position to make predictions and influence business outcomes quickly and efficiently with comprehensive and relevant real-time intelligence.

4. Security, risk, and governance — Secure what matters most
Cyber threats are escalating. Ageing apps and processes (along with new ones) are full of unforeseen risks. Privacy and compliance requirements are mounting. Micro Focus provides the industry’s broadest set of integrated security, risk, and governance solutions, with an analytics-driven approach to securing what matters most – identities, applications and data.

For the channel, the opportunity lies in taking customers on the transformation journey, providing consultancy and structuring hardware infrastructure to best leverage the software. Successful digital transformation needs the holistic oversight and cross-vendor capabilities of channel organisations. It’s for this reason that Micro Focus hired Lachlan Downing as NZ country manager – his specific remit is to support the local channel facilitating Micro Focus support and resources throughout the sales cycle.

In February this year, Micro Focus also announced the launch of its new, unified partner program, which includes access to all products across the Micro Focus portfolio on a global scale. In conjunction with the new program, a new partner portal was introduced that streamlines and simplifies the ways partners engage with Micro Focus and customers.

Lachlan Downing said, “This year Micro Focus New Zealand has transitioned to a channel-centric model and we have also fine-tuned our partner program, which will streamline access to marketing development funds (MDF) for Gold and Platinum partners and will help accelerate turnaround time for deal registration, quotes and orders as well as build skills and capability through full access to our training and certification content.

“The announcement of the new Micro Focus partner program and portal demonstrates our continued commitment to providing an easier path for our partners to confidently generate predictable revenue, build pipeline and do business with us.”

Todd Parsons, Channel Director – Australia and New Zealand, Micro Focus, said, “The new partner program and portal brings together the full Micro Focus product portfolio and allows full access from one system and program. Leading up to this we were still working with multiple partner programs and systems as we completed the acquisition of HP’s software business. The new program will allow greater efficiencies but also ensure our customers and partners can more effectively utilise our portfolio of solutions and products.”

Source: https://www.reseller.co.nz/brand-post/content/658810/overcoming-the-challenges-of-digital-transformation/

09 Mar 2019

Disruptive Technology, Innovation, Knowledge Base Economy Author: Manahel Thabet 0 comment

How AI And Machine Learning Helps In Up Skilling To Better Career Opportunities

The AI market is expected to grow from $21.46 Bn to $190.61 Bn between 2018 and 2025

AI will create nearly 2.3 million jobs by next year

Mathematical and programming skills are central to acquiring competency in this field

There are at least two clear trends that show a demand-supply mismatch in tech jobs in cutting-edge IT fields such as Artificial Intelligence and Machine Learning. One is via industry predictions that estimate growth in the AI market from $21.46 Bn to $190.61 Bn between 2018 and 2025.

Year on year growth is projected to be an impressive 36.62% during the same period. The second trend is more subtle. Big Indian IT firms in the US are reportedly ‘hoarding’ employees in these two fields as they foresee a shortage of skilled experts. They also fear a corresponding rise in the cost of hiring employees for tech contracts they have bagged for the future.

How Are AI & Machine Learning Being Used In Industry?

Unlike the exaggerated robots of the 2001 Steven Spielberg movie of the same name, Artificial Intelligence (AI) in reality is tamer. AI is understood to mean ways of making computers, computer-controlled robots or program think intelligently mimicking the manner in which humans think intelligently.

A computer program with AI can use can solve generic problems it is programmed to instead of just specific ones. They can accommodate new modifications to input without breaking structure. Traditional programmers would have to sort, sift and debug thousands of lines of code to make modifications.

AI finds applications in strategy games such as chess or poker where advance moves are determined by heuristic logic, natural language processing, virtual assistant technology, image and speech recognition and automated robotics.

General AI systems which can solve any given problem are rare. Insurance and banking organizations regularly use AI to monitor fraud. Marketers use AI every time you shop online to gather your browsing habits and predict what you are most likely to buy. They will then advertise those products through pop-ups and logos. Self-driving cars, auto-pilot modes and smart homes using sensors all rely on AI and affect daily lives of consumers.

There is also a difference between AI and Machine Learning (ML) although a number of articles on the web club them together or use them interchangeably. “ML is the study of computer algorithms that improve automatically through experience” according to Tom Mitchell of Carnegie Mellon University. It is simply one of the ways we use to achieve AI or something closer.

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