Mediaboss Marketing

Leadership team of credit-as-a-service startup Migo, one of a growing number of businesses using AI to create consumer-facing products.

The ability to make good decisions is literally the reason people trust you with responsibilities. Whether you work for a government or lead a team at a private company, your decision-making process will affect lives in very real ways.

Organisations often make poor decisions because they fail to learn from the past. Wherever a data-collection reluctance exists, there is a fair chance that mistakes will be repeated. Bad policy goals will often be a consequence of faulty evidentiary support, a failure to sufficiently look ahead by not sufficiently looking back.

But as Daniel Kahneman, author of Thinking Fast and Slow, says:

The idea that the future is unpredictable is undermined every day by the ease with which the past is explained. If governments and business leaders will live up to their responsibilities, enthusiastically embracing methodical decision-making tools should be a no-brainer.

Mass media representations project artificial intelligence in futuristic, geeky terms. But nothing could be further from the truth.

While it is indeed scientific, AI can be applied in practical everyday life today. Basic interactions with AI include algorithms that recommend articles to you, friend suggestions on social media and smart voice assistants like Alexa and Siri.

In the same way, government agencies can integrate AI into regular processes necessary for society to function properly.

Managing money is an easy example to begin with. AI systems can be used to streamline data points required during budget preparations and other fiscal processes. Based on data collected from previous fiscal cycles, government agencies could reasonably forecast needs and expectations for future years.

With its large trove of citizen data, governments could employ AI to effectively reduce inequalities in outcomes and opportunities. Big Data gives a birds-eye view of the population, providing adequate tools for equitably distributing essential infrastructure.

Perhaps a more futuristic example is in drafting legislation. Though a young discipline, legimatics includes the use of artificial intelligence in legal and legislative problem-solving.

Democracies like Nigeria consider public input a crucial aspect of desirable law-making. While AI cannot yet be relied on to draft legislation without human involvement, an AI-based approach can produce tools for specific parts of legislative drafting or decision support systems for the application of legislation.

In Africa, businesses are already ahead of most governments in AI adoption. Credit scoring based on customer data has become popular in the digital lending space.

However, there is more for businesses to explore with the predictive powers of AI. A particularly exciting prospect is the potential for new discoveries based on unstructured data.

Machine learning could broadly be split into two sections: supervised and unsupervised learning. With supervised learning, a data analyst sets goals based on the labels and known classifications of the dataset. The resulting insights are useful but do not produce the sort of new knowledge that comes from unsupervised learning processes.

In essence, AI can be a medium for market-creating innovations based on previously unknown insight buried in massive caches of data.

Digital lending became a market opportunity in Africa thanks to growing smartphone availability. However, customer data had to be available too for algorithms to do their magic.

This is why it is desirable for more data-sharing systems to be normalised on the continent to generate new consumer products. Fintech sandboxes that bring the public and private sectors together aiming to achieve open data standards should therefore be encouraged.

Artificial intelligence, like other technologies, is neutral. It can be used for social good but also can be diverted for malicious purposes. For both governments and businesses, there must be circumspection and a commitment to use AI responsibly.

China is a cautionary tale. The Communist state currently employs an all-watching system of cameras to enforce round-the-clock citizen surveillance.

By algorithmically rating citizens on a so-called social credit score, Chinas ultra-invasive AI effectively precludes individual freedom, compelling her 1.3 billion people to live strictly by the Politburos ideas of ideal citizenship.

On the other hand, businesses must be ethical in providing transparency to customers about how data is harvested to create products. At the core of all exchange must be trust, and a verifiable, measurable commitment to do no harm.

Doing otherwise condemns modern society to those dystopian days everybody dreads.

How can businesses and governments use Artificial Intelligence to find solutions to challenges facing the continent? Join entrepreneurs, innovators, investors and policymakers in Africas AI community at TechCabals emerging tech townhall. At the event, stakeholders including telcos and financial institutions will examine how businesses, individuals and countries across the continent can maximize the benefits of emerging technologies, specifically AI and Blockchain. Learn more about the event and get tickets here.

Originally posted here:
How businesses and governments should embrace AI and Machine Learning - TechCabal

Cisco on Friday announced advancements to its IoT portfolio that enable service provider partners to offer optimized management of cellular IoT environments and new 5G use-cases.

Cisco IoT Control Center(formerly Jasper Control Center) is introducing new innovations to improve management and reduce deployment complexity. These include:

Using Machine Learning (ML) to improve management: With visibility into 3 billion events every day, Cisco IoT Control Center uses the industry's broadest visibility to enable machine learning models to quickly identify anomalies and address issues before they impact a customer. Service providers can also identify and alert customers of errant devices, allowing for greater endpoint security and control.

Smart billing to optimize rate plans:Service providers can improve customer satisfaction by enabling Smart billing to automatically optimize rate plans. Policies can also be created to proactively send customer notifications should usage changes or rate plans need to be updated to help save enterprises money.

Support for global supply chains: SIM portability is an enterprise requirement to support complex supply chains spanning multiple service providers and geographies. It is time-consuming and requires integrations between many different service providers and vendors, driving up costs for both. Cisco IoT Control Center now provides eSIM as a service, enabling a true turnkey SIM portability solution to deliver fast, reliable, cost-effective SIM handoffs between service providers.

Cisco IoT Control Center has taken steps towards 5G readiness to incubate and promote high value 5G business use cases that customers can easily adopt.

Vikas Butaney, VP Product Management IoT, CiscoCellular IoT deployments are accelerating across connected cars, utilities and transportation industries and with 5G and Wi-Fi 6 on the horizon IoT adoption will grow even faster. Cisco is investing in connectivity management, IoT networking, IoT security, and edge computing to accelerate the adoption of IoT use-cases.

Read more from the original source:
Cisco Enhances IoT Platform with 5G Readiness and Machine Learning - The Fast Mode

Using machine learning, a Stanford-led research team has slashed battery testing times - a key barrier to longer-lasting, faster-charging batteries for electric vehicles.

Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology.

For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent. Although the group tested their method on battery charge speed, they said it can be applied to numerous other parts of the battery development pipeline and even to non-energy technologies.

"In battery testing, you have to try a massive number of things, because the performance you get will vary drastically," said Ermon, an assistant professor of computer science. "With AI, we're able to quickly identify the most promising approaches and cut out a lot of unnecessary experiments."

The study, published by Nature on Feb. 19, was part of a larger collaboration among scientists from Stanford, MIT and the Toyota Research Institute that bridges foundational academic research and real-world industry applications. The goal: finding the best method for charging an EV battery in 10 minutes that maximizes the battery's overall lifetime. The researchers wrote a program that, based on only a few charging cycles, predicted how batteries would respond to different charging approaches. The software also decided in real time what charging approaches to focus on or ignore. By reducing both the length and number of trials, the researchers cut the testing process from almost two years to 16 days.

"We figured out how to greatly accelerate the testing process for extreme fast charging," said Peter Attia, who co-led the study while he was a graduate student. "What's really exciting, though, is the method. We can apply this approach to many other problems that, right now, are holding back battery development for months or years."

Designing ultra-fast-charging batteries is a major challenge, mainly because it is difficult to make them last. The intensity of the faster charge puts greater strain on the battery, which often causes it to fail early. To prevent this damage to the battery pack, a component that accounts for a large chunk of an electric car's total cost, battery engineers must test an exhaustive series of charging methods to find the ones that work best.

The new research sought to optimize this process. At the outset, the team saw that fast-charging optimization amounted to many trial-and-error tests - something that is inefficient for humans, but the perfect problem for a machine.

"Machine learning is trial-and-error, but in a smarter way," said Aditya Grover, a graduate student in computer science who co-led the study. "Computers are far better than us at figuring out when to explore - try new and different approaches - and when to exploit, or zero in, on the most promising ones."

The team used this power to their advantage in two key ways. First, they used it to reduce the time per cycling experiment. In a previous study, the researchers found that instead of charging and recharging every battery until it failed - the usual way of testing a battery's lifetime -they could predict how long a battery would last after only its first 100 charging cycles. This is because the machine learning system, after being trained on a few batteries cycled to failure, could find patterns in the early data that presaged how long a battery would last.

Second, machine learning reduced the number of methods they had to test. Instead of testing every possible charging method equally, or relying on intuition, the computer learned from its experiences to quickly find the best protocols to test.

By testing fewer methods for fewer cycles, the study's authors quickly found an optimal ultra-fast-charging protocol for their battery. In addition to dramatically speeding up the testing process, the computer's solution was also better - and much more unusual - than what a battery scientist would likely have devised, said Ermon.

"It gave us this surprisingly simple charging protocol - something we didn't expect," Ermon said. Instead of charging at the highest current at the beginning of the charge, the algorithm's solution uses the highest current in the middle of the charge. "That's the difference between a human and a machine: The machine is not biased by human intuition, which is powerful but sometimes misleading."

The researchers said their approach could accelerate nearly every piece of the battery development pipeline: from designing the chemistry of a battery to determining its size and shape, to finding better systems for manufacturing and storage. This would have broad implications not only for electric vehicles but for other types of energy storage, a key requirement for making the switch to wind and solar power on a global scale.

"This is a new way of doing battery development," said Patrick Herring, co-author of the study and a scientist at the Toyota Research Institute. "Having data that you can share among a large number of people in academia and industry, and that is automatically analyzed, enables much faster innovation."

The study's machine learning and data collection system will be made available for future battery scientists to freely use, Herring added. By using this system to optimize other parts of the process with machine learning, battery development - and the arrival of newer, better technologies - could accelerate by an order of magnitude or more, he said.

The potential of the study's method extends even beyond the world of batteries, Ermon said. Other big data testing problems, from drug development to optimizing the performance of X-rays and lasers, could also be revolutionized by the use of machine learning optimization. And ultimately, he said, it could even help to optimize one of the most fundamental processes of all.

"The bigger hope is to help the process of scientific discovery itself," Ermon said. "We're asking: Can we design these methods to come up with hypotheses automatically? Can they help us extract knowledge that humans could not? As we get better and better algorithms, we hope the whole scientific discovery process may drastically speed up."

Originally posted here:
Machine learning could speed the arrival of ultra-fast-charging electric car - Chemie.de

BRUSSELS The European Union agreed on Monday to launch a new naval and air mission to stop more arms reaching the warring factions in Libya, overcoming initial objections from Austria and Hungary, which feared the ships might attract migrants and enable more of them to reach Europe.

The decision by foreign ministers was a victory for the new European foreign-policy chief, Josep Borrell Fontelles, who has criticized the need for unanimity among all member states to make decisions on foreign and security policy.

The new mission will be limited to the eastern Mediterranean, where most arms smuggling to Libya takes place, away from the routes most migrants take to try to reach Europe from chaotic Libya. The agreement satisfies the objections of fiercely anti-immigrant nations like Austria and Hungary, which have moved sharply to the right in recent years.

Mr. Borrell had warned that the European Union could not stand idly by while nearby Libya was embroiled in civil war aided most recently by Russia and Turkey, which support opposite sides.

At a Berlin summit meeting last month, world leaders agreed to encourage a cease-fire and stop the flow of weapons into Libya, but little has changed on the ground and fighting continues.

The Tripoli government of Fayez al-Sarraj, backed by the United Nations, is under attack from forces led by Khalifa Hifter, who controls much of the south and east of the country. He is supported by states including Russia, the United Arab Emirates and Egypt, while Mr. Sarraj is supported by Turkey and Qatar.

The E.U.s new arms blockade replaces another E.U. mission which had gone defunct Operation Sophia which had been rescuing migrants off the Libyan coast and ferrying them to Europe. Italy, Austria and Hungary objected, fearing an influx of migrants. Operation Sophia had been inactive since last March, but was never officially ended.

Mr. Borrell had hoped to revive Operation Sophia., and criticized Austria on Sunday for blocking its revival, saying that it was absurd for a landlocked country without a navy to exercise such a veto.

Austria has taken a tough anti-migrant stance under its current chancellor, Sebastian Kurz, and its position was supported by another landlocked country, Hungary, whose right-wing populist government has also drawn a hard line against migration.

We all agree to create a mission that blocks the flow of arms into Libya, Foreign Minister Luigi di Maio of Italy told reporters, referring to a U.N. arms embargo first imposed in 2011 but widely violated. Re-establishing the embargo is seen as vital to stabilizing the Libyan conflict. Recent efforts to restore a cease-fire there have collapsed.

But Mr. di Maio also said that if the mission creates a pull factor, that is to say the ships attract migrants, the mission will be stopped.

Austrian Foreign Minister Alexander Schallenberg said similarly that Vienna would be vigilant for any signs that the mission, which will take several weeks to set up, was attracting migrants into Europe.

There is a basic consensus that we now want a military operation and not a humanitarian mission, he said.

Mr. Borrell said that he hoped the operation could be patrolling by the end of March and would operate in international, not Libyan, waters.

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With Libya Still at War, E.U. Agrees to Try Blocking Weapons Flow - The New York Times

MILAN Italian authorities have arrested the captain of a Lebanese-flagged cargo ship which was seized in the port of Genoa on suspicion of trafficking arms to Libya, including tanks and artillery, the city's chief prosecutor said on Friday.

Any deliveries of weapons to Libya would be in violation of a United Nations embargo, although U.N. officials say the embargo has been subject to frequent violations.

The vessel, the Bana, was blocked by police in Genoa harbor on Feb. 3.

It was then searched after a ship's officer told Italian authorities that weapons had been loaded onto the ship at the Turkish port of Mersin then transported to the Libyan capital Tripoli, a judicial source said.

The shipment included tanks, howitzers, machineguns and air defense systems, the source said.

The ship was originally scheduled to sail from Turkey to Genoa. But according to the informant, who has requested political asylum, Turkish military officers escorting the shipment had told the crew to declare that the stop in Tripoli was due to mechanical problem.

The Bana then continued without cargo to Genoa in order to load cars in the Italian port, chief prosecutor Franco Cozzi said.

The ship's captain, Joussef Tartiussi, a Lebanese national, was arrested on Wednesday on suspicion of trying to influence his crew's testimony and concealing evidence, Cozzi said.

Tartiussi's lawyer declined to comment.

The Turkish government backs Fayez al-Sarraj, prime minister of Libya's internationally-recognized government, which has been fighting since last April for control of Tripoli against the Libyan National Army led by eastern-based commander Khalifa Haftar.

Italian authorities are analyzing the ship's navigation equipment and mobile phones of crew members with the aim of verifying the route followed by the Bana, whose transponders were turned off after it left the Turkish port, Cozzi said.

He said that even though the alleged trafficking had not taken place in Italian waters, it was still necessary to carry out the investigation because if it had occurred, any arms deliveries would be in violation of the U.N. embargo.

(Reporting by Emilio Parodi; editing by James Mackenzie and Angus MacSwan)

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Italy Arrests Ship's Captain Over Alleged Libya Arms Trafficking - The New York Times