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How blockchain technology is impacting cloud accounting and tax?

 

For example, AI-based optical character recognition is transforming the way in which businesses log and store expense information. Blockchain technology delivers interoperability between traditionally siloed tax data structures, streamlining compliance and reporting.

The most profound way in which technology is changing the tax ecosystem, however, is the rise of cloud accounting.  

What is cloud accounting?

Cloud accounting allows businesses to eliminate the need for expensive local financial data storage, which presents security risks and take advantage of powerful taxation software and services based on remote servers.

Cloud accounting services include payroll, accounting, invoicing, accessibility, and third-party integration features, and can significantly reduce the overheads associated with executing an effective tax strategy. Businesses that use cloud accounting services benefit from a cost reduction in hardware maintenance, and an improved user interface.

Enterprise and the cloud

The use of cloud accounting has dramatically increased over the last decade. Data published by Forbes demonstrates that 80 percent of major enterprise organizations now operate critical financial software on cloud-based platforms that significantly improve overall business efficiency. 

Also Read: How I led a startup within a MNC

Getting cloud-based tax and accounting software set up at your business is relatively simple, but it’s best to enlist the help of accountancy services to ensure you’re collecting data and reporting in a compliant manner, which automates tasks that would be time-consuming tasks. 

Cryptocurrency payments

The third-party integration offered by cloud accounting platforms are extremely flexible — many online merchants, for example, accept digital currencies such as Bitcoin as a payment method. Traditional taxation software can struggle to track cryptocurrency payments, whereas third-party applications are able to provide full reporting functionality designed specifically for digital assets.

Another modern tax tech is able to assist with the complex nature of cryptocurrency tax reporting. Major cryptocurrency exchanges can work to keep customer data extremely secure and private, which can make it difficult to track trades for capital gains, income tax reporting and atomic swaps

Financial sensitive data

Data breaches are extremely costly for businesses of all sizes. Both large and small scale enterprises are often the target of hackers that attempt to steal sensitive financial data. A single breach can incur a heavy cost.

Also Read: What I’ve learned from building lean in Asia

Data published by IBM reveals that the average data breach costs businesses almost $4 million, with each individual breached record valued at over $150. Cloud-based tax solutions and accounting software help to keep your business data safe with enterprise-scale security, eliminating the threat of data breaches. 

Key takeaway

Many businesses operate under tight budget constraints that doesn’t leave much room for professional full-time accounting staff. Cloud-based tax tech reduces the total amount of time businesses must direct toward financial strategy and tax reporting, freeing up capital that can be reinvested into the business itself. 

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India’s CarDekho buys Carmudi Philippines, aims to digitise the country’s auto ecosystem

CarDekho Group, which owns and operates a string of auto portals in India, has acquired car classifieds site Carmudi Philippines.

It is the second Southeast Asian country where CarDekho has started operations after its launch of Indonesia operations under OTO.com in 2016

The acquisition of Carmudi is aligned with CarDekho’s business strategy to expand and fortify its footprint across the region. In the Philippines, CarDekho will aggressively focus on building up and digitising the ecosystem and offer solutions to both new and used car buyers.

Also Read: ‘We’re burning money’, says Lippo Group’s Mochtar Riady; to sell 70% stake in e-wallet OVO

“The Philippines’s underlying macro fundamentals make it an extremely promising market. The market demand for new private vehicles in the Philippines has grown at a CAGR of 14 per cent during 2014-2018 with new car sales crossing 380,000 units in 2018. We see this growth as a big opportunity to digitise the Philippines auto ecosystem and engage with consumers throughout their online car buying journey. Our strong ecosystem play has made us a leader in India and Indonesia. And now we are expecting the same for the Philippines,” Umang Kumar, Co-founder and President, CarDekho, said.

“CarDekho’s backing will help us in further strengthening our position in this region. This means added enhancements in technology, processes, and platform resulting in great user experience. Carmudi is already known for quality listings, powerful search, and one-stop convenience but the collaboration with CarDekho will enable us to digitalise and simplify the entire auto ecosystem,” said Cholo Syquia, Country Head, Carmudi Philippines.

Founded in 2008 and headquartered in Jaipur, CarDekho currently operates auto sites such as CarDekho.com, Gaadi.com, ZigWheels.com, BikeDekho.com and PowerDrift.com in India. The group recently launched an insurance portal called InsuranceDekho.com offering services in motor and health insurance, and Gaadi.com is a one-stop destination for selling pre-owned cars.

The group also runs specialised portals like TyreDekho.com and TrucksDekho.com.

Also Read: 10 mistakes that new entrepreneurs tend to make and should avoid in 2020

CarDekho also works actively with over 4,000 new auto dealerships and 3,000 used car dealers across India. Also, it works in collaboration with more than ten financial institutions and 18 insurance companies across the country to facilitate used car financing and insurance for both buyers and sellers.

CarDekho has raised funding from marquee investors which include Sequoia IndiaHillhouse CapitalCapitalG(formerly known as Google Capital), Tybourne Capital, HDFC Bank, Axis Bank, Times InternetRatan Tata, and Trifecta.

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How learning like babies can be the future of AI?

 

 

A three-year-old Stacy sat with her dad beside her one beautiful Sunday morning. It was the one day where she got to interact and play with him. The rest of the week, John was busy experimenting with theories and building machine learning models that could have an impact on the world. Like an enthusiastic toddler, Stacy would get up early on Sunday mornings to do all her favourite things with her father. One of them included building Lego structures.

This Sunday was different than the rest because it was Stacy’s birthday and John had her brought her a brand new Lego set with unique characters. As Stacy and Jon sat in the backyard early morning, John gave her the present.

An excited Stacy immediately unwrapped the Lego box and took out the pieces. Just like every other time, she started building it in an upward direction. Stacy loved building towers, traffic lights, and skyscrapers and was going to do the same this day.

The only difference being she would now make the all-new Lego character sit on top of those structures. But, that’s when John interrupted and suggested building it sideways.

Also Read: How Chinas Greater Bay Area initiative will create a testbed for AI and decentralised tech industry

Could Stacy build the Lego sideways or in any other direction, because she had only been building it upwards? Five minutes later, Stacy starts sticking tapes to the wall. Its fifteen minutes now and little Stacy exclaims, Here Dad! Batman is now sitting on my Lego train track that enters the wall!

A machine inspired experiment

John’s attempt to encourage Stacy is an example of an experiment. Stacy could easily apply her knowledge and experience of building Legos upwards to building them sideways. And all this she could do within minutes. What Stacy demonstrated can be called as mere common sense, which even the most advanced computers of today’s generation fail to display.

Unlike Stacy, who could quickly learn to apply her existing knowledge to a new context, modern artificially intelligent systems still find such a case hard to reproduce. Like John, many other scientists and experts believe that artificial intelligence could learn from babies. While this could open many possibilities for the future, it could also impact the world in ways we can’t even imagine.

Scientists in the past have tried feeding direct knowledge to computers to create artificial intelligence. However, since that approach failed miserably, we now have machine learning to the rescue.

Today’s machine learning techniques enable the computer to learn on their own by figuring out what to do upon looking at a large dataset. Researchers from all across the world claim that these machine learning models can be trained to learn almost anything and everything. It even includes one of the human’s most prized possesions-common sense.

The idea of common sense

But it seems like these researchers are ignoring decades of scientific work in the field of cognitive science and developmental psychology that demonstrate that humans have some innate abilities. The inherent capabilities of humans are nothing but programmed instincts that appear in a child as they are born and grow up. These help us think abstractly, clearly and fundamentally attribute to what we call common sense.

For a more precise distinction, our machine learning models today rely on a large number of data sets to produce accurate results. Even the meta-learning models that learn only from a limited number of data sets need a few hundred for the task. But, the underlying question is, do children learn in this manner? Did John show Stacy a thousand cases of building a Lego sideways before she could make it?

In another instance, let’s take a much simpler problem, where a child learns to identify objects around them. Do we need to show a child a few thousand apples, before they recognize it as one? The answer is no, and it sounds radically mindless when we apply machine learning methods to human beings. The answer to why such practices are inapplicable to even the most underdeveloped human brains lies in our innate abilities.

Artificial Intelligence researchers ought to bring these qualities and instincts of a child’s learning to complex machines. However, most systems that are riding high on machine learning’s success seldom find this of importance. Computer scientists appreciate the simplicity and one of their goals involves reducing the debugging of complex Java development code.

Also Read: How Chinas Greater Bay Area initiative will create a testbed for AI and decentralised tech industry

Josh Tenenbaum, a psychologist at the Massachusetts Institute of Technology (MIT) in Cambridge, says that big companies like Facebook and Google are another reason why artificial intelligence has reached its limits and is being further pushed in that direction. These companies are merely interested in solving short term problems using machine learning.

Some of these are facial recognition and the web search that can be done by training a model on a vast number of data sets. Since such models work remarkably well, there seems no need for exploring an intelligence that is innate like a child.

The way machines learn

However, there is no doubt that the existing techniques have led to some out of the ordinary breakthroughs. Today, you can give a machine some millions of pictures of animals and label them like a cat or a dog. Even in the absence of any further information about the characteristics of cats and dogs, the machine will be able to classify new examples of cats and dogs. To achieve this, the machine learning models abstract some statistical patterns from the pictures and then use them for classification.

Similarly, a machine called Google’s Deep Mind’s Alpha Zero can be trained to play a game of Chess or a video game right from scratch. The logic behind it is simple. When a computer performs a game, it gets a score. Over time, as it keeps on playing millions of such games, it learns to maximize its score. Alpha Zero has also been able to beat IBM’s Deep Blue, another trained model, at Chess. The surprise comes when these models don’t even understand the mechanics of the game and go on finding statistical patterns to increase their learning. In other words, they are not intelligently learning but learning from their experiences.

In spite of being a great use, the problem is that these algorithms have developed a limitation. The more you feed them with data, the better they will learn. But when it comes to generalizing from all this data, they fail miserably.

Imagine this in the context of babies. They don’t need millions of samples to learn. They learn much more generally and accumulate a more robust kind of knowledge when compared to artificially intelligent machines. For researchers, the future of artificial intelligence lies in unravelling the mystery behind the way babies learn, and the way devices can implement it.

Thinking about the big picture or the future, scientists need to develop AI so that it can solve many fierce problems involving common sense and flexibility than today. If we want to imagine a world with autonomous cars that can run in chaotic traffic, or bots that explain the news to a reader, we need a build AIs to solve problems in a more generalized environment.

However, with some research going on in the world, there is some hope left for the future. Massachusetts Institute of Technology (MIT) recently launched a research initiative called Intelligent Quest to understand human intelligence in terms of engineering. The initiative is already raising millions of dollars by now and in some ways, trying to answer a similar issue like that of nature versus nurture in learning theories.

Even the Defense Advanced Research Projects Agency is working on a project called the Machine Common Sense to understand how babies and young children learn. As astonishing as it sounds, the government research lab that helped invent the Internet and the computer, is now partnering with child psychologists and computer scientists for the task.

A different approach to learning

There might be evidence for developing a system with childlike learning capabilities because many credible organizations across the world are investing in such research. But there are problems with machines that we seek to solve. Human babies, the smartest among all other species, learn by the trial and error method. Apart from this, cognitive development scientists say that as babies, we are born with some basic instincts that help us quickly gain a flexible common sense.

For machines, it has been challenging because we haven’t since any conceptual breakthroughs in machine learning since the 1980s. Most of the popular machine learning algorithms came into the picture back then. To date, all we have seen in the name of advancements is ever-expanding sets of data that are being used to train machine learning models on a large scale.

Young children, on the other hand, learn differently from the machines. The kind of data that the machine learns from are generally curated by the people and of good quality and clear category.

You won’t find people posting blurry pictures of themselves. All they try is to display the best shot. Similarly, games like Chess are defined by people to work within a fixed range of possibilities and under specific rules. But when it comes to children, it is the opposite of clarity.

Ongoing research at Stanford University suggests that babies see a series of chaotic and poorly filmed videos that consist of a few familiar things such as toys, parents, dogs, food, etc. These move around at odd angles and are the opposite of millions of clear photographs present in an internet data set.

Another factor comes into play when machines learn. It is called ‘Supervision’. Machines need to be told what they’re learning. When images are annotated, they are given particular labels. Similarly, when machines play games, each of their moves is scored. All this helps the machine see what it exactly needs to learn.

The data for children is, however, largely unsupervised. Parents do tell their babies notions such as ‘good job’, ‘danger’ or tell them what animal is given in a picture. But it is mostly to keep them safe and sound. A large part of the baby’s learning is spontaneous and motivated by one’s self.

Also Read: Today’s top tech news: Tourplus raises US$400K, developer school 42 launches in Malaysia

Even if we provide large data sets of data to a machine, they cannot figure out the same kind of generalizations as children do. Their knowledge can be considered shallow, and they can be fooled very easily with what is known as adversarial examples. For example, if you give an AI an image that has jumbled pixels, it will most probably classify it as a cat if the pixels fit the right statistical pattern of its learning. However, a child will seldom make that mistake.

In a similar instance, advanced AI models such as Alpha that we have today, do not imply common sense in their learning. If an AI learned the game Go on a standard 19 * 19 board, it wouldn’t be able to demonstrate the same playing skills on a 21 * 21 board. Instead, the AI would have to learn the game anew. Scientists have also tested this theory with the domain of odd and even numbers. A network was trained to take input as an even number and simply spit it out. However, when the same number was tested with odd numbers, it immensely faltered. This isn’t the case with a child.

Conclusion

AI as a program keeps an eye on the learner and dictate them whether they are right or wrong at every step of the way. This is quite unlike human children, who under helicopter parenting might be able to do a designated task well but fail in critical matters such as creativity and resiliency.

Alter the problem even with the smallest degree, and they will have to learn all over again. That’s the case with machines, and it is exactly how they differ from human children.

No doubt we are far away from approaching a human-like intelligence level in machines. While this might not be our sole purpose, we still want an AI like C3PO that can make us even smarter. Our only solution to achieve a desirable AI is to take cues from babies and create more curious AIs than obedient ones.

Editor’s note: e27 publishes relevant guest contributions from the community. Share your honest opinions and expert knowledge by submitting your content here.

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Fintech startup Halofina secures pre-Series A funding led by Mandiri Capital Indonesia, helping millennials managing finances

Indonesian financial management startup Halofina announces that it has raised an undisclosed amount of pre-Series A funding round led by Mandiri Capital Indonesia, backed by Finch Capital, DealStreetAsia has learned.

The company said that it will use the fresh capital for product development, organization and talent enhancement, as well as strategic partnerships.

This is the company’s second round of funding, as it previously received funding from Singapore-based Plug and Play, and Rekanext as well as Indonesian firm Radika in late 2018.

Halofina was founded in 2017 by Adjie Wicaksana and financial industry veteran Eko Pratomo. It is an AI-based personal financial planning application that helps its users manage their finances and build investment strategies.

Halofina said that its users are largely the millennial generation and middle-upper income group.

Also Read: Plug and Play Indonesia brings in 17 startups into its 3rd batch

The company first launched its app in March 2018, and started off as a company that operated in the field of offline and online personal finance education, said Halofina co-founder and CEO Wicaksana in an interview with DealStreetAsia.

According to studies, the middle-income class in 2020 is expected to be 140 million people. And the majority of the middle-income group is the millennials. Upper middle income, millennials and digital savvy population amount to around 35 million people in Indonesia.

“The number is significant, particularly when we take into account the fact that the investors in our capital market are only over 1.5 million,” Wicaksana said.

Halofina’s main feature is LifePlan, which helps users calculate and plan their future financial needs for life goals like marriage, housing, education, vehicle, by giving them a cost estimate and calculating how much needs to be saved per month. The feature also gives a recommendation on asset allocation, based on users’ goals, risk profile, and financial profile, helping users decide on the investment products to purchase, which currently consists of mutual fund and gold.

The platform then continues to provide users with the option of the products they need, as well as track and monitor it afterward.

Also Read: Meet the 10 Indonesian fintech startups you may have never rooted for before

“In the first quarter of next year, we will work with third parties to develop educational content such as an audiobook, podcast, video, and even online consultation which will enable experts to make content and provide consultation service,” Wicaksana said.

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Today’s top tech news: Lynk gets new funding, Ola considering IPO and layoffs

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Global “Knowledge-as-a-Service” platform Lynk announces successful funding round- Press release

Lynk, that spearheaded the “Knowledge-as-a-Service” (KaaS) sector, announced the close of a funding round led by Singapore-based MassMutual Ventures Southeast Asia (MMV SEA). Alibaba Entrepreneurs Fund and Wavemaker Partners, who led the seed round, also participated.

Lynk allows customers to access expertise and insights from advisors to innovate, enter new markets, and quickly understand business risk and evaluate opportunities. Unlike other expert access products, Lynk is focused on its KaaS technology platform that utilizes natural language processing, conversational AI analytics, and machine learning with human-in-the-loop to enable expert knowledge acquisition and sharing at scale.

Anvesh Ramineni, Managing Director at MassMutual Ventures Southeast Asia said, “Lynk’s unparalleled growth and proprietary approach to harness unstructured data in building out a platform that can enable a wide range of potential applications made it an obvious choice for us. We look forward to supporting the company’s expansive growth across various regions of the world.”

Indian insurtech startup Acko raises funds from Ascent Capital- DealStreetAsia

Insurance tech startup Acko General Insurance has raised US$16 million in fresh funding from growth capital provider Ascent Capital, according to the company’s filings with the registrar of companies (RoC), said a report by DealStreetAsia.

Founded by Coverfox co-founder Varun Dua in 2016, Acko offers general insurance products, including auto, smartphone, and travel insurance. The startup has built a diverse base of partnerships with consumer internet platforms across e-commerce and travel categories, besides auto manufacturers, to expand its customer base. Acko has a tie-up with cab-hailer Ola and other online travel aggregator platforms, such as RedBus and Goibibo, for offering online travel insurance. It has around 45 million registered customers and around 20 digital partners, including travel, cab-hailing, and e-commerce platforms.

The Mumbai-based insure-tech startup had earlier raised money from Binny Bansal, SoftBank’s Kabir Mishra, Amazon Inc., Accel Partners and Infosys founders Narayana Murthy and Kris Gopalakrishnan.

In March, it had secured a US$65-million Series C round led by Bansal. In May 2018, Acko had secured a US$12 million round led by Amazon India and Ashish Dhawan, founder of homegrown private equity fund ChrysCapital. In 2017, it had raised US$30 million in seed money from Accel Partners and SAIF Partners, among others. To date, it has raised over US$100 million.

Facebook caught in the crossfire of Singapore’s ‘fake news’ law- Bloomberg

The Singaporean government on Friday invoked its recently enacted “fake news” law, this time ordering Facebook Inc. to publish a correction notice on a post made by an anti-government blog, according to a news article by Bloomberg.

In the third such order in a week, an arm of the Ministry of Communications and Information instructed Facebook to correct a States Times Review post accused of using falsehoods to criticize the ruling People’s Action Party. The government had previously denounced the report that police had arrested a government whistle-blower and taken down information that exposed a plot to turn the affluent city into a Christian state.

Also read: The world should wish the Singapore fake news law is Fake News

Singapore introduced its controversial fake-news law as it prepares to hold general elections by April 2021, though the ruling party has called for early polls in recent cycles. Officials, including Home Affairs and Law Minister K Shanmugam, have openly questioned the ability of internet companies to handle widespread misinformation — a growing scourge of elections around the world. But critics worry the new legislation can be used to clamp down on free speech. A Facebook representative acknowledged it has received the government request but declined further comment.

Ola may lay off 225 employees as SoftBank-backed firm gears up for IPO- Reuters

Softbank-backed Indian ride-hailing firm Ola, aims to begin the IPO process by the end of March 2021 and plans to cut its 4,500-strong workforce by up to 5% or 225 heads as part of preparations, people with direct knowledge of the matter told Reuters.

The news comes as tech investor SoftBank smarts from the abandoned share sale of major portfolio firm WeWork, as well as its first quarterly loss in 14 years after an $8.9 billion hit to its Vision Fund, through which it is Ola’s top stakeholder.

Ola, officially ANI Technologies Pvt Ltd, is India’s home-grown rival to US peer and fellow SoftBank portfolio firm Uber Technologies Inc. Local media have previously reported Ola was targeting an initial public offering (IPO).

As part of that effort, Ola has engaged McKinsey & Company and EY as consultants said the executive. At the same time, Ola plans to reduce its 4,500-strong permanent workforce by 4% to 5%, said another person.

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