Author: Manahel Thabet

23 Sep 2019
The AI arms race spawns new hardware architectures

The AI arms race spawns new hardware architectures

As society turns to artificial intelligence to solve problems across ever more domains, we’re seeing an arms race to create specialized hardware that can run deep learning models at higher speeds and lower power consumption.

Some recent breakthroughs in this race include new chip architectures that perform computations in ways that are fundamentally different from what we’ve seen before. Looking at their capabilities gives us an idea of the kinds of AI applications we could see emerging over the next couple of years.

Neuromorphic chips

Neural networks, composed of thousands and millions of small programs that perform simple calculations to perform complicated tasks such as detecting objects in images or converting speech to text are key to deep learning.

But traditional computers are not optimized for neural network operations. Instead they are composed of one or several powerful central processing units (CPU). Neuromorphic computers use an alternative chip architecture to physically represent neural networks. Neuromorphic chips are composed of many physical artificial neurons that directly correspond to their software counterparts. This make them especially fast at training and running neural networks.

The concept behind neuromorphic computing has existed since the 1980s, but it did not get much attention because neural networks were mostly dismissed as too inefficient. With renewed interest in deep learning and neural networks in the past few years, research on neuromorphic chips has also received new attention.

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22 Sep 2019
Artificial Intelligence (AI) creates new possibilities for personalisation this year

Artificial Intelligence (AI) creates new possibilities for personalisation this year

Technology brands expand beyond their core products and turn themselves into a lifestyle

New Delhi: Artificial Intelligence (AI) and cross-industry collaborations are creating new avenues for data collection and offering personalised services to users this year, according to a report.

Among other technology trends that are picking up this year are the convergence of the smart home and healthcare, autonomous vehicles coming for last-mile delivery and data becoming a hot-button geopolitical issue, according to the report titled “14 Trends Shaping Tech” from CB Insights.

“As a more tech-savvy generation ages up, we’ll see the smart home begin acting as a kind of in-home health aide, monitoring senior citizens’ health and well being. We’ll see logistics players experiment with finally moving beyond a human driver,” said the report.

“And we’ll see cross-industry collaborations, whether via ancestry-informed Spotify playlists or limited edition Fortnite game skins,” it added.

In September 2018, Spotify partnered with to utilise DNA data to create unique playlists for individuals.

Playlists reflect music linked to different ethnicities and regions. A person with ancestral roots in Bengaluru, for example, might see Carnatic violinists and Kannada film songs on their playlists.

DNA data is also informing how we eat. GenoPalate, for example, collects DNA info through saliva samples and analyses physiological components like an individual’s ability to absorb certain vitamins or how fast they can metabolize nutrients.

From there, it matches this information to nutrition analyses that it has conducted on a wide range of food and suggests a personalised diet. It also sells its own meal kits that use this information to map out menus.

“We’ll also see technology brands expand beyond their core products and turn themselves into a lifestyle,” said the report.

For example, as electric vehicle users need to wait for their batteries to charge for anywhere from 30 minutes to two hours, the makers of these vehicles are trying to turn this idle time into an asset.

China’s NioHouse couples charging stations with a host of activities. At the NioHouse, a user can visit the library, drop children off at daycare, co-work, and even visit a nap pod to rest while charging.

Nio has also partnered with fashion designer Hussein Chalayan to launch and sell a fashion line, Nio Extreme.

Tech companies today are also attempting to bridge the gap between academia and the career market.

Companies like the Lambda School and Flatiron School offer courses to train students on exactly the skills they will need to get a job, said the report.

These apprenticeships mostly focus on tech skills like computer science and coding. Training comes with the explicit goal of employment and students only need to pay their tuition once they have landed a job that pays them above a certain range.

Investors are also betting on the rise of digital goods. While these goods cannot be owned in the physical world, they come with clout, and offer personalisation and in-game experiences to otherwise one-size-fits-all characters, the research showed.


21 Sep 2019
To survive, asset managers need to embrace disruptive technologies

To survive, asset managers need to embrace disruptive technologies

FROM THE OUTSIDE, asset management looks like an exciting industry that’s immune to technology — but that’s not true.

Changes in regulations, rising customer expectations, and the growing pressure from new-age competitors are forcing the asset management industry to explore disruptive technologies in order to stay in business.

According to a new study by the Investment Company Institute (ICI), the asset management industry is at a critical juncture in its history — investing in innovation and reinvigorating their products and processes.

In fact, while front-office transformations remain slow, operations executives are aggressively transforming their operating models to achieve greater agility and cost-effectiveness, as they take on the challenges of supporting more complex products and services.

The study revealed that 64 percent of firms surveyed for the report have completed a major operating model change in the past three years to improve operational efficiencies.

Asset managers, many of whom are uncertain about the ability of their operations and technology to support the firm’s objectives, believe they need to alter their strategies at the front-end to focus on driving distribution and creating differentiated products.

“Doing this effectively requires embracing technology and innovation, including investment platform technology and artificial intelligence, for better investment decision-making,” said Accenture Senior MD Michael Spellacy — whose firm collaborated with ICI to create the report.

The study shows that 55 percent of asset management firms reported having a formal initiative in place to evaluate the business and operational potential of new technologies such as the cloud and APIs.

Asset managers join the fintech ecosystem

Given the rapid pace of development in the world of technology, asset managers are also evaluating partnerships with the fintech ecosystem and exploring collaborations with start-ups, accelerators, and incubators.

The study also reveals that middle office functions—including collateral management, data management, derivatives processing, and transaction management—will be the biggest beneficiaries of fintech partnerships.

In the back office, the report found that respondents expect that fintech firms will be able to quickly and successfully help transform expense management, fund accounting, and financial reporting.

Approximately one-third of the firms agreed that at-scale middle office fintech partnerships were common across the industry, suggesting that these partnerships are already delivering results.

According to analysts, however, in order to ensure the success of a fintech partnership, asset managers need to adopt a laser-like focus on delivering bottom-line impact.

“It is vital to avoid the initial focus on ‘shiny objects’ that can result in proofs of concept that lack a clear vision of eventual production and outcomes,” the report pointed out.

Transformation key to long-term success

Technology adoption in the asset management industry isn’t exactly driven by consumer demand for better experiences — but stakeholders do expect more transparency, accountability, and control over their monies.

Further, with margin pressures making profitability difficult, using disruptive technologies might provide new revenue opportunities to asset managers through smarter and more intelligent product and service portfolios.

The survey conducted by ICI suggests that decision-makers in the industry are aware of the changing landscape and are taking action.

“Asset managers are disrupting their legacy operating models and skillsets to reequip their firms to win in a disrupted future.



“Executing this transformation successfully is imperative for long-term success,” concluded the report.

Many asset managers have shifted their strategic focus to the front office and clients and are looking at accelerating their journey to technology in order to boost operational capabilities and acquire/develop top talent in order to support the evolving needs of the front office.

In the near future, partnerships with the fintech ecosystem and customer-driven technology projects are expected to drive the asset management industry into a new space — where digital is weaved into the very fabric of the organization.


19 Sep 2019
Artificial Intelligence (AI) Stats News: AI Is Actively Watching You In 75 Countries

AI Is Actively Watching You In 75 Countries

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the strong state of AI surveillance worldwide, the lack of adherence to common privacy principles in companies’ data privacy statement, the growing adoption of AI by global businesses, and the perception of AI as a major risk by institutional investors.

AI surveillance and the state of data privacy

At least 75 out of 176 countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms (56 countries), facial recognition systems (64 countries), and smart policing (52 countries); technology linked to Chinese companies—particularly Huawei, Hikvision, Dahua, and ZTE—supply AI surveillance technology in 63 countries and U.S. firms’ technology—from IBM, Palantir, and Cisco—is present in 32 countries; 51% of advanced democracies deploy AI surveillance systems [Carnegie Endowment for International Peace AI Global Surveillance (AIGS) Index]

An analysis of 29 variables in 1,200 privacy statements against common themes in three major privacy regulations (the EU’s GDPR, California’s CCPA, and Canada’s PIPEDA) found that many organizations’ privacy statements fail to meet common privacy principles; less than 1% of organizations had language stating which types of third parties could access user data; only 2% of organizations had explicit language about data retention; only 32% of organizations had “readable” statements based on OTA standards [Internet’s Society’s Online Trust Alliance]


AI and the future of work

57% of technology companies do not expect technological advances will displace any of their workers in the next five years; 29% of respondents expect job displacement and 68% plan to retain workers by offering reskilling programs; software development (63%), data analytics (54%), engineering (52%), and AI/machine learning (48%) are the tech skills in highest demand [Consumer Technology Association survey of 252 tech business leaders]

Business adoption of AI

17% of 30 Global 500 companies have reported the use of AI/machine learning at scale and 30% reported selective use in specific business functions; in 3 years, 50% expect to be using AI/machine learning at scale; 26% have deployed RPA at scale across the enterprise or major functions; 65% say their use of RPA today is selective and siloed by individual groups or functions; in 3 years, 83% expect to have RPA deployed at scale; companies investing in AI report achieving on average 15% productivity improvements for the projects they are undertaking; most companies reported that their investments in AI-related talent and supporting infrastructure will increase approximately 50% to 100% in the next three years [KPMG 2019 Enterprise AI Adoption Study based on in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]

85% of organizations surveyed have a data strategy and 77% have implemented some AI-related technologies in the workplace, with 31% already seeing major business value from their AI efforts; top business functions for gaining most value from AI are sales (35%) and marketing (32%) and top technologies are machine learning (34%), chatbots (34%), and robotics (28%) [Mindtree survey of 650 IT leaders in the US and UK]

Expected business impact of AI

Top AI priorities for the next 3 to 5 years: customer and market insights that will refine personalization, driving sales and retention; back office and shared services automation to remove repetitive human tasks; finance and accounting streamlined to improve efficiency and compliance; analysis of unstructured voice and text data for specific functional use cases [KPMG 2019 Enterprise AI Adoption Study based in in-depth interviews with senior leaders at 30 of the world’s largest companies and other sources]

85% of institutional investors view AI as an investment risk that could potentially provoke societal backlash as well as geopolitical tension; 52% of the investors surveyed, who stated AI was a risk, also regarded it an opportunity, whereas 33% saw it as only a risk and 7% regard it as an opportunity only [BNY Mellon Investment Management and CREATE-Research in-depth, structured interviews with 45 CIOs, investment strategists and portfolio managers among pension plans, asset managers and pension consultants in 16 countries and a literature survey of about 400 widely respected research studies]

AI research successes

A deep learning algorithm, trained on non-imaging and sequential medical records, predicted the development of non-melanoma skin cancer in an Asian population with 89% accuracy [JAMA Dermatology]

Researchers at MIT developed a machine learning model that can estimate a patient’s risk of cardiovascular death. Using just the first fifteen minutes of a patient’s raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories. Patients in the top quartile were nearly seven times more likely to die of cardiovascular death when compared to the low-risk group in the bottom quartile. By comparison, patients identified as high risk by the most common existing risk metrics were only three times more likely to suffer an adverse event compared to their low-risk counterparts [MIT CSAIL]


18 Sep 2019
Disruptive Technology Design Considerations

Disruptive Technology Design Considerations

A disruptive technology, according to Harvard Business School professor Clayton M. Christensen who coined the term, is one that displaces an established technology, shaking up the industry, or a ground-breaking technology that creates a new industry.

In this context, disruptive technology could be a variety of innovations. For instance, 5G, RFID and AI used for personalisation in a retail or hotel setting. These can stream demographic-relevant content to each individual. It could also be Augmented Reality, Virtual Reality helmets, immersive higher pixel density displays or transparent displays.

Other examples are drones or invisible technologies, such as high fibre connectivity for higher quality transportation of content.

There are a number of considerations that need to be addressed with disruptive technology, particularly in critical environments. This is true whether it is a theme park that attracts thousands of visitors or an oil and gas control room which requires uptime 24/7.

It is advisable in these circumstances to balance new, leading-edge innovations with established, tried and tested technology. Adopting disruptive technologies early on comes with an element of risk. For example, any teething problems or bugs are likely to be discovered and ironed out further along in the process. Therefore, early adopters could experience reliability issues.

Creating immersive experiences

It’s not just reliability a designer needs to think about when installing new tech. VR has the potential to create impressive immersive experiences, however, the helmets can be isolating. There can also be issues around health and safety, and hygiene if hundreds and hundreds of people are going to use them continuously.

It must be stressed that the suitability of the technology  needs to be assessed on a case-by-case basis. Its compatibility with the existing tech in the environment also needs to be taken into account.

Where there is a tight time schedule, it may make better sense to go with tried and tested technology. Installing a piece of cutting-edge tech could require lengthy design, testing and implementation to ensure it meets its purpose.

When making a decision about the technology to use in a project, we assess it according to a number of factors. These are its readiness, its suitability and its fitness for purpose. We also look at where it fits in the client’s AV technology road map.

It is of key importance to look at the practical implications of new technology, and its ability to scale for use in large attractions. For example, in museums and theme parks, where large numbers of people will be using it constantly.

Another important consideration is the need for high-quality content to complement/ accompany the new technology.

At the heart of this process is technology master planning, something evidenced in our recent projects incorporating disruptive technologies.

MGM Cotai – The Spectacle

At the MGM Cotai Hotelthe Electrosonic team met and overcame the profound technical challenges of the world’s largest free-span glazed roof. The team creates an impactful digital art experience a year in advance of anything else on the market in terms of innovation and technology.

The project leverages the latest 4K displays, sufficiently bright to counteract background light in public environments. It shows the team’s capacity to optimize presentation for crisp videowalls. These can display cinematic portraits, big scenic shots of skylines, and multiple vignettes of attractions.

Electrosonic’s innovative multisensory experience takes place around the atrium. It highlights a global array of digital art. It also utilises true 4K LED processing of the media walls, creating ‘digital wallpaper’.


17 Sep 2019
Meet Five Synthetic Biology Companies Using AI To Engineer Biology

Meet Five Synthetic Biology Companies Using AI To Engineer Biology

TVs and radios blare that “artificial intelligence is coming,” and it will take your job and beat you at chess.

But AI is already here, and it can beat you — and the world’s best — at chess. In 2012, it was also used by Google to identify cats in YouTube videos. Today, it’s the reason Teslas have Autopilot and Netflix and Spotify seem to “read your mind.” Now, AI is changing the field of synthetic biology and how we engineer biology. It’s helping engineers design new ways to design genetic circuits — and it could leave a remarkable impact on the future of humanity through the huge investment it has been receiving ($12.3b in the last 10 years) and the markets it is disrupting.

The idea of artificial intelligence is relatively straightforward — it is the programming of machines with reasoning, learning, and decision-making behaviors. Some AI algorithms (which are just a set of rules that a computer follows) are so good at these tasks that they can easily outperform human experts.

Most of what we hear about artificial intelligence refers to machine learning, a subclass of AI algorithms that extrapolate patterns from data and then use that analysis to make predictions. The more data these algorithms collect, the more accurate their predictions become. Deep learning is a more powerful subcategory of machine learning, where a high number of computational layers called neural networks (inspired by the structure of the brain) operate in tandem to increase processing depth, facilitating technologies like advanced facial recognition (including FaceID on your iPhone).

Biology, in particular, is one of the most promising beneficiaries of artificial intelligence. From investigating genetic mutations that contribute to obesity to examining pathology samples for cancerous cells, biology produces an inordinate amount of complex, convoluted data. But the information contained within these datasets often offers valuable insights that could be used to improve our health.

In the field of synthetic biology, where engineers seek to “rewire” living organisms and program them with new functions, many scientists are harnessing AI to design more effective experiments, analyze their data, and use it to create groundbreaking therapeutics. Here are five companies that are integrating machine learning with synthetic biology to pave the way for better science and better engineering.

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16 Sep 2019
AI Can Now Pass School Tests but Still Falls Short on the Turing Test

AI Can Now Pass School Tests but Still Falls Short on the Turing Test

From winning at Go to passing eighth grade level multiple choice tests, AI is making rapid advances. But its creativity still leaves much to be desired.

On September 4, 2019, Peter Clark,  along with several other researchers, published “From ‘F’ to ‘A’ on the N.Y. Regents Science Exams: An Overview of the Aristo Project∗” The Aristo project named in the title is hailed for the rapid improvement it has demonstrated when it tested the way eighth-grade human students in New York State are tested for their knowledge of science. 

The researchers concluded that this is an important milestone for AI: “Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand. The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years.”

The Aristo project is powered by the financial resources and vision of Paul G. Allen, the Founder of the Allen Institute for Artificial Intelligence (A12). As the site explains, there are several parts to making AI capable of passing a multiple-choice test.

Aristo’s most recent solvers include:

The Information Retrieval, PMI, and ACME solvers that look for answers in a large corpus using statistical word correlations. These solvers are effective for “lookup” questions where an answer is explicit in text.
The Tuple Inference, Multee, and Qualitative Reasoning solvers that attempt to answer questions by reasoning, where two or more pieces of evidence need to be combined to derive an answer.
The AristoBERT and AristoRoBERTa solvers that apply the recent BERT-based language-models to science questions. These systems are trained to apply relevant background knowledge to the question, and use a small training curriculum to improve their performance. Their high performance reflects the rapid progress made by the NLP field as a whole.
While Aristo’s progress is, indeed, impressive, and, no doubt, there are some eight graders who wish they could find some way to carry along the AI with them to the test, it still is far from capable of passing a Turing test. In fact, the Allen Institute for Artificial Intelligence admitted that it was deliberately testing its AI in a different way when it set out to develop it in 2016.

The explanation was given in an article entitled, “Moving Beyond the Turing Test with the Allen AI Science Challenge. Admitting that the test would not be “a full test of machine intelligence,” it still considered worthwhile for its showing “several capabilities strongly associated with intelligence – capabilities that our machines need if they are to reliably perform the smart activities we desire of them in the future – including language understanding, reasoning, and use of commonsense knowledge.”

There’s also the practical consideration that makes testing with ready-made tests so appealing: “In addition, from a practical point of view, exams are accessible, measurable, understandable, and compelling.” Come to think of it, that’s why some educators love having standardized tests, while others decry them for the very fact that they give the false impression they are measuring intelligence when all they can measure is performance of a very specific nature.

When it comes to more creative intelligence in which the answer is not simply out there to be found or even intuited, AI still has quite a way to go. We can see that in its attempts to create a script.

Making movies with AI
Benjamin (formerly known as Jetson) is the self-chosen name of “the world’s first automated screenwriter.” The screenwriter known as Benjamin is “a self-improving LSTM RNN [Long short-term memory recurrent neural network] machine intelligence trained on human screenplays.

Benjamin has his/its own Facebook page, Benjamin also used to have a site under that name, but now he/it shares the credit on a more generally named one,, which offers links to all three of the films based on scripts generated by AI that were made within just two days to qualify for the Sci-Fi London’s 48hr Film Challenge.

Benjamin’s first foray into film was the script for “Sunspring.” However, even that required a bit of prompting from Ross Goodwin, “creative technologist, artist, hacker, data scientist,” as well as the work of the filmmaker Oscar Sharp, and three human actors.

The film was posted to YouTube, and you can see it in its entirety by sitting through the entire 9 minutes. See if you share the assessment expressed by the writer Neil Gaiman whose tweet appears on the Benjamin site: “Watch a short SF film gloriously fail the Turing Test.”

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15 Sep 2019
You Can Now Prove a Whole Blockchain With One Math Problem – Really

You Can Now Prove a Whole Blockchain With One Math Problem – Really

The Electric Coin Company (ECC) says it discovered a new way to scale blockchains with “recursive proof composition,” a proof to verify the entirety of a blockchain in one function. For the ECC and zcash, the new project, Halo, may hold the key to privacy at scale.

A privacy coin based on zero-knowledge proofs, referred to as zk-SNARKs, zcash’s current underlying protocol relies on “trusted setups.” These mathematical parameters were used twice in zcash’s short history: upon its launch in 2016 and first large protocol change, Sapling, in 2018.

Zcash masks transations through zk-SNARKs but the creation of initial parameters remains an issue. By not destroying a transaction’s mathematical foundation – the trusted setup – the holder can produce forged zcash.

Moreover, the elaborate ‘ceremonies‘ the zcash community undergoes to create the trusted setups are expensive and a weak point for the entire system. The reliance on trusted setups with zk-SNARKs was well known even before zcash’s debut in 2016. While other research failed to close the gap, recursive proofs make trusted setups a thing of the past, the ECC claims.

Bowe’s Halo
Speaking with CoinDesk, ECC engineer and Halo inventor Sean Bowe said recursive proof composition is the result of years of labor – by him and others – and months of personal frustration. In fact, he almost gave up three separate times.

Bowe began working for the ECC after his interest in zk-SNARKs was noticed by ECC CEO and zcash co-founder Zooko Wilcox in 2015. After helping launch zcash and its first significant protocol change with Sapling, Bowe moved to full-time research with the company.

Before Halo, Bowe worked on a different zk-SNARK variant, Sonic, requiring only one trusted setup.

For most cypherpunks, that’s one too many.

“People we are also starting to think as far back as 2008, we should be able to have proofs that can verify other proofs, what we call recursive proof composition. This happened in 2014,” Bowe told CoinDesk.

Proofs, proofs and more proofs
In essence, Bowe and Co. discovered a new method of proving the validity of transactions, while masked, by compressing computational data to the bare minimum. As the ECC paper puts it, “proofs that are capable of verifying other instances of themselves.”

Blockchain transaction such as bitcoin and zcash are based on elliptic curves with points on the curve serving as the basis for the public and private keys. The public address can be thought of the curve: we know what the elliptic curve looks like in general. What we do not know is where the private addresses are which reside on the curve.

It is the function of zk-SNARKs to communicate about private addresses and transactions–if an address exists and where it exists on the curve–anonymously.

Bowe’s work is similar to bulletproofs, another zk-SNARK that requires no trusted setup. “What you should think of when you think of Halo is like recursive bulletproofs,” Bowe said.

From a technical standpoint, bulletproofs rely on the “inner product argument,” which relays certain information about the curves to one another. Unfortunately, the argument is both very expensive and time consuming compared to your typical zk-SNARK verification.

By proving multiple zk-SNARKs with one–a task thought impossible until Bowe’s research–computational energy is pruned to a fraction of the cost.

“People have been thinking of bulletproofs on top of bulletproofs. The problem the bulletproof verifier is extremely expensive because of the inner product argument,” Bowe said. “I don’t use bulletproofs exactly, I use a previous idea bulletproofs are built on.”

In fact, Bowe said recursive proofs mean you can prove the entirety of the bitcoin blockchain in less space than a bitcoin blockhead takes – 80-bytes of data.

The future of zcash
Writing on Twitter, Wilcox said his company is currently studying the Halo implementation as a Layer 1 solution on zcash.

Layer 1 solutions are implementations into the codebase constituting a blockchain. Most scaling solutions, like bitcoin’s Lightning Network, are Layer 2 solutions built on top of a blockchain’s state. The ECC’s interest in turning Halo into a Layer 1 solution speaks to the originality of the discovery as it will reside next to code copied from bitcoin’s creator himself, Satoshi Nakamoto.

ECC is exploring the use of Halo for Zcash to both eliminate trusted setup and to scale Zcash at Layer 1 using nested proof composition.

— zooko (@zooko) September 10, 2019

Since the early days of privacy coins, scaling has been a contentious issue: with so much data needed to mask transactions, how do you grow a global network?

Bowe and the ECC claim recursive proofs solve this dilemma: with only one proof needed to verify an entire blockchain, data concerns could be a thing of the past:

“Privacy and scalability are two different concepts, but they come together nicely here. About 5 years ago, academics were working on recursive snarks, a proof that could verify itself or another proof [and even] verify multiple proofs. So, what [recursive proof composition] means is you only need one proof to verify an entire blockchain.”

To be sure, this isn’t sophomore-level algebra: Bowe told CoinDesk the proof alone took close to nine months of glueing various pieces together.

A new way to node
A further implication of recursive proofs is the amount of data stored on the blockchain. Since the entire ledger can be verified in one function, onboarding new nodes will be easier than ever, Bowe said.

“You’re going to see blockchains that have much higher capacity because you don’t have to communicate the entire history in one. The state chain still needs to be seen. But if you want to entire the network you don’t need to download the entire blockchain.”

While state chains still need to be monitored for basic transaction verification, syncing the entire history of a blockchain–over 400 GB and 200 GB for ethereum and bitcoin respectively–becomes a redundancy.

For zcash, Halo means easier hard forks. Without trusted setups, ECC research claims, “proofs of state changes need only reference the latest proof, allowing old history to be discarded forever.”

When asked where his discovery ranks with other advancements, Bowe spoke on its practicality:

“Where does this stand in the grand scheme of things in cryptocurrency? It’s a cryptographic tool to compress computation… and scale protocols.”


14 Sep 2019
How AI can save the retail industry

How AI can save the retail industry

Brick and mortar stores are closing left and right, but artificial intelligence may be able to keep them alive.

The future of retail continues looking grim, as more brick and mortar stores close their doors. US retailers have announced 8,558 store closures so far this year, with total US store closures predicted to hit 12,000 by the end of 2019, reported Coresight Research on Friday.

While the internet and automation are typically to blame for these closures, the same technology could actaaually be the solution for physical store locations, said Paul Winsor, general manager of retail at DataRobot.

“If retailers want to stay open in the existing stores that they are operating in, my recommendation to them is to ask: Are they understanding the changing habits of those customers, and how they’re shopping with them, in those locations?” Winsor said.

“To survive in the tough, tough retail market, you have to start to turn your business, and make predictions, based on learning from your historical data,” he added. “It’s all about learning from your historical data.”

After being in the retail industry for more than 30 years, Winsor said that artificial intelligence (AI) and machine learning are tools retailers must use to get ahead—and to stay open.

How businesses stayed open in the past
“Data driven retail is not new. Technology has been around to help companies understand their business from a data perspective before,” Winsor said. “The data just hasn’t been as individual and accurate, as the way that machine learning can help you do that.”

To make predictions in the past, retailers would simply look at daily and weekly transactional data and draw conclusions from that, Winsor said.

As technology evolved and convenience took priority, online stores became the primary way to shop. Since technology took over the shopping experience, it also took over the way retailers draw conclusions and predictions about their services. If retailers refuse to advance and adapt to an evolving retail infrastructure, they will inevitably be left behind.

The three ways AI helps retailers
“With AI, we’re dealing with machines that can simulate intelligent behavior or imitate intelligent human behavior, i.e. sense, reason, act and adapt,” said Brian Solis, principal analyst at Altimeter. “One of the most popular ways leading brands are using AI today is through machine learning.”

“The difference is that with machine learning, systems can recognize patterns from clean data sets, and with proper management, learn from that data to assess and even predict outcomes and improve performance over time,” Solis added. “This helps retailers learn how to personalize engagement, offers and next best action, as well as guide product and service development.”

1. Understanding the customer

Machine learning helps retailers understand their customers and predict future behaviors, Winsor said.

“We want to be more convenient in the way that we shop and we want to be, we want more convenience, and we want to shop across multiple channels,” Winsor said. “We know, as consumers ourselves, that we are constantly changing our habits and therefore what machine learning and AI is doing in this space.”

2. Forecasting

“The really impactful part is around forecasting,” Winsor noted. “We are now seeing retailers using AI and automated machine learning to operate their demand forecasting to understand the actual quantity needed today based on the demand from the customers.”

Not only will this increase accuracy, but it increases operational efficiency, saving both time and money for the organization.

“It’s going to really increase your accuracy because you’re taking in, you’re learning from the past and you’re predicting what that quantity needs to be in the future,” Winsor said. “Operational efficiency is absolutely key, because we’re talking about an industry that is operating its business on very low operating margins.”

3. Streamlining product supply and development

Machine learning and AI can play a significant role in determining a retailer’s supply and development plans.
Some questions machine learning can answer, according to Winsor, include: Are retailers selling the right products today based on the customer’s demands and expectations? And are they priced at the right level? And are they the right products in the right assortment in the right stores—in the right location?

Future of AI in retail
The future of retail is more automated and more individualized, Solis said. “Consumer choice will become less chaotic and stressful.”

“The more promising and realistic future scenarios include screens, connected dressing rooms, and virtual racks that are tailored to me based on my personal, data-defined persona,” Solis said. “It only shares things I would consider based on previous history, and also coming trends, aligned with individual preferences. You could play that scenario out in a multitude of retail sectors, i.e. automotive, appliances, etc.”


12 Sep 2019
3 Blockchain Improvements That Will Lead to Its Mainstream Adoption

3 Blockchain Improvements That Will Lead to Its Mainstream Adoption

Amazon, Walmart, Facebook, IBM: These are among the household names using blockchain to change business. In fact, according to a recent Deloitte survey, more than half of companies say blockchain is a critical priority for their organization. And 83 percent say there’s a compelling business case for the innovation.

Distributed ledger technology (DLT) has arrived, but the question is whether it’s scalable to match the marketplace’s high expectations. Consumers want real-time transactions while having assurance of security from validating nodes. Businesses also want high throughput for their global operations, with zero downtime. But it doesn’t always work that way, especially with new tech. There are glitches, bugs and design flaws. Moreover, regulators often force organizations (or networks) to change how they operate.

Here are three critical features that a blockchain network must have to achieve wide adoption.

Fast speed is essential to help enterprises in practical, day-to-day use. MetaHash is a network based on blockchain 4.0 technology that’s applicable to a wide variety of industries. Aiming to supplant traditional infrastructure, it’s one of the fastest such networks in the world.

In the past, even the largest networks ground to a halt when they saw spikes in traffic. Slow speed leads to frustrations and some users returning to traditional solutions. When there’s a ton of traffic, it’s not uncommon for Bitcoin settlements to take longer than 30 minutes. Two years ago, the CryptoKitties game went viral and clogged the Ethereum network, where MetaHash processes 50,000 transactions per second and validates settlements in three seconds or less.

To an extent, blockchain is already seeing large-scale adoption from global enterprises. Walmart uses it to track food from farm-to-plate to ensure safe products. Facebook is rolling out Libra, a digital token, and pharmaceutical giant Merck is creating a pilot program to fight counterfeit drugs. But at this stage, those DLT initiatives are still new and experimental.

What happens when immutably stored data accumulates to huge files? Can a network sustain its performance when a ton of viewers (business partners, regulators and other stakeholders) join? In the future, experts think some DLT systems will see performance issues from what’s called “blockchain bloat.” It occurs when trillions (or more) of blocks are permanently stored on-chain, causing wallets to take weeks to download and causing a system to become less reliable.

Sharding is a proposed solution, but many systems, including Ethereum, have been unsuccessful at implementing it. It partitions databases so that individual “shards” store different chunks of data. As blockchain matures, sharding will become more critical as the amount of permanently stored data increases to gargantuan sizes.

Combining DLT with other innovations, such as artificial intelligence (AI), will also help to bring mainstream adoption. AI-assisted tech is being embedded in mobile, smart home devices, vehicles and wearables, and blockchain can be used to store all the captured data. One example is DeepCloud AI, which is using both blockchain and AI to decentralize cloud computing (DCC). The DCC platform enables businesses and individuals to monetize idle storage and computing capacity. AI can match nearby computing resources with real-time customer demand. This approach improves speed, reduces cost and prevents clogged networks.

Last but not least is privacy. Data security has received national attention due to breaches at Equifax, Yahoo! and Marriott, as well as congressional hearings on Facebook and Google selling personal data of millions of users. Blockchain’s selling points include privacy, data security and an anonymity network. This is all fairly compelling given strong user preference for privacy, as well as governmental inability to hold big tech accountable for selling everyone’s data.

To gain mainstream adoption, networks must feature significantly favorable economics. Financial institutions have invested a fortune in their infrastructure. Blockchains can make a compelling case that makes consumers want to switch by making transactions extremely inexpensive and fast in comparison. Cost efficiency, data security, smart contracts, frictionless cross border settlements — these are very real advantages. Due to growing adoption and scale of use, blockchain ventures must solve scalability issues to eventually achieve mainstream adoption.