Failures and breakthroughs – exposed, reflected, considered

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Limits of deep learning and way ahead

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Artificial intelligence has reached peak hype. News outlets report that companies have replaced workers with IBM Watson and algorithms are beating doctors at diagnoses. New AI startups pop up every day – especially in China – and claim to solve all your personal and business problems with machine learning.

Ordinary objects like juicers and wifi routers suddenly advertise themselves as “powered by AI”. Not only can smart standing desks remember your height settings, they can also order you lunch.

Much of the AI hubbub is generated by reporters who’ve little or superficial knowledge about the subject matter and startups  hoping to be acquihired for engineering talent despite not solving any real business problems. No wonder there are so many misconceptions about what A.I. can and cannot do.

Deep learning will shape the future ahead

Neural networks were invented in the 60s, but recent boosts in big data and computational power made them actually useful. The results are undeniably incredible. Computers can now recognize objects in images and video and transcribe speech to text better than humans can. Google replaced Google Translate’s architecture with neural networks and now machine translation is also closing in on human performance.

The practical applications are mind-blowing. Computers can predict crop yield better than the USDA and indeed diagnose cancer more accurately than expert physicians.

DARPA, the creator of Internet and many other modern technologies, sees three waves of AI:

  1. Handcrafted knowledge, or expert systems like IBM’s DeepBlue or IBM Watson;
  2. Statistical learning, which includes machine learning and deep learning;
  3. Contextual adaption, which involves constructing reliable, explanatory models for real world phenomena using sparse data, like humans do.

As part of the current second wave of AI, deep learning algorithms work well because of what the report calls the “manifold hypothesis.” This refers to how different types of high-dimensional natural data tend to clump and be shaped differently when visualised in lower dimensions.

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By mathematically manipulating and separating data clumps, deep neural networks can distinguish different data types. While neural networks can achieve nuanced classification and predication capabilities they are what is called “spreadsheets on steroids.”

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Deep learning algorithms have deep learning problems

At the recent AI By The Bay conference, one expert and inventor of widely used deep learning library Keras,  Francois Chollet, thinks that deep learning is simply more powerful pattern recognition vs. previous statistical and machine learning methods and that the most important problems for AI today are abstraction and reasoning. Current supervised perception and reinforcement learning algorithms require lots of data, are terrible at planning, and are only doing straightforward pattern recognition.

By contrast, humans “learn from very few examples, can do very long-term planning, and are capable of forming abstract models of a situation and manipulate these models to achieve extreme generalisation.”

Even simple human behaviours are laborious to teach to a deep learning algorithm. Let’s examine the task of not being hit by a car as you walk down the road.

Humans only need to be told once to avoid cars. We’re equipped with the ability to generalise from just a few examples and are capable of imagining (i.e. modelling) the dire consequences of being run over. Without losing life or limb, most of us quickly learn to avoid being overrun by motor vehicles.

Let’s now see how this works out if we train a computer. If you go the supervised learning route, you need big data sets of car situations with clearly labeled actions to take, such as “stop” or “move”. Then you’d need to train a neural network to learn the mapping between the situation and the appropriate action. If you go the reinforcement learning route, where you give an algorithm a goal and let it independently determine the ideal actions to take, the computer will “die” many times before learning to avoid cars in different situations.

While neural networks achieve statistically impressive results across large sample sizes, they are “individually unreliable” and often make mistakes humans would never make, such as classify a toothbrush as a baseball bat.

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Your results are only as good as your data

Neural networks fed inaccurate or incomplete data will simply produce the wrong results. The outcomes can be both embarrassing and damaging. In two major PR debacles, Google Images incorrectly classified African Americans as gorillas, while Microsoft’s Tay learned to spew racist, misogynistic hate speech after only hours training on Twitter.

Undesirable biases may even be implicit in our input data. Google’s massive Word2Vec embeddings are built off of 3 million words from Google News.  The data set makes associations such as “father is to doctor as mother is to nurse” which reflect gender bias in our language.

For example, researchers go to human ratings on Mechanical Turk to perform “hard de-biasing” to undo the associations. Such tactics are essential since word embeddings not only reflect stereotypes but can also amplify them. If the term “doctor” is more associated with men than women, then an algorithm might prioritise male job applicants over female job applicants for open physician positions.

Neural networks can be tricked or exploited

Ian Goodfellow, inventor of GANsshowed that neural networks can be deliberately tricked with adversarial examples. By mathematically manipulating an image in a way that is undetectable to the human eye, sophisticated attackers can trick neural networks into grossly misclassifying objects.

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The dangers such adversarial attacks pose to AI systems are alarming, especially since adversarial images and original images seem identical to us. Self-driving cars could be hijacked with seemingly innocuous signage and secure systems could be compromised by data that initially appears normal.

Potential solutions

How can we overcome the limitations of deep learning and proceed towards general artificial intelligence? Chollet’s initial plan is using “super-human pattern recognition like deep learning to augment explicit search and formal systems”, starting with the field of mathematical proofs. Automated Theorem Provers (ATPs) typically use brute force search and quickly hit combinatorial explosions in practical use. In the DeepMath project, Chollet and his colleagues used deep learning to assist the proof search process, simulating a mathematician’s intuitions about what lemmas might be relevant.

Another approach is to develop more explainable models. In handwriting recognition, neural nets currently need to be trained on many thousand examples to perform decent classification. Instead of looking at just pixels, generative models can be taught the strokes behind any given character and use this physical construction information to disambiguate between similar numbers, such as a 9 or a 4.

Yann LeCun, AI boss of Facebook, proposes “energy-based models” as a method of overcoming limits in deep learning. Typically, a neural network is trained to produce a single output, such as an image label or sentence translation. LeCun’s energy-based models instead give an entire set of possible outputs, such as the many ways a sentence could be translated, along with scores for each configuration.

Geoffrey Hinton, called the “father of deep learning” wants to replace neurons in neural networks with “capsules” which he believes more accurately reflect the cortical structure in the human mind. Evolution must have found an efficient way to adapt features that are early in a sensory pathway so that they are more helpful to features that are several stages later in the pathway. He thinks capsule-based neural network architectures will be more resistant to the adversarial attacks.

Perhaps all of these approaches to overcoming the limits of deep learning have a value. Perhaps none of them do. Only time and continued investment in AI will tell. But one thing seems quite certain: it might be impossible to achieve general intelligence simply by scaling up today’s deep learning techniques.

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Survival of blockchain and Ethereum vs. alternatives

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As outlined in my previous post, blockchain faces number of fundamental – technological, cultural, and business – issues before it becomes mainstream. However, potential of blockchain, especially if it were coupled with AI, cannot be ignored. The potent combination of blockchain and AI  can revolutionise healthcare, science, government, autonomous driving, financial services, and a number of key industries.

Discussions continue about blockchain’s ability to lift people out of poverty through mobile transactions, improve accounting for tourism in second-world countries, and make governance transparent with electronic voting. But, just like the complementary – and equally hyped – technologies of AI, IoT, and big data, blockchain technology is emerging and yet unproven at scale. Additional, socio-political as well as economic roadblocks remain to blockchain’s widespread adoption and application:

1. Disparity of computer power and electricity distribution

Bitcoin transactions on blockchain require “half the energy consumption of Ireland”. This surge of electricity use is simply impossible in developing countries where the resource is scarce and expensive. Even if richer countries assist and invest in poorer ones, the UN is concerned that elite, external ownership of critical infrastructure may lead to a digital form of neo-colonialism.

2. No mainstream trust for blockchain

Bitcoin inspired the explosive attention on blockchain, but there isn’t currently much trust in the technology outside of digital currencies. With technologies still in their infancy, blockchain companies are slow to deliver on promises. This turtle pace does not satisfy investors seeking quick ROI. Perhaps the largest, challenge to blockchain adoption is the massive transformation in architectural, regulatory, and business management practices required to deploy the technology at scale. Even if such large-scale changes are pulled off, society may experience a culture shock from switching to decentralised, automated systems after a history of only centralised ones.

3. Misleading and misguided investments

Like the internet, blockchain technology is most powerful when everyone is on the same network. The Internet grew in fits and starts, but was ultimately driven by the killer app of email. While Bitcoin and digital currencies are the “killer app” of blockchain, we’ve already seen aggressive investments in derivative cryptocurrencies peter out.

Many technologies also call themselves “blockchain” to capitalise on hype and capture investment, but are not actual blockchain implementations. But, even legitimate blockchain technologies suffer from the challenge of timing, often launching in a premature ecosystem unable to support adoption and growth.

4. Cybersecurity risks and flaws

The operational risks of cybersecurity threats to blockchain technology make early adopters hesitate to engage. Additionally, bugs in the technology are challenging to detect, yet caused outsized damage. Getting the code right is critical, but this requires time and talent.

While relatively more known Bitcoin’s PoW-based blockchain systems and Ethereum see limelight and PR, there are number of alternative blockchain protocols and approaches, which are scalable and solve many of fundamental challenges the incumbents face.

PoW and Ethereum alternatives

1. BitShares, SteemIt (based on Steem) and EOS white papers which are all based on Delegated Proof of Stake (DPOS). DPOS enables BitShares to process 180k transactions per second, which is more than 5x NASDAQ transactions/s. Steem and Bitshares process more transactions/day than the top 20 blockchains combined.

In DPOS, each 2 seconds – Bitcoin’s PoW generates a new block each 10 minutes – a new block is created, through witnesses (stakeholders can elect any number of witnesses to generate blocks – currently 21 in Steem and 25 in BitShares). DPOS is using pipelining to increase scalability. Those 20 witnesses generate their own block in a specified order, that holds for a few rounds (hence the pipelining), after the order is changed. DPOS confirms transactions with 99.9% certainty in an average of just 1.5 seconds while degrading in a graceful, detectable manner that is trivial to recover from. It is easy to increase the scalability of this schema, by introducing additional witnesses either by increasing the pipeline length or using sharding to allow to generate in a deterministic/verifiable way few blocks during the same epoch.

2. IOTA (originally designed to be financial system for IoT) is a new blockless distributed ledger which is scalable, lightweight and fee-less. It’s based on DAG, and its performance INCREASES the bigger the networks gets.

3. Ardor solves the common (to all blockchains) bloat problem, relying on an innovative parent/child chain architecture and pruning of the child chain transactions. It shares some similarities with plasma.io, based on NXT blockchain technology and already running on testnet.

4. LTCP uses State Channels by stripping 90% of the transaction data from the blockchain. LTCP combined with RSK’s Lumino network or Ethereum’s Raiden network can serve 1 billion users in both retail and online payments.

5. Stellar runs off of Stellar Consensus Protocol (SCP) and is scalable, robust, got a distributed exchange and is easy to use. SCP implements “Federated Byzantine Agreement,” a new approach to achieving consensus in a real-world network that includes faulty “Byzantine” nodes with technical errors or malicious intent. To tolerate Byzantine failures, SCP is designed not to require unanimous consent from the complete set of nodes for the system to reach agreement, and to tolerate nodes that lie or send incorrect messages. In the SCP, individual nodes decide which other participants they trust for information, and partially validate transactions based on individual “quorum slices.” The systemwide quorums for valid transactions result from the individual quorum decisions by individual nodes.

6. A thin client is a program which connects to the Bitcoin network but which doesn’t fully validate transactions or blocks, i.e it’s a client to the full nodes on the network. Most thin clients use the Simplified Payment Verification (SPV) method to verify that confirmed transactions are part of a block. To do this, they connect to a full node on the blockchain network and send it a filter (Bloom filter) that will match any transactions affecting the client’s wallet. When a new block is created, the client requests a special lightweight version of that block: Merkle block, which includes a block header, a relatively small number of hashes, a list of one-bit flags, and a transaction count. Using this information—often less than 1 KB of data—the client can build a partial Merkle tree to the block header. If the hash of the root node of the partial Merkle tree equals the hash of Merkle root in the block header, the SPV client has cryptographic proof that the transaction was included in that block. If that block then gets 6 confirmations at the current network difficulty, then the client has extremely strong proof that the transaction was valid and is accepted by the entire network.

The only major downside of the SPV method is that full nodes can simply not tell the thin clients about transactions, making it look like the client hasn’t received bitcoins or that a transaction the client broadcast earlier hasn’t confirmed.

7. Mimir proposes a network of Proof of Authority micro-channels for using in generating a trustless, auditable, and secure bridge between Ethereum and the Internet. This system aims to establish Proof of Authority for individual validators via a Proof-of-Stake contract registry located on Ethereum itself . This Proof-of-Stake contract takes stake in the form of Mimir B2i Tokens. These tokens serve as collateral that may be repossessed in the event of malicious actions. In exchange for serving requests against the Ethereum blockchain, validators get paid in Ether.

8. Ripple’s XRP ledger already handles 1,500 transactions/second on-chain, which keeps on being improved (was 1,000 transactions/sec at the beginning of 2017).

9. QTUM, a hybrid blockchain platform whose technology combines a fork of bitcoin core, an Account Abstraction Layer allowing for multiple Virtual Machines including the Ethereum Virtual Machine (EVM) and Proof-of-Stake consensus aimed at tackling industry use cases.

10. Blocko, which has enterprise and consumer grade layers and has already successfully piloted/launched products (dApps) with/for Korea Exchange, LotteCard and Huyndai.

 

Bitcoin and blockchain demystified: basics and challenges

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Bitcoin, blockchain, Ethereum, gas, …

A new breed of snake oil purveyors are peddling “blockchain” as the magic sauce that will power all the world’s financial transactions and unlock the great decentralised database in the sky. But what exactly are bitcoin and blockchain?

Bitcoin is a system for electronic transactions that don’t rely on a centralised or trusted third-party (bank or financial institution). Its creation was motivated by the fact that digital currency made of digital signatures, while providing strong ownership control, was viable but incomplete solution unable to prevent double-spending. Bitcoin’s proposed solution was a peer-to-peer network using proof-of-work (system to deter network attacks) to record a public history of transactions that is computationally impractical for an attacker to change if honest nodes control a majority of CPU power. The network is unstructured, and its nodes work with little coordination and don’t need to be identified. Truth/consensus is achieved by CPU voting, i.e network CPUs express their acceptance of valid blocks (of transactions) by working on extending them and rejecting invalid blocks by refusing to work on them.

Satoshi Nakamoto’s seminal paper “Bitcoin: A Peer-To-Peer Electronic Cash System” has references to a “proof-of-work chain”,“coin as a chain,” “chain of ownership”, but no “blockchain” or “block chain” ever make an appearance in it.

Blockchain (which powers Bitcoin, Ethereum and other such systems) is a way for one Internet user to transfer a unique piece of digital asset (Bitcoins, Ether or other crypto assets) to another Internet user, such that the transfer is guaranteed to be safe and secure, everyone knows the transfer has taken place, and nobody can challenge the legitimacy of the transfer. Blockchains are essentially distributed ledgers and have three main characteristics: a) decentralisation, b) immutability and c) availability of some sort of digital assets/token in the network.

While decentralisation consensus mechanisms offer critical benefits, such as fault tolerance, a strong guarantee of security, political neutrality, and authenticity, they come at the cost of scalability. The number of transactions the blockchain can process can never exceed that of a single node that is participating in the network. In fact, blockchain actually gets weaker as more nodes are added to its network because of the inter-node latency that logarithmically increases with every additional node.

All public blockchain consensus protocols make the tradeoff between low transaction throughput and high-degree of centralisation. As the size of the blockchain grows, the requirements for storage, bandwidth, and computing power required to fully participating in the network increases. At some point, it becomes unwieldy enough that it’s only feasible for a few nodes to process a block — that might lead to the risk of centralisation.

Currently, the blockchain (and with it, Bitcoin, Ethereum and others) challenges are:

  1. Since every node is not allowed to validate every transaction, we need nodes to have a statistical and economic means to ensure that other blocks (which they are not personally validating) are secure.
  2. Scalability is one of the main challenges. Bitcoin, despite having a theoretical limit of 4,000 transactions per second (TPS) currently has a hard cap of about 7 transactions per second for small transactions and 3 per second for more complex transactions. An Ethereum node’s maximum theoretical transaction processing capacity is over 1,000 TPS but processes between 5-15 TPS. Unfortunately, this is not the actual throughput due to Ethereum’s “gas limit, which is currently around 6.7 million gas on average for each block. Gas is the computation cost within Ethereum, which users pay in order to issue transactions or perform other actions. A higher gas limit means that more actions could be performed per-block. In order to scale, the blockchain protocols must figure out a mechanism to limit the number of participating nodes needed to validate each transaction, without losing the network’s trust that each transaction is valid.
  3. There must be a way to guarantee data availability, i.e. even if a block looks valid from the perspective of a node not directly validating that block, making the data for that block unavailable leads to a situation where no other validator in the network can validate transactions or produce new blocks, and we end up stuck in the current state (reasons a node is offline include malicious attack and power loss).
  4. Transactions need to be processed by different nodes in parallel in order to achieve scalability (one solution is similar to database sharding, which is distribution and parallel processing of data). However, blockchain’s transitioning state has several non-parallelizable (serial) parts, so we’re faced with some restrictions on how we can transition state on the blockchain while balancing both parallelizability and utility.
  5. End-users and organisations (such as banks) have hard time or don’t want to use blockchain. To do even a simple Bitcoin transaction requires a prior and stringent KYC check just to sign up on one of many crypto trading or exchange platforms.  “The Rare Pepe Game is built on a blockchain with virtual goods and characters and more,” explains Fred Wilson of USV. “And it shows how clunky this stuff is for the average person to use.”
  6. There is lot of hype, around blockchain which sets wrong expectations, misleads investments and causes lots of mistakes. Bloomberg reports that Nasdaq is seeking to show progress using the much-hyped blockchain. The Washington Post lists Bitcoin and the blockchain as one of six inventions of magnitude we haven’t seen since the printing press.  Bank of America is allegedly trying to load up on “blockchain” patents. Also, due to its volatility, uncertain status (can it be considered a legal tender such as normal fiat money or is it security, etc?),  there is much instability of holding crypto assets.
  7. Contrary to common belief, disintermediating financial institutions, so the reasoning goes, multiple parties can conduct transactions seamlessly, without paying a commission. However, according to one research, cost savings might be dubious as moving cash equity markets to a blockchain infrastructure would drive a significant increase of the overall transaction cost. Trading on a blockchain system would also be slower (at least in foreseeable future) than traders would tolerate, and mistakes might be irreversible, potentially bringing huge losses.
  8. To drive massive adoption which will induce further technological advancement, a killer app on blockchain or Ethereum would be a must. Despite much invested resources and efforts globally, So far there doesn’t seem to be one, but there arguably is potential in few areas such as digital gold, payments and tokenization.
  9. Blockchain’s immutability might pose a problem for specific types of data. The EU ‘right to be forgotten requires the complete removal of information, which might be impossible on blockchain. There are other privacy-related concerns that people might want to remove or forgotten such as previous insolvency, negative rankings, and other personal details that need to change.

To conclude, I think Ethereum is furthers along compared to PoW-based public blockchains. Ethereum is still orders of magnitude off (250x off being able to run a 10m user app and 25,000x off being able to run Facebook on chain) from being able to support applications with millions of users. If current efforts are well executed, Ethereum could be ready for a 1–10m user app by the end of 2018.

However, there are less-known alternative models that are much more scalable. Once scalability issues are solved, everything will become tokenized and connected by blockchain.

Blockchain + AI = ?

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What happens when two major technological trends see an synergy or overlap in usage or co-development?

We have blockchain’s promise of near-frictionless value exchange and AI’s ability to conduct analysis of massive amounts of data. The joining of the two could mark the beginning of an entirely new paradigm. We can maximize security while remaining immutable by employing AI agents that govern the chain. With more companies and institutions adopting blockchain-based solutions, and more complex, potentially critical data stored in distributed ledgers, there’s a growing need for sophisticated analysis methods, which AI technology can provide.

The combination of AI and blockchain is fueling the onset of the “Fourth Industrial Revolution“ by reinventing economics and information exchange.

1. Precision medicine

Google DeepMind is developing an “auditing system for healthcare data”. Blockchain will enable the system to remain secure and shareable, while AI will allow medical staff to obtain analytics on medical predictions drawn from patient profiles.

2. Wealth and investment management

State Street is issuing blockchain-based indices. Data is stored and made secure using blockchain and analyzed using AI. It reports that 64% of wealth and asset managers polled expected their firms to adopt blockchain in the next five years. Further, 49% of firms said they expect to employ AI. As of 01.2017, State Street had 10 blockchain POC’s in the works.

3. Smart urbanity

To supply the energy, distributed blockchain technology is implemented for transparent and cost-effective transactions between producers and consumers, while machine learning algorithms can even hone in on transactions to estimate pricing. Green-friendly AI and blockchain help reduce energy waste and optimize energy trade. For example, an AI system governing a building can oversee energy use by counting in factors like the presence and number of residents, seasons, and traffic information.

4. Legal diamonds

IBM Watson is developing Everledger using blockchain technology to tackle fraud in the diamond industry, and deploying cognitive analytics to heavily “cross-check” regulations, records, supply-chain, and IoT data in the blockchain environment.

5. More efficient science

The  “file-drawer problem“ in academia is when researchers don’t publish “non-result” experiments. Duplicate experiments and a lack of knowledge follow, trampling scientific discourse. To resolve this, experimental data can be stored in a publicly accessible blockchain. Data analytics could also help identifying elements like how many times the same experiment has happened or what the probable outcome of a certain experiment is.

There are forecasts that AI will play a big role in science once “smart contracts” transacted by blockchain require smarter “nodes” that function in a semi-autonomous way. Smart contracts (essentially, pieces of software) simulate, enforce and manage contractual agreements and can have wide-ranging applications when academics embrace the blockchain for knowledge transfer and development.

6. IP rights management

Digitalization has introduced complicated digital rights to  IP management, and when AI learns the rules of the game, it can identify actors who break IP laws. As for IP contract management, for music (and other content) industry, blockchain enables immediate payment methods to artists and authors. One artist recently suggested the blockchain could help musicians simplify creative collaboration and making money.  Ujo Music is making use of the Ethereum blockchain platform for song distribution.

7. Computational finance

Smart contracts could take center stage where transparent information is crucial for trust in financial services. Financial transactions may no longer rely on a human “clearing agent” as they automatized, performing better and faster. But since confidence in transactions remains dependent on people, AI can help monitor human emotions and predict the most optimal trading environment. Thus, “algotrading” can be powered by algorithms that trade based on investment patterns correlated with emotions.

8. Data and IoT management

Organizations are increasingly looking to adopt blockchain technologies for alternative data storage. And with heaps of data distributed across blockchain ledgers, the need for data analytics with AI is growing. IBM Watson merged blockchain with AI via the Watson IoT group. In this, an artificially intelligent blockchain lets joint parties collectively agree on the state of the device and make decisions on what to do based on language coded into a smart contract. Using blockchain tech, artificially intelligent software solutions are implemented autonomously. Risk management and self-diagnosis are other use cases being explored.

9. Blockchain-As-A-Service software

Microsoft is integrating “BaaS modules” (based on the public Ethereum) in its Azure that users can create test environments for. Blockchains are cheaper to create and test, and in Azure they come with reusable templates and artifacts.

10. Governance 3.0

Blockchain and AI could contribute to the development of direct democracy. They can transfer big hordes of data globally, tracing e-voting procedures and displaying them publicly so that citizens can engage in real-time. Democracy Earth Foundation aspires to “hack democracy“ by advocating open-source software, peer-to-peer networks, and smart contracts. The organization also aims to fight fake identities and reclaim individual accountability in the political sphere. IPDB is a planetary-scale blockchain database built on BigchainDB. It’s a ready-to-use public network with a focus on strong governance.

apple and judiciary of cool

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It was perceivable that “to Google” became a verb synonymous with “to search;” but it might’ve been difficult to forecast a judiciary ruling on the concept of coolness.

This happened unsurprisingly in a judgment involving Apple, which brought the case alleging Samsung’s Galaxy Tab 10 infringed its iPad design. British Judge Birss disagreed saying Apple’s designs weren’t being infringed because Samsung’s product was not as “cool” as the iPad.

This can be watershed in jurisprudence.

Originally, in a 1997 article, Gladwell wrote about the group of people he called coolhunters who scoured American cities to find out what cool kids thought about sneakers.

Written by Hayk

July 14, 2012 at 8:45 pm

Posted in business, innovation, technology

Tagged with , , ,

are you ready for an entrepreneurial jump?

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  • Do you solve a real problem?
  • Do your family/friends support you?
  • Will you sacrifice anything to make your business a success, putting normal life on hold?
  • Are you prepared for a lonely journey?
  • Are you resilient enough to listen to NOs/doubters/haters, but stay headstrong?
  • Do you care about your customers/business as if it’s your baby?
  • Have you got co-founders that share your passion/vision?

Entrepreneurship is living a few years of your life like most people won’t, so that you can spend the rest of your life like most people can’t.

Written by Hayk

October 6, 2011 at 6:45 am

hedge funds and twitter – glimpses of future?

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Since creation of the first hedge fund in 1949, 10.000 funds and $2 trillion past, there is a change under way.

Derwent Capital, with its initial $39 million, is Europe’s first “social media-based hedge fund” founded on findings of this paper, which asserts that Twitter can:

predict daily moves in the Dow Jones … change in emotions expressed online would be followed between two and six days later by a move in the index… predict its movements with 87.6% accuracy.

Still small compared to standard ones, the fund analyzes thousands of tweets for words “happy,” “sad,” “angry,” etc. to determine public sentiment/mood.

Written by Hayk

September 30, 2011 at 6:50 am