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The Lightning Network is now on the Mainnet, and although the protocol should still be used with caution as the protocol and security testing is ongoing, now is a great time to familiarise yourself with its inner workings and how early adopters are using it. I will outline some test applications - including buying a digital test coffee — and how to deploy your own Lightning node to start supporting your own Lightning payment channels.
The Lightning protocol was first introduced in , but has only recently been operational on the Mainnet, having been deployed in December We have seen some early adoption of the protocol, with operational testnet nodes at the time of this writing visit https: We can also browse a directory of test Lightning services to familiarise ourselves with some use cases, which include VPS services, podcasts, and mobile phone top-up.
Another example is Starblocks , who are selling digital test coffees to showcase the technology. The network is indeed being tested, suggesting developers do want Bitcoin to be more than a Digital Gold. If you are a developer, and you would like to spin up your own Lightning node, then Lightning. Obtaining tBTC for Lightning testing is also available. The Lightning Network opens up many possibilities for the expansion of Bitcoin, including cross chain transactions.
A Forbes article recently pointed this out, giving the reader an overview of the Lightning Network and some prospects for its future. Blockchain is a rapidly evolving technology, and the Lightning Network is no exception. These are the first use cases, and we are expecting a lot more as the final release of the Lightning Network nears closer.
Super intelligence is a term used for AI intelligence considerably smarter than of a human. This level of AI is generally termed as ASI, artificial super intelligence - and we are heading there at a quick pace. This intelligence matches that of a human. Elon Musk predicts we will reach this level in the next decade, with a general consensus we will reach this in the next 5 - 50 years.
However given growth projections, the AI will not stay here long, and continue its evolution to ASI, which would take a matter of days or weeks after breaking the AGI threshold. One of the answers to this is to become part of the AI with devices that communicate directly with our memory and consciousness, enhancing our perceptions that will connect us with a singular super intelligence entity.
We do not want super intelligence to exclusively be owned by one or a select few entities. At this moment Google are the most likely candidate to be the first to break into AGI. One party in control of intelligence can utilise it to force their agendas, whether they deem them positive or negative. There are undoubtedly other players developing intelligence behind closed doors. Given the secretive nature of competitive technology, we are unlikely to be exposed to breakthroughs until patents are filed.
However we are also seeing huge AI interest in the open source community. It is indeed possible to utilise Blockchain technology to distribute trusted data for all to use in training their AI, and startups are already experimenting with these capabilities. Blockchain scalability solutions will ultimately unlock the door for big data on Blockchain.
When this happens, huge libraries of trusted, verified data will be accessible to anyone with the desire. Ethereum is a strong contender to support such a system, having conducted over 4 years of research into scaling their Blockchain whilst maintaining decentralisation. We will begin to see this research being applied to the Blockchain later this year. We see a solution of smart contracts autonomously managing datasets, and connecting to other distributed systems to access such data in exchange for tokens to process the request.
Utilising resources over a decentralised distribution is an alternative solution to centralised tech giants running huge server farms in their own facilities. We have already witnessed that people from around the globe do want a decentralised form of monetary value, taking into consideration the huge growth of Cryptocurrencies in their infancy form. Throughout we saw some huge ICOs and decentralised application ideas being introduced to the market, paving the way for smart contract controlled applications, eliminating the need of a trusted entity to make decisions.
Beyond this, we have seen the initial adoption of IPFS in its early phases, which is proving to be a vital piece of the puzzle of decentralisation. IPFS allows the storage of public data on a distributed network. By launching your own IPFS node, you can store data such as images, video and text that will be hashed and stored in small data chunks on your node, and distributed accordingly.
With the understanding that you can store your static public files in a decentralised manor, this solves another problem on the road to decentralisation. IPFS has versioning of every file it hosts, therefore a history of changes can be logged for your application. Upon exploring storage of secure data in a decentralised manor, we can conclude that these are challenging problems to overcome.
We have seen Blockchain startups such as EOS attempting to tailor a full solution to this problem. Both of these services adopt a pay to use approach, trading tokens for database querying. The cost of querying will depend on the token price, which will be fully determined when the products are publicly released.
A proof of concept called Mango https: All Git objects metadata and data are stored on IPFS , while an Ethereum smart contract provides means for access control and stores the pointers to the latest repository revisions. Having access to a project from a distributed network, we can now download the project onto a local machine, either in a compiled or uncompiled state or both. Having light weight nodes of various distributed networks and Blockchains pre-installed on an OS may be a viable solution in a world where decentralised applications are dominant, eliminating the single point of failure problem.
ISPs are centralised entities that provide internet access. Although the internet as we know it today relies on ISPs, there is a growing interest in mesh networking and P2P networks to provide an alternative means of accessing data across geographies.
With mesh networks, however, all devices are connected with wireless signals wired connections are also possible but considered impractical due to the sheer bulk of wires required. Although mesh networks have been used in both military and emergency relief efforts since the 70s, they have only recently become viable for civilian use as the cost of hardware has decreased.
Beyond hardware, the easy access to renewable energy systems makes powering these networks possible without the reliance on centralised energy companies; with solar energy now being cheaper than coal, it is very economically incentivising to adopt such solutions. As technical boundaries continue to be lifted, the road to a fully decentralised online economy is truly underway.
How will your company transition to such an economy? Now is an interesting time as companies are merging the two worlds into their products, realising that a full Web3. But they inevitably will be, and many have already realised this.
Bridges are being built to compensate for the lack of decentralised completeness. In a market where these Web2. This may not appear obvious in the market of today where the majority of applications with a Blockchain layer amongst their stack are built on a centralised platform.
Furthermore, it is questionable whether the teams behind many of these applications have thought of the process they will have to undertake to remove this centralised component of their product - the platform.
If Facebook introduced decentralised elements to their platform, this would characterise it as a Web2. They could do this in a few ways:.
Hence merging them with a Web2. This is the key element that you cannot integrate into a centralised platform, and what ultimately will define a Web3. Because concretely, the community is shaping the application, and no one owns it.
For this scenario to be possible, the following need to happen:. The stark contrast between a closed, secretive competitive organisation and an open, decentralised collaborative organisation is extremely apparent, with somewhat opposite interests at heart. In order to create a Web3. If you are investing in an ICO or any decentralised application business, check whether the team have the capabilities to take the product from a partly decentralised product to a fully decentralised product.
The differences between Web2. How many will successfully make the leap? This remains to be seen. In deep learning, one of the tradeoffs we consider when developing algorithms is that of precision and recall. Precision and recall is a simple yet useful way to measure the quality of predictions. In order to do this, my model takes in thousands of market features and outputs a probability of the price increasing, this value being my h x value, or the result of hypothesis given x.
Traders are using my predictions towards their strategies, so it is very important I give them reliable data. Furthermore, I may wish to introduce bias to my hypotheses to suit both bullish traders and bearish traders. In this case, I have predicted a false negative result. The predicted class was negative price decreasing , however the actual prediction was positive, as the price actually increased. Using this terminology, we can break our results into 4 categories: Both precision and recall output a value between 0 and 1.
What we would like is a high precision and a high recall, but this is very rarely the case. By testing a range of models and plotting their precision and recall values, their curves will give you an idea of the ideal tradeoff you should be aiming for.
One way to tell if our algorithm is biased towards a positive class is if we have a very low precision, but a very high recall.
For example, if we are in a bullish market where the price continuously increases on a daily basis, I could just return 1 without processing my neural networks , and get a better precision than if I used my prediction model!
This leads us onto the final piece of our quality testing, where we use our precision and recall values to calculate an F Score - a score between 0 and 1 to measure how effective our algorithms are. The term F Score, also referred to as F subscript 1 score, is just a favoured term the deep learning field have adopted for this calculation, and the most popular implementation is:. This calculation takes into consideration biased hypotheses in the result we generate.
What we are after is a high F score; the closer to 1, the better our hypothesis. This insight contains technical details aimed at developers of ICO websites. I will outline methods to speed up your web app, for super speedy load times.
We visit a fair few ICO web apps as we research and look for great ideas in Blockchain. Most of them load too slow for our standards. This figure drops somewhat in cases where users have been referred or are already emotionally invested in the product. However, first impressions count, and every second counts where a user can close a tab in a split second upon dissatisfaction.
Do not attempt to carry out all these optimisations in one session. Iterate through them one by one and apply them smartly to your project. How much static content are you serving, and can you serve more of it across your servers? Review how you are distributing your files.