How Silicon Valley has become a $3 trillion asset - It all starts from a story!

How did Silicon Valley become a $3 trillion area? 

It all starts with a story - and this narrative starts with Mr and Mrs Leland Stanford 

Early 19th century was farmland 

1885 - Mr and Mrs Leiland Stanford set up Stanford University in memory of their son who died at Harvard. There is a great narrative how they arrived at the Harvard Presidents office in threadbare clothes- and was ignored by the secretary - eventually after a few hours - was “granted an audience” - and offered to erect a memorial in the form of a building . The president , looking at their clothes said - “it’s very expensive you know .... Harvard’s buildings cost $7m - at which Mrs Stanford whispered to her husband “is that all that is needed to set up a University? - and they moved to Palo Alto and set up Stanford!

1930s navy built an aerospace hub - scientists and smart people moved to area 

1939 HP founded there - got government contract making radar and artillery 

1940s - at Bell labs - William Shockley co-invented the transistor. Transistor became computer processor

1956 - Shockley set up silicon labs - employed grads from Stanford 

1958 - 8 employees left to create Fairchild semiconductors - became known as the “traitorous 8” -made computer components for Apollo programme 

1960s - Gordon Moore and Robert Noyce 2 of the 8 founded intel on a one page business plan 

Late 60s - 2 other of the 8 founded Kleiner Perkins 

1969 - government research project with Vint Cerf - that went on to become the internet 

1970 - Xerox set up in Palo Alto

1971 - Don Hoefler set up Silicon Valley Times - and the name Stuck!

Lots of stuff around Stanford research

1980s - Atari, Apple and Oracle founded

1990s - Ebay, yahoo ., Paypal and google

2000 - 2002 - dot com crash - I remember seeing empty building after enoty building!

2003 - 2018  - Facebook , Uber , Twitter and Tesla 

2018 - 2030 - ????

2030 - 2050 - ????

The future of work - towards 2030

This  article is based on a paper by Lee Rainie in 2017 as part of the Annual Meeting of the Global Future Councils

 Alvin Toffler predicted a future in his 1970 bestseller Future Shock that looks much like today’s reality.

He anticipated 

• the rise of the internet, 
• the sharing economy, 
• companies built on “adhocracy” rather than centralized bureaucracy, 
• the broader social confusions and concerns about technology.
•  how the evolving relationship between people and technology would shape how societies and economies develop. 

So where will we be in 2030?

•  Jobs - what will they look like? 
• How will we re educate ourselves?
• What will education look like?
• How will we earn a living?
• Will we need to earn a living?
• How will we communicate?
• Will everything we do be inaliebly linked to the internet?
• Privacy? Will it exist?

So many other questions - what questions do you have?

Can we master greater connectivity

Many are convinced that the internet will be everywhere - or nearly everywhere - in the next generation. It will be "on" most things and built into many objects and environments. 

Experts claim that the internet will fade into the background, becoming like electricity - less visible but deeply embedded in human endeavors. 

Even those without high levels of literacy will interact with digital material and apps using their voice, igniting an unprecedented expansion of knowledge and learning.

The build of AI - will there be mistakes along the way? Who will build it? Will it incorporate values that we can be proud of? 

This explosion of connectivity has bought and will continue to bring infinite new possibilities, but also economic and social vulnerabilities. 

The level of coordination and coding required to stitch the Internet of Things together is orders of magnitude more complicated than any historical endeavour yet. 

It is likely that things will break and no one will know how to fix them. 

Bad actors will be able to achieve societal disruptions at scale and from afar.

 Consequently, we are faced with some hard, costly choices. 

• How much redundancy should these complex systems have?
•  How will they be defended and by whom? 
• How is liability redefined, as objects are networked across a global grid and attacks can metastasize quickly? 

Will we create more meaningful work?

Will AI , IOT and machine learning Be good for humans? Will it create or destroy jobs? Will more valuable jobs replace those supplanted by technology. 

How are we as humans going to react to the technology revolution? 

What jobs will replace those that will be done by machines. 

How will education and skills-training adapt?

Colleges, community colleges and trade schools - models are being disrupted - Teaching is now blended  through online video or hybrid courses which provide both online and classroom experiences. 
Artificial intelligence systems will assess student performance and the sufficiency of the course. 
Employees are also self-training with online material.

What will always be needed is collaboration and human connection.

Heather McGowan and Chris Shipley points out that the  best education programmes will be those teaching how to be a lifelong learner, and that alternative credential systems will arise to assess the new skills people acquire. 

So, what specific human talents will be unable to be duplicated by machines and automation for some time? 

They say 

• social and emotional intelligence, 
• creativity, 
• collaborative activity,
• abstract and systems thinking, 
• complex communication skills, and
• the ability to thrive in diverse environments.

What are schools and universities need to doing to re-orient to emphasise these non-technical skills?

It’s all about Trust!!

• Trust is about reliability, capability and intimacy 
• Trust is not about wiifm 
• Trust is key to the development of a sustainable future 
• Trust is a social, economic and political binding agent. is the glue for economic development and social cohesion. 
• Trust is the lifeblood of friendship and care-giving. 

When trust is absent, all kinds of societal woes unfold, including violence, chaos and paralysing risk-aversion. 

With the proliferation of internet and mass collaboration - has trust been degraded? 

Preferences for convenience, comfort, and information have made people vulnerable to the ways organisations  can identify, target and manipulate them

• Fake news
• Using other people’s info
• Spam
• Preying on needs from data analytics 
• Data theft
• Unlawful data use 

How much can social and organizational innovation alleviate new problems?

There are new ways of doing things

Old corporate structures are old

New ways of collaborating - using tools that can track measure and reward activity 

Some primary aspects of collective action and power are already changing as social networks become a societal force. 
These networks are used for both knowledge-sharing and mobilizing others to action. 
There are new ways for people to collaborate to solve problems. 

BBG is a case in point (www.bbg.business)

New laws and court battles are inevitable and are likely to address questions such as: 

• Who owns what information?
• Who can use and profit from information? 
• When something goes wrong with an information-processing system (say, a self-driving car propels itself off a bridge), who is responsible? 
• Where is the right place to draw the line between data capture - or surveillance - and privacy? 
• What kinds of personal information can be legitimately considered when assessing someone’s employment, creditworthiness or insurance status? 
• Who oversees the algorithms that decide what happens in society? 

There is a long road ahead to 2030. There is a lot of opportunity to make the uncertain more certain. 
We look forward to being a player in this exciting time 

Machine Learning: What’s in It for Business?

Alena Busko, Contributor

Inbound marketer, Softeq

https://www.huffingtonpost.com/entry/machine-learning-whats-in-it-for-business_us_59ad236be4b0c50640cd60a5

Who knew that stats would be the bomb?

How do you take Big Data and convert it into actionable information that would help business thrive?

Who knew that Machine learning and data analysis algorithms will benefit those service providers who have accumulated large data volumes about their clients, enabling analysts and marketers obtain impartial insight into customer behavior: how the client activity changes if the company has modified its service or introduced a new one; whether the existing service offering has a weak spot, which needs fixing; who to target at what time! 

 Here is an example of how the results of a problem-solving session that took place at a datathon was used to make a business decision 

Datathon is a hackathon focused on problem-solving using Machine Learning.

A bank provided client data in an anonymized form. The datathon participants were to analyze the datasets by generating multiple hypotheses and identifying the viable ones. The problem was expected to be solved using cluster analysis, a method of unsupervised learning.

Unsupervised Learning – a machine learning algorithm that teaches the computer system to identify inferences in datasets. They would consist of input data without labeled responses. Cluster analysis helps find hidden patterns or grouping in data based on specific parameters. For instance, this could be segmentation of subscribers of a mobile services provider.

Multiparameter Data as Basis for Hypotheses

The team was to make hypotheses using data with disparate input parameters. They included description of the product or service acquired, the amount paid using the credit cards issued by the bank, and user demographics – age and sex. The majority of the data fell into categories based on high-level payment destinations – shops, gas stations, services, etc. Some of the categories had a more detailed level of description, for instance, AliExpress, Uber, Burger King, iTunes. The major hypothesis made by the team was as follows: if they analyzed the user money spending patterns, they would generate a rather informative user portrait.

Data Processing, Pattern and Correlation Analysis

Processing Unlabeled Data

The team analyzed all payment destinations and identified correlations. The inference – the greater the number of identical occurrences in payment destinations, the tighter is the correlation (a heavier connector weight on the pic)

The team processed the unlabeled data as follows: reduced its dimensionality, performed clustering and correlation analysis. For this, they used such tech and tools as Python, t-SNE, DBSCAN, and Matplotlib. The participants also sanity-checked the data against the real-world parameters. Thus, they identified an outlier in payment destination values that amounted to 16,000 for an Uber ride. When studied closely, the amount turned out to have a foreign currency attribute. Once the team converted the amounts in major currencies to a common currency and screened the rare ones, the data became more informative

Identifying Patterns and Correlations

The team managed to identify several meaningful patterns and interdependencies. The graph analysis and cluster analysis used by the participants demonstrated a correlation among the clients commuting via Uber and those who shop on iTunes. Another cluster located nearby showed that credit card holders with foreign currency accounts are young people who are regulars at local bars and restaurants.

Unbiased Hypotheses Verification

The Datathon participants test-proved that unsupervised learning is a great fit for validating unbiased hypotheses. This method does not aim to identify cause-and-effect relations or achieve stable results, which otherwise may add subjectivity to the data processing results. For instance, the assumption that an average fast-food lover would frequent different fast-food brands did not prove valid during this problem-solving session.

The topic covered is the result of Olga Babik’s contribution.

Olga Babik is a tech blogger and marketing specialist with Softeq, a software company in Houston, TX. Olga closely collaborates with the Softeq engineering team who work on a variety of IoT projects with the focus on big data mining and machine learning processing at the backend. She highlights her colleagues’ first-hand experience and skills in prototyping, devising, integrating, deploying, and supporting connected solutions driven by firmware, software, and hardware.

The Basics Of Bitcoin

Great article by Ezekiel de Jong 

After my recent attendance as a guest speaker at Dubai's Block-chain Innovation & Investment Summit, Hong Kong's Token 2049 & a handful of other amazing events. I found myself being asked by investors & new comers the same questions quite often.

Here are some of the insights and knowledge I have had the pleasure to learn since 2012. Some of this knowledge was learnt the hard way, some was mentored to me by pioneers of this age and some is my own discoveries made over 6 years and counting in the digital currency & block-chain technology sector.

The ultimate ELI5 guide on Bitcoin: how does it works and how we can make a profit from it.

What is Bitcoin?

Imagine a currency that can be minted by anyone, using a mechanism that provides a reliable way to control your production and assures you’re not minting counterfeit coins.

Imagine a currency that can be used worldwide, without any restrictions, and it’s worth the same regardless of the region you are.

Imagine a currency that is completely secure, that cannot be scraped, counterfeit or stolen.

Better: imagine a currency that you can use as a secret savings account, that nobody can confiscate, steal or even know it exists.

Bitcoin is a decentralized, secure and valuable cryptocurrency which is used by more and more people. 

It’s a cryptocurrency; that is, a currency that is virtual (there are no physical coins) and secured by robust encryption algorithms that even the Pentagon cannot crack. 

It’s decentralized because there’s no central authority minting bitcoins; any person with a powerful enough computer can do it. 

It’s secure because nobody can steal bitcoins from your accounts (except if you are careless). 

It’s valuable because bitcoin is a reference for other alternative cryptocurrencies like Ethereum, Litecoin and so; alternative cryptocurrencies (also known as altcoins) are like ripoffs, which are attractive (or not) due to innovative features or its reduced transaction fees).

All the bitcoins in existence are stored in a virtual structure called the blockchain. 

Blockchain is public ledger that keeps all the accounting, for all the people holding bitcoins. You can check that ledger, but you cannot alter it in any way.

What is a blockchain?

In a nutshell: it’s a public ledger that holds all the entries needed to keep the bitcoin accounting up to date. To put it easy: imagine a real-life ledger. It has pages and pages of listings: Tommy spent $ 2 in a coffee, Vicky sent Tommy $ 500 for a consulting job, Nathalie received $ 100 from George, etc. Every page in that real life ledger is a block in a blockchain: a block contains data; that is, entries in a ledger. 

Apparently, is not that simple (there are metadata and other information) but for our purpose, that’s not important.

Pages in our blockchain are glued together by some metadata: a hash points to the prior block, just like the numbers printed in a real-life ledger assures anyone that no page is missing. 

Also, every block in the blockchain is validated by all the computers involved, in a scenario known as reaching consensus. Every computer minting bitcoins is helping to create new blocks (pages in our ledger), and, thus, validating and securing all the entries on that block (or page). Securing the blockchain (and receiving bitcoins in exchange for the effort) is what is called mining.

Mining bitcoins

In the early times of bitcoin, mining was done with a simple home computer, but nowadays you need a powerful cloud of computers, a mining rig, to profit from it. Millions are competing with each other to obtain part of the mining rewards for each block, so, to make things fair, all the computers in the network reach a consensus: raise the mining difficulty to very high values.

While it can seem unfair for people with just an average computer, this is what makes bitcoin the strongest cryptocurrency: nobody can just try and hack random accounts and steal their bitcoins. That’s almost impossible, and, to do that, you need to invest far more money that you’ll be able to steal.

You can still mine bitcoins using just your computer, but that will not be profitable at all: you can join a mining pool and add your computing power to thousands of other people to earn a small portion of the rewards. You’ll get a tiny fraction of an already small fraction, but you’ll be contributing to secure the bitcoin blockchain.

Bitcoin value

What makes something desirable and valuable? Imagine a block of solid gold, or a case full of diamonds. Both objects are valuable due their scarcity, and desirable due to its durability and stability over long periods of time.

Bitcoin is something relatively new. Some people doesn’t even know about its existence, yet many people want to own some. That’s because, regardless of its age, bitcoin is already seen as a commodity —just like silver, gold or diamonds.

Of course, there are some ripples in our golden lake. Volatility, for starters. Bitcoin’s price isn’t stable yet, and it’s subject to speculation, fear, uncertainty about its future and veiled threats about bans and heavy regulations.

And that’s the part that seasoned speculators love most: fear can be profitable. FUD can be profitable.

Profiting from Bitcoin

Speculators are people that keeps only one universal truth in mind: buy low, sell high. They buy at bid prices and sell at ask prices. As bitcoin usually doesn’t fluctuate so much during a trading day, and it has a great volume, making a profit buying and selling bitcoin isn’t that hard to achieve. You only need to follow some guidelines:

Be refractory to gurus and experts spreading FUD: 

FUD can be your best ally, but also your kryptonite. All the FUD spread by journalists and mass media must be taken cum grano salis, and always be used for your own benefit. If there’s a rumor about bitcoin being banned in some country, don’t panic: it’s almost always a ruse to lower the price so big actors can but larger volumes at a discount. Join (and enjoy) the party!

Never panic: bitcoin isn’t going to disappear or sink overnight. It can happen —and will happen eventually—, but it will not be today, tomorrow or the next month. You’ll see that coming.

Be prepared for long periods of sunken prices: a bearish market (i. e. a market that’s afraid enough that only a small fraction of people are brave enough to buy) isn’t rare, and you must be prepared for that. Always treat your investments in bitcoin (and any other crypto for that matter) like a hobby, and not like an actual income. You cannot predict when the markets will turn bearish or bullish; therefore, you cannot predict when you will be able to sell and obtain a profit. It can be weeks or even months until that happens. Again: don’t panic.

Repeat every morning: buy low, sell high: the only way to make a profit in volatile markets is repeating it like a mantra. There’s no way to teach you how to spot when a given commodity is low and when is high; we can talk about candlestick charts, OHLC charts, trends and SAR analysis, but it will be worthless if you haven’t a trained gut. Candlestick charts are invaluable, but they are just half of what you need to go out and succeed in a wild market like these.

In a nutshell

Bitcoin is a relatively new commodity, one that you cannot touch or hold in your hands, but you cannot just steal either. It’s stored on a blockchain, that is a virtual ledger stored out there, in the cloud. People can make a profit out of it in two ways: mining it (doing a hard, blue-collar job, with a fixed payment) or speculating with it (a white collar job if you ask, but with a fairly high risk involved, and high profits awaiting for those brave enough to give it a try).

So what is 5G? Who are the leaders? What does it mean for jobs?

They say that  5G wireless business is worth $500 billion and millions of new jobs.

China and South Korea are the leaders in laying out the infrastructure for 5G - with Huawei at the forefront. 

So what does 5G do?

2G delivered text,  3G the internet,  4G brought video, but 5G provides high speeds and a whole new transmission system and dimension.

5G systems support 1000 more devices per meter than 4G, using higher frequencies and secondary antennae to relay signals. It eliminates the transmission inconsistencies and slowdowns caused by buildings, mountains, and crowds.

 

By 2020, it is predicted that the average American and Aussie and European will own and use some 30 internet connected devices and 76% of data traffic will be streaming video. 

There will be 50 billion connected devices worldwide. These can range from existing technology, such as smartphones, tablets and smart watches, to fridges, cars, augmented reality specs and even smart clothes.

 

Some of these will require significant data to be shifted back and forth, while others might just need tiny packets of information sent and received. 

The 5G system itself will understand and recognize this and allocate bandwidth respectively, thereby not putting unnecessary strain on individual connection points.

 

5G will provide unbelievably fast broadband speeds, but more importantly it will have enough capacity to perform every function needed without loss of speed or connection, no matter how many people are connected simultaneously.

 

5G will run on a new "high-spectrum band", which uses higher frequency signals than 4G. The new band will be much less congested, which will be vital for use with the Internet of Things. However signals won't travel as far, so it will need more access points positioned closer together.

 

Larger cells will be used in the same way as they are now, with broad coverage, but urban areas, will be covered by multiple smaller cells, fitted in light poles, on the roofs of shops and homes, and even inside bricks in new buildings. Each of these will ensure that the connection will be regulated and seemingly standard across the board.

 

Algorithms will know how fast a device is travelling, so can adapt which cell it is connected to. For example, a connected car might require connection to a macro-cell in order to maintain its connection without having to re-establish continuously over distance, while a smartphone can connect to smaller cells with less area coverage as the next cell can be picked up easily and automatically in enough time to prevent the user noticing.

 

But you don’t just revolutionize global connectivity overnight.  You have to build massive networks of antennae for internet providers. 

 

4G boosted domestic GDP by $100 billion and led to an 84% increase in wireless-industry employment. 

Companies in data-intensive industries like self-driving cars, IoT, and blockchain will migrate to areas with5G to stay competitive.

 

We look forward to playing a part with Huawei in making Australia 5G ready

 

The key Areas where AI is focussing on

Where does your texhnology, app, project fit in?

5 Attributes you need to look at when building an AI model or App - to claim an R and D tax rebate

A major issue in claiming an R and D tax deduction is to mapping the language that the government defines as r and d to your project. 

1. What is the science?
2. What is the technical risk?
3. Is it innovative? 
4. What testing have you done? 

And the list goes on 

The reality is that most software projects and ongoing developments of software and apps should be defined as R and D . It all about the interpretation and justification - and the key is to be able to document and justify your project as an r and d one....because I believe that anything can be justified, and the intention is that the government wants to encourage companies to continue to innovate - 

Because innovation is the food for growth! 

Everyone is talking AI and machine learning..... is an app just a microcosm of a larger machine learning AI model? 

So, what makes AI or an app useful?  It’s all about how data in the model is used.

An AI model is a way of looking at data - as data changes, the AI model nnneds to adapt accordingly - 

An AI system needs to be built with five attributes in mind says Dinesh Nirmal - vice president of analytics development at IBM. 

1. Managed

The AI and machine learning model needs to have  thoughtful, durable, and transparent infrastructure. 

That starts with identifying the data pipelines and correcting issues with bad or missing data. There needs to be a methodology of integrating  data governance and version control for models. The version of each model — and their might be thousands of them concurrently needs to clearly  indicates its inputs - 

where the data came from needs to be known 

2. Resilient

Being fluid means accepting that models will fall out of sync. That “drift” can happen quickly or slowly depending on what’s changing in the real world. Regression testing needs to be done on a regular basis. .

Accuracy thresholds need to be defined and and automatic alerts to let you know when the model need attention. 

Will you need to retrain the model on old data, acquire new data, or re-engineer your features from scratch? The answer depends on the data and the model.

Before trying to find the problem, one needs to look at defining the problem.

3. Performance 

The AI model needs to compute the transactions in milliseconds, not minutes, to gain a competitive advantage and make the system work. 

Optimum performance is key 

The AI model needs to run fast and error-free regardless of where you deploy it on premises , or in the cloud.

4. Measurable

The results and outputs need to be clearly measured and have adequate reports. 

When starting the project , visualize how you are going to report what you’re learning and how it changes.

What you can measure you can manage - think about how you can easily report on short , medium and long term goals 

Some Kpis 

1. improvements in data access and data volume,
2. improvements in model accuracy, and ultimately
3. improvements to the bottom line.

5. Continuous

The AI model needs to change and continuously learn as the world changes. The Ai model needs to be continuously evaluated and retrained to adapt to a changing world. 

Jupyter and Zeppelin notebooks that can plug into processes for scheduling evaluations and retrain models are useful tools to use 

You will gain an understanding of absorbing  the advantages and limitations of the algorithms, languages, datasets, and tools that are being used. 

Fluid AI demands continuous improvement for data, tools, and systems, but also continuous improvement from the team. 

Data science is a journey. Pay attention to these five attributes and you’ll bring focus to each moment and force yourself to find clarity about the future.

The data will never sit still, but would you really want it any other way?