Unfortunately robots are already here. Without fear or embarrassment, we can ask the questions: “When can I have a robot maid at home?”, or “Can I get a robot cook some day?”

Recently an influx of news came from China about the appearance of more human-looking robots in everyday life.

Chinese are surpassing Japan in purchasing consumer robots. The robotic industry in China is growing at the 40+% per year since 2014. Among all the robotic units sold in the world since 2014, more than 20% are in China.

In theory, because of the superposition possibility of the quantum bits (called Qubits, explained by an earlier blog article on new chip designs), if we can use the qubits to represent computational mechanisms, it should deliver exponentially increased computing power, by an order of millions of times faster than traditional computers. Researchers at World Economic Forum held in Davos, Switzerland in January 2016 expressed great enthusiasms that such technologies will become available and be the disruptive force to the traditional computing and communication technologies by year 2020. It comes with high hopes that quantum computers may bring a giant leap forward in complex computations such as in machine learning and optimization. We may also expect that the future machines can become a lot smarter in a “humanly” way.

Quantum computers are hard to produce because the difficulties to detect the quantum particles or control the quantum state. Some current pioneers of these quantum machine initiatives, either the quantum chips made by IBM or D-Wave machines that Google has invested in, may require special algorithms to operate. Traditional algorithms are not suitable on these quantum designs. Because algorithms can definitely make a significant difference on the performance of the problem-solving of any machine, heated debates and competitions on these new quantum computers are common. The light-year promises in computational power from these quantum machines compared to the classical supercomputers or algorithms have yet been demonstrated, but by theory, that stage is achievable.

New ideas are coming out these years to make these machines capable of handling all conventional computational jobs. We may need it for many current AI algorithms to work faster and better. However the economical or practical judgment of whether we need a sickle to shave the beard is another question. From pure computational standpoints, software simulations of quantum computations are possible and in progress in the technology world today. These simulations can offer significant cost advantage over the hardware solutions before we truly can harness the quantum particles in measurable ways.

Started in January 2011 by Tencent, within a few years, WeChat has reached 90% of smartphone users in China, 600 million active users per months, covered 200 countries in 20 languages. Its growth rate has far exceeded those of Facebook or Twitter. After entering India, within one year it reached 27% of the market share in its category.

Where does its power come from? TriStrategist thinks the following:

1. It fits the needs of today’s digital-driven, mobile-driven mass-media environment. It is easy to use, intuitive, fast and scalable. The simplicity of its interface focuses on the mass-media essentials and allows users to easily publish, link or forward contents.

2. It supports instant voice and video messages. No matter a 3-year-old girl or 90-year-old grandfather, once they open the WeChat app on their cell phones, they can start using it with fingers, their voices and phone cameras. No computer background is needed.

3. Outstanding performance. Once getting connected to WeChat, Worldwide users can find the speed and stability they need to support instant information sharing and communication. Messages, web articles, pictures, videos, music, emoticons, etc., the amount of data and information being exchanged daily through WeChat is amazing, yet all seem to be within the expectation of performance.

4. The straightforward features to support businesses and personal lifestyle choices. The power of communication is given to the users. It’s not only a networking platform, but also a combined entertainment, information sharing and lifestyle consumption facilitator.

A recent story published in Science Daily told the first case of a patient’s controlling robotic arms using thoughts alone. The project was conducted as a clinical collaboration between Caltech, Keck Medicine of USC and Rancho Los Amigos National Rehabilitation Center. Most earlier experiments on allowing brain’s controls over prosthetic limbs were done by implanting neural prosthetic devices to the motor cortex, the brain’s movement center, which gives detailed orders to the body through spinal cord for the right movements. They often observed clumsy responses and didn’t work as smoothly as designed. Now a different approach is adopted. For the first time, two small chips were implanted to the brain’s posterior parietal cortex area (PPC), a high-level cognitive area responsible for the “intent” of the movement, an early pathway of a brain’s movement planning. By recording the signals from PPC cells and decoding them through computer analysis simultaneously, computer can in turn order the prosthetic limbs to do what the brain has intended. It created an almost out-of-world experience for the first long-term paralyzed patient when he could grab a drink or move the computer mouse easily by just using his own thought alone.

Another study by Stanford published in WIRED this week is about using neural stem cells to grow 3-D pieces of brain balls -“human cortical spheroids” (hCSs) – that can look, form connections and pass signals like living brain matters. Molecules in stem cells submerged in nutrient fluid started dividing and growing, forming the “messenger” type of new cells, astrocytes, which are critical for the formation of synapses – bridges between neurons for passing electric signals. The high excitement of this experiment comes from the possibility that with today’s technological capabilities, the organic growth patterns of those “brain matters” in the lab can be sliced and recorded in great details with time through electrophysiological recording. Therefore real models of neural network are palpable and the human brain may be truly deciphered in its entirety one day.

Compared with Relational databases, especially in dealing with unstructured data, NoSQL database technologies in general are less expensive, more scalable and often with better query performance. Typically these technologies allow data to be stored in more native formats and does not need to enforce schema in advance.

Today’s NoSQL database products can be roughly divided into the following categories:

– Document DB: Data are stored in a “document” structure which consists of many different key-value pairs. Document itself is an object container. MongoDB is one of the leading Document DBs on the market. MongoDB data are stored in BSON (Binary JSON) format. Microsoft is now offering a fully managed XML-JSON-based DocumentDB service on Azure. The document concepts are the same.

– Graph DB: Data are stored in a network structure which can be easily represented by visual graphs, such as social connections. Neo4J and HyperGraphDB are examples of Graph DB offerings. The data in a Graph DB are stored as Nodes and Labels. It allows faster queries on relationships between nodes. For example, to answer a question on the potential relationship between two seemingly non-related Twitter IDs. The query needs not to perform expensive joins. In building a graph from raw data, most of the relationships in the data model need to be pre-defined by JSON files. Dynamic relationship crawling APIs can still be challenges for Graph DB.

– Simple Key-value DB: Every single item in the database is a key-value pair. Redis is one of such examples. Additional functionalities can be added to the pair such as specifying a type for the value, as “string” or “Integer”, etc.. Google acquired Firebase in 2014 as a real-time database for developers. It’s in fact a JSON-based Key-String DB. JSON can be returned through RESTFUL client-side code. The sample usages of Firebase are real-time chat rooms, control notifications, etc.

– Wide-column stores: Examples are Cassandra and HBase. These open-source data models are optimized for data stores across multiple clusters and fast query performance over large datasets. They are widely used today for analyzing Big Data. HBase is also available in Mcrosoft Azure HDInsight service offering.

Scientists and engineers together have been trying to build a brain-like computer for decades. With the concepts of Artificial Neuron Network (See our earlier blog on Artificial Neuron Networks ) and advancement in computer engineering, a modern computer chip designed without a conventional powerful central processing unit (CPU), but with millions of parallel “neurons” and connecting “synapses” packed into a single unit to simulate one brain function, e.g., one cognitive ability, may well come closer to capture that specific brain function after repeated learning, storing and processing information. When a large number of these special units combined together in a coordinated fashion, a “machine-made brain” may just be born. Thus “neuromorphic computing” evolves from here. IBM, Qualcomm and several other chip designers and manufacturers have been experimenting with the ideas with great progress in recent years.

Besides enhanced “brain-like thinking” capabilities of such machines, another key benefit from neuromorphic chips is the energy conservation. Information storage and processing are now arranged inside the same interconnected neuron nodes. The cost of energy and heat from the switching, such as those in between memory and CPU in conventional chips, has been drastically reduced, resulting in better performance in general.

Due to its significant disruptive nature (to both future hardware and software) and high potential commercial clout, the World Economic Forum’s Meta-Council on Emerging Technologies ranked neuromorphic technology as one of the Top 10 Emerging Technologies of 2015. The interesting facts today are that although prototypes of the neuromorphic chips are available, great software to demonstrate their “brain” power are yet to come out.

A car is also a complex technology ensemble. Unsurprisingly, with the backdrop of today’s exciting technology developments on all fronts, so many new innovations can be assembled and reflected in the making of a car. If one looks at 2015 new models of cars or trucks, technology connectedness has already become a common theme with most of the vehicles equipped with touch-screen GPS and mobile connections. Navigation, communication and digital media services are easily present.

Auto industry is due for some truly exciting changes. Google’s driverless cars are already a reality although yet to be commercialized. In progress today there are new design ideas with augmented-reality technologies which can turn a windshield into a 3D computer-graphic display of navigation and other key information. Apple, known for its revolutionary innovations on personal devices from its recent past, is now gearing up to make the next revolution in automobiles. A recently disclosed project called “Titan” piqued interests of many on what Apple may come up – an electric car competing with Tesla or a whole new level of technology experiences in a personal vehicle?

Many modern-day thinkers and leaders have already given plenty of thoughts on the future of transportation. Rapid changes of the world and societies will push for drastic departure from the traditional business models and vehicle designs in both automobile and transportation industries. Bill Ford pointed out the future need of a transportation ecosystem and a personal vehicle as a component of mobility and connectedness. Elon Mask went further and released a design of a super-fast hyperloop as a future mass transportation channel (See pic), which is targeted to solve many of today’s transportation challenges in highly congested areas.

Technology will only enhance future individual freedom and mobility. Driverless cars will have their own usefulness and super-fast mass transit will surely be in the picture of future transportation, but people’s love of the freedom of driving is not going to wane. In fact the true revolutions in personal vehicles are indeed coming with the concept of “driving” redefined. Having appeared in the sci-fi movies multiple times, flying cars have long been dreamed of and imagined for decades in the past. Today many attempts are well on the way for these personal aerial vehicles (PAVs, see pic). With FAA’s quick catch-up proposals on drones these days, it’s reasonable to assume that regulatory limitations on many future unthinkable will not be too daunting after all in a changing world.

R is an open-source programming language for statistical computing. It has gained much popularity among data analysts and data scientists today. R objects and libraries support a wide range of statistical tools and graphical techniques. Its object-oriented programming model allows easy extension of R among other software programming languages. For example, it can combine C/C++, Fortran to handle heavy computational tasks and at the same time be called within JAVA, .NET or Python for business applications. It also supports a LaTeX-like documentation format.

The extended family tools of R are booming in open source world. RStudio, an open source startup, offers R Shiny Server, a web application framework based on R to produce interactive web applications and visualizations. R Shiny Server supports many data-binding widgets that can be easily dragged into web UI. Along with more sophisticated graphic tools such as Google Charts (with googleVis package, an interface of R and Google Charts APIs), many interactive pivots or dashboards can be built on the fly and displayed dynamically. With the ability to combine coding on the same page of the presentation, it offers greater capabilities in allowing dynamic contents online. One of the typical example is a Motion Chart (see pic), used for finance, traffic, voting, and many other areas that requires instant statistical handling of large amount of dynamic data and fast visual displays. These charts automatically bind data point-by-point on UI by design.

For enterprise users, the power of full automation on many data-driven business reporting is within reach. To use R-series tools, a backend data pipeline still needs to be built but not complicated. Today data analysts in many companies are exploring these tools to gain instant business values and visibility out of the typically tedious and often offline data analysis work. For example, eBay has been using R Shiny Server for reporting eBay Partner Network analytics.