focus rather than the applications that we saw in the previous talk, more on the science side. I'll start by quickly describing the differences between neurocomputing and neuromorphic, but some of these differences were already nicely described by my previous speakers. Roman was saying how the field of neurocomputing dates back to the early 40s with McCulloch and Pitts and even to the early 30s. Neuromorphic computing is a relatively younger field. Mark was mentioning Carver Mead who coined the term neuromorphic in 1992 in a paper in the Proceedings of IEEE to refer to hardware implementations of neurocomputing algorithms. And it's a field that started in the early 90s, maybe late 80s, but now became very, very popular. And you can see that because there is a wide range of different neuromorphic computing architectures that are being now proposed in the community to implement different types of algorithms and to follow different types of approaches. So you can go from, we've seen also in this morning, super computing algorithms implemented on standards, computing architectures, or implemented on generic computers or graphical processing units like we saw from the celebral model with Tadashi. We've heard about Spinnaker, which is the sort of dedicated computing platform assembled using ARM processors. So standard microprocessors being done in Manchester with under the leadership of Steve Furber. Another example is high precision, but also power hungry analog circuits to simulate large scale neural networks being developed in Heidelberg under the leadership of Cahindt-Smeyer. We've heard about the IBM True North. IBM is also interested in pursuing more for practical applications, neuromorphic computing approaches, and they have a very nice, remarkable, very low power chip that is fully digital. It's not analog. And it's using integrated and fire neurons and asynchronous digital circuits. We've also heard from Mark the approach that Stanford is taking through Neurogrid. It's again a million neuron system implemented with analog circuits on a 16 chip printed circuit board. And I'm going to tell you more about smaller scale systems like we saw yesterday. It's interesting to focus also on small scale systems, not only large scale systems that are being integrated on single chips, but that also try to implement or emulate in hardware more details of the neural processes that we are interested in understanding. And the Institute of Zurich, of Neuroinformatics in Zurich, INI, is not the only one pursuing this approach. There's other groups around the world here in Australia, the Marx Institute in Italy, the National Health Institute in Germany, the CITEC in Bielefeld, and elsewhere in the world, just to give you an overview. So the neuromorphic term actually is being used for a wide range of different hardware implementations which have different application domains. So if we want to sort of keep with this classification, you can think of neuromorphic dedicated platforms for practical applications, for example, like the IBM chip. You can use neuromorphic computing platforms as a tool for neuroscientists to simulate your neural networks, for example, to implement Nestor neuron on dedicated computing platforms. And this would be sort of simulation or the approach that Carver Mead proposed in the early 90s and that I will try to show you also and that we follow in Zurich, is using hardware to directly emulate the physics of the neural systems that you're interested in understanding. This approach is the one that is following this idea of understanding by building. It's also what Dick Feynman, also at Caltech at the time, wrote on his board, what I cannot create, I do not understand. The idea is that we're not trying to build a simulation platform. We're really trying to use technology, VLSI technology, microelectronics and silicon, as an extra tool to understand the principles of computation in the brain. You can think of this as a tool in the toolset of a neuroscientist. So you can do software simulations, you can do electrophysiology, you can do imaging, but you can also build electrical systems that reproduce the physics of the real systems in order to understand the underlying principles of computation. And the important point is that if you follow this approach, it's important to use components that reproduce the physics of the processes being modeled directly. What I mean by that is that when we use electronics, we have to use them in a way that directly emulates the processes of neural systems. So if we look at transistors, this is a symbol of a transistor, sort of the basic fundamental unit in all of the chips that we make. Instead of using them as a digital fully on or fully off device, like you would do in digital, we use them in analog. Not only we use them in analog, changing smoothly the input and measuring the output smoothly, we use them in a region which is called subthreshold with very small voltages and very small currents. You know, hundreds of millivolts and femto, picot, nanoamperes. In this region, you see we have a semi-log plot. We have, that means that if we change linearly the input, we get an exponential output current. The reason that we get an exponential output current is because the mechanism of carrier transport is diffusion. This is exactly the same physics that is underlying the transport of ions across membrane channels. In fact, if you look at what Hodgkin and Huxley were measuring in the 50s, when they were trying to characterize the sodium and the potassium conductances, you get exactly the same plots. You get voltage differences on the x-axis, semi-log plot on the y-axis, and you get a line which saturates. You get exponentials. You have Boltzmann statistics and diffusion, both whether you're using electrons to flow through transistor channels, or you're using ions to flow through proteic channels. And now you can imagine, this was the insight of Carver Mead. If you had these analogies at the microscopic level with single channels, once you integrate many, many of these transistors together, you will observe analogies also at the macroscopic level. We'll have exponentials, logarithms, thresholding, all of the type of non-linearities and behaviors and dynamics that we observe in the neural systems by using electronics in this domain here. And this is something really particular that only a few groups in the world are pursuing. Like, as I said, in Germany, Italy, Switzerland, some groups in the States. So the idea is we use the transistors in this way. We put many and many of them in a chip because we can use very large-scale integration and we build neural networks. That's the principle here. So the transistors are being used to implement synopses and neurons. I'll show you examples of that. We can easily implement all sorts of dynamics and feedback mechanisms and plasticity. I'll show you those also. And integrate many of these components on chip. In the end, we end up with a silicon device, a microelectronic device that has massively parallel arrays of non-linear circuits that are all computing in parallel. These arrays are distributed by design. They're doing distributed computation. It was mentioned because these are analog circuits, once you fabricate them, even though the layouts, the design is the same for everyone and you just copy-paste like you see here, when you fabricate them, they will have variability. They're inhomogeneous because of device mismatch. And so these computational structures are distributed but also highly imprecise and variable. Classical engineers see this as a bug. Neuroscientists or people that want to reproduce or emulate neural processes see this as a feature because neurons are the most diverse type of cells that you have. And also the noise is an important factor. Noise is being now used as a feature for doing, for example, stochastic resonance or improving the type of computation that can be done in these types of neural networks. And also because we have electronics, we get noise for free. Instead of trying to fight it and minimize it, we actually try to exploit it for computation. Again, by design, these chips are fault tolerant. If some transistors fail, the synopsis will just stop working but because everything is working in parallel, it's not going to compromise the operation of the whole chip. Maybe performance will degrade but the chip will still be functional. This is different from what you have in your microprocessor on your laptop. If one single transistor is broken, you have to throw away the whole chip. So from the point of view of technology, it's a promising approach that might be useful also for improving technology in standard computing platforms. And again to emphasize, computation is analog. We're using microvolts and nanoamps. So we have really nanowatts. Extremely low power and extremely parallel. Once we go off chip, we exploit the best of both worlds. We can use digital, we're not against it. So what we do is we encode the address of the neuron that produced the action potential with a digital word and we put that on a digital bus. And then you can have a chip in which you have analog computation inside and then digital communication outside. And then you can connect multiple chips and take advantage of all of the digital infrastructure that the IT industry has developed in these, tens and hundreds of years. What is also important to notice is that we are, as Roman was saying, it's more and more important to realize that synapses are not just multipliers. They have important nonlinear functionalities and dynamics. And we can reproduce these dynamics in silicon really easily. So these chips are really massively parallel arrays of nonlinear dynamical circuits that have realistic synaptic and neural dynamics. And this is a really a big paradigm shift in computing. Computing in standard technologies is basically done following the Turing machine approach for Neumann architectures where you have memory outside and then computation in the central processing unit. In the chips that I just showed you, we have memory and computation co-localized. And again, we do have time. Time is not discrete. In these chips, time is intrinsic. I just took this word while Roman was talking and put it in the subtitle here. Time represents itself. This is what Carver Mead used to say. So we have continuous dynamics. We're not discretizing and everything is real time. So if you want to emulate one millisecond of neural activity, you have to wait for one millisecond and see what's going on in your neural electronic circuits. It's important to try to reproduce biological plausible time constants if we want to interact, if we want to build neural systems that interact with the environment. And the reason is that if we want to interact in real time, for example, for gesture recognition, we have to have time constants in our systems that are well matched to the time constants of the signals that we want to process. And if it's natural events like speech or motion, these are tens of milliseconds. It's not gigahertz like you have in laptops. It's really hertz or fractions of hertz. And this is something difficult to achieve with silicon. Silicon wants to go fast, so we have to slow it down. And again, it's something that not everybody is doing. Also people doing analog circuits, for example, in Heidelberg are not using real time. They're using accelerated time because it's really a difficult task. But if we use sub-threshold transistors like Carver Mead was saying, we can achieve these 10 millisecond or 100 millisecond long time constants. And this is just to show you that just by putting together, is it five, six transistors in a capacitor, you can really reproduce, emulate the physics of real synapses. Here you have some data taken from a chip that has this circuit. And you see that you have these decaying exponentials which are a really good match to the decaying exponentials measured from real synapses. So you have excitatory post-synaptic currents produced by electronics, and you have excitatory post-synaptic currents measured in real synapses. And you see you have exactly the same type of dynamics. You can change parameters on the circuit, for example, which determine the efficacy of the synapse if you change this voltage here, and then you get different type of outputs depending on the parameters. You can even derive the transfer function of the circuit. It's a circuit which is so simple that you can write down all of the equations of the transistors, you know, do Kirchhoff, and then derive the transfer function. And you do get a first-order differential equation, which is a really good match for synaptic dynamics. It's a really good model. And this is for one part of the synapse. You can think of this as a building block. You'll see you have a differential pair with two transistors here that are pulling current in one way and pushing current in the other. And you can put these building blocks together to build more complex structures, such as neurons, conductance-based neurons. So this would be like a multi-compartmental model where you have a leak conductance here, you have after hyper-pororizing conductance here, and then you have other circuits that implement, for example, sodium activation and sodium inactivation conductances or potassium conductances. This is just, again, using the analogy that Carver was saying that the single transistor represents a channel, or maybe a population of channels. It's nice to notice that by trying to optimize, minimize power consumption, minimize size, in designing these circuits, we ended up, the community, ended up with a design that is a two-variable system, which is actually identical to the adaptive exponential integrative fire model that Roman and Wolfram Gerstner proposed in 2005. Here we have a first-order differential equation with a non-linearity. This is exponential with a positive exponent. And this is a slow variable that is governing the adaptation current, which is this after hyper-pororizing current here. And if you take data from the chip and if you fit it with these equations, you see you have a really good fit with both negative exponent exponentials and positive exponent exponentials. This is really hard to get if you just have arbitrary fitting functions. You can put, just inject constant current into the neuron, measure what happens after it goes into the adapted state because the chip is subject to thermal fluctuation, you get Poisson statistics. It's a fully deterministic circuit, but it's a real physical system. You get stochastic behavior out of it. And you can change parameters. You also have parameters here, like the leak conductance, the adaptation rate, and so on. And by changing these parameters, you can obtain different behaviors. This is an example of spike frequency adaptation and bursting that was obtained just by having constant current injection and finding the right parameters for this particular set of data. So we have now circuits that we can use to implement synopsis, circuits that we can use to implement neurons. We can really build large neural networks, but the interesting thing that's in the title of the stock is plasticity. How can we put together these circuits and include plasticity to reproduce interesting computational properties? So I'm going to quickly go over the spike-based learning idea and framework. One of the most popular models is spike timing dependent plasticity, STDP. It was sort of shown in the late 1997 by Pooh and Markham independently. You can observe spike timing dependent plasticity in many different animal models, many different areas of the brain. And the classical curve is shown here. If you have causal relationship, pre-synaptic spikes produce post-synaptic outputs. You tend to increase weights, and if you have anti-causal relationships, you tend to decrease weights. This is the classical curve, but just the word of caution is that you can observe everything and the contrary of everything if you look at biological experiments. And in fact, if you go, for example, in electric fish, you can have anti-STDP. If you look at GABA-ergic neurons in hippocampus, the same culture that was used for this data, you have pure hebbian type of plasticity. And if you look at stellate cells in your cortex, you have anti-hebbian plasticity, just as a word of caution. But the idea is that you can implement learning just by using spike timing. And this was a very popular idea. Many, many models were proposed. And of course, there was also controversy saying this is not necessarily the most powerful algorithm that can explain the data measured in biology. Maybe it's an epiphenomenon and there are more elaborate algorithms that can actually explain this data and other data as well. This is what we followed. We followed the theories of Gerstner, Fussi, and many other computational neuroscientists that are saying spike timing is not the only thing. If you want to really understand and reproduce the data, you also have to look at other factors, such as, for example, the state of the memory potential of the postsynaptic neuron when you receive a presynaptic spike. And we were very happy to see these theories come up because they are really nice for VLSI implementation. It's really easy and efficient to implement these theories that come from theoretical physicists and computational neuroscientists in silicon because it doesn't require you to have very elaborate circuits. For example, the theory says you don't need to have 32-bit floating point precision for a synapse. It's enough to have two states, either an LTP state or an LTD state, on long time scales. In silicon, this is really nice because then you just need a flip-flop. You need a couple of transistors to achieve this. The other recipe is that, okay, if you have bisability on long time scales, you also need redundancy. You need many, many synapses to encode patterns, to encode variables. And this is easy to achieve in silicon because we have very large-scale integration. We can put many, many transistors on a chip. And finally, another important ingredient for plasticity is the stochasticity. If you have many, many synapses, it's important that you select a random subset of them when you want to perform LTP. Otherwise, all of the synapses just behave as one big single synapse and it wouldn't be very useful. And the stochastic mechanism that we use to do this random selection of subsets of synapses is the spike timing. Because we have Poisson spike trains, and if we represent variables with mean rates, for example, a variable is a three hertz number, every time we produce a three hertz Poisson spike train, this precise spike timing is different and it's random. And this is enough to allow us to have stochasticity in the learning to get this to work. And we can, as you saw the circuits for synapses and neurons with just a small number of transistors, five to 10, we can implement these spike-based learning algorithms. And then we can integrate them on large scale on chips. We've been doing this for many years since the early 90s, first at Caltech and then in Zurich. And so we've been doing many, many types of chips. The latest chip that we've fabricated is essentially a big memory chip in which the memory elements are synapses. They're these complex nonlinear elements that have biologically plausible dynamics and that have spike-based plasticity algorithms implemented in them. So it's a chip that has 64K, actually 128K synapses and 256 neurons. We don't have these 10 to the 11 neurons that people want to reproduce in hardware. We only have 256 neurons. The idea is that we're only looking for principles at the moment. And when we want to explore principles and understand the principles of computation, it's not necessary to have large scale simulations. These have been sufficient for us to do all sorts of very interesting projects, these numbers, hundreds of neurons and maybe thousands of neurons. I just want to demonstrate some examples of plasticity with you. So the first thing that one can try to do is to implement the most trivial type of learning which is the perceptron-based learning. In perceptron-based learning, basically you have, sorry, you have a single neuron which is receiving a big input from all of the big synaptic matrix field. And the spike-timing-based plasticity algorithms that we implemented actually reproduce Hebbian type of learning as well. So what we do is we initialize all of the synapses to have a low weight. Remember, these are bistable synapses. In the end, they're either high or low. And then we present Poisson spike trains that represent some pattern. So here it could be IBM, INI, or anything else. White pixels are Poisson spike trains of say 50 hertz or maybe 100 hertz and black pixels are Poisson spike trains of five hertz. When we present this to the input, these are converted into spikes. They go to the synapses. The synapses produce currents. The currents are integrated into the neuron. The neuron eventually fires. And then by looking at the timing of the pre-synaptic spike and the membrane potential of the post-synaptic neuron, the synapses decide whether they potentiate or depress. And because we were using Poisson spike trains, only a random subset of synapses potentiates. And if we do this, if we present a stimulus for 50 milliseconds, a small random subset potentiates. If we keep on presenting for another 50 milliseconds and more and more and more, more and more synapses potentiate. It's important though that some synapses do not potentiate because then we have resources left to learn different types of patterns. For example, if we learn IBM, we don't overwrite the INI pattern. So we have a neuron that is able to be tuned to different classes of patterns without using all of its synaptic resources. This is the Peri-Stimulus time histogram of the neuron before training the neuron, actually we reproduce this for all 256 neurons and before training the all neurons had a very low firing rate. And after training, they had a high firing rate. You see because of the mismatch I told you the variability, there's a huge variability also in the output of these neurons. This is after a few milliseconds, maybe 50 or 100 milliseconds of training. If you keep on presenting the same stimulus though, the histogram changes and more and more neurons get potentiated more, more synapses so the neurons become more strongly tuned. So you go from this situation to this one to this one by keeping on presenting input patterns. So this perceptron idea works really nicely. If we try then to do more elaborate type of benchmarks like we've heard about the handwritten digit recognition benchmark, then you'll see that this perceptron works but it's nowhere near close the state of the art in machine learning. So what we tried is we tried again to represent numbers with Poisson spike trains. We presented them to a neuron and then we trained the neuron to recognize three against all other digits. And the classification results, the accuracy, the performance was around maybe 50%, you know, just to give you an idea the state of the art machine learning groups fight over small percentages in 99.98 and 99.97%. So 50% is laughable. No one would ever even consider this type of technology in these type of benchmarks. But neuroscience and machine learning comes to the rescue because there are some theories from the mid 90s that show you, that show that if you have enough weak classifiers or crappy classifiers like the one that we have here and you put them together, you can improve by using ensemble learning techniques or boosting techniques, you can improve the classification performance just by combining many, many classifiers. And so this is what we did. We just, you know, we have 256 neurons in this chip instead of using one, we started using more and more tens and 20s and 50s. And we saw that the hardware follows the theory. The theory says that the error goes down with the number of elements or classifiers exponentially. This is really powerful. And so this is just to tell you that this technology, even though these proof of concepts, perceptron ideas are laughable when you look at a single chip, at a single neuron, you can actually achieve state-of-the-art performance using more neurons. And you can do this using one chip that burns a few microwatts and performs in real time. You know, in 50 milliseconds, you have your classification result. The 99.98 accuracy performance that machine learning algorithms achieve, they do that using GPU farms, which burn kilowatts, if not megawatts, and take up a room that's as big as this room here. So this is the difference. We're getting closer to, you know, the technology that is in our head by using these sub-threshold transistors. Another type of learning is, this was supervised learning because we were telling the neuron, you know, whenever you see three fire and when you see four, five, six, don't fire, we were giving it a teacher signal. Another form of learning is that you can do is by using Hopfield-type networks. We connected all of the neurons to all of the synapses. So we have a fully connected network in the chip. And then we just inject current into a subset of these neurons and we see what happens. It's just unsupervised. And you can imagine if you inject, for example, current into the bottom four neurons of the chip, you will have activity at the output here. Because of the recurrent connections, you have activity in all of the synapses of all of the neurons. But the synapses that receive input, presynaptic inputs, and have no post-synaptic outputs, they will tend to depress, just have been learning, or STDP. The synapses that receive presynaptic input and also have post-synaptic output, there is correlation, neurons that fire together, wire together, so they will tend to potentiate. It's just classical, happy, and learning in Hopfield networks, but we can do this with sub-threshold, non-linear dynamics in these chips. And you can stimulate other sets of synapses, and you will get other sets of synapses potentiated just with unsupervised learning. This is the data from the chip that demonstrates this. We have the synaptic matrix here. Black means low state, white means high state. We stimulated the bottom 64 neurons for one second, and the synapses started to increase randomly, and the other synapses started to decrease randomly. We repeated the same stimulation again for another second by choosing different populations of synapses. So this population of synapses could represent your mother's face, this one your father's face, and so on, they just represent some patterns. And as you keep on presenting over and over the same input patterns, the synaptic matrix starts to get its shape. Until finally, the synaptic matrix is strong enough that you have sustained activity. This is the property of the attractor network. Even after you take your stimulus away after one second, the attractor stays active for as long as you don't inhibit it. And here, when we stimulate different patterns, we inhibit the others. So we keep the attractors in its on state until we stimulate something else. And here, for example, in the last simulation, we didn't inhibit anybody else, so you see the attractor just stays on. This is classical Hopfield theory, but again, we achieve this with microwatts on these neurons. And these synapses, they have this stop learning mechanism which has been demonstrated by theoretical physicists to have maximum memory capacity in these Hopfield networks if you're interested, all the details are in these papers. So once we have attractors, we can use these chips to look at neuroscience. Again, the idea is that we have this as a tool in your neuroscience tool set. And you can try to reproduce or understand perceptible by stability, decision-making, working memory. So for example, we configure the chip to have two attractors, A and B, which were mutually inhibiting each other, and then provided some input. You can see when you provide input to the population A, if you look at the mean firing rate, you have a high activity. When you take the input away, you still have some sustained activity. It's slightly lower, but it's still there.  You will have high firing rates during the presentation of the stimulus and lower sustained activity after you take the stimulus away. And you can repeat this on and on by having switching between these two percepts just by stimulating the different populations. Or you can kill everything just by having an inhibitory input. You can look at the dynamics. You can reproduce the gamma type. In fact, if you have constant current injection in both populations, you will have autonomous switching between the two populations. And this is what happens by just injecting the same amount of input both to A and B. One attractor will stay active until for finite size effect it will be switched off. The other attractor will become active, and so on. You can look at the raster plot coming out of the chip, or you can look at the mean firing rates just by doing the population averaging. And the switching dynamics really reproduces the type of dynamics that is measured in monkeys when they're looking at perceptual by stability. So to make a long story short, we have the possibility of implementing, of directly emulating the biophysics of real neurons in silicon by using these sort of unconventional domains, a subthreshold domain where we bias transistors in this domain. And now I showed you a learning chip where we have plasticity in the sinopsis. We recently implemented a multicore chip where instead of having a single chip, we have a core. And within the same chip, we have multiple cores. And then we can send spikes from one core to the other using the digital framework that I told you about. So inside the cores, we have analog computation. And then not outside the chip, but outside the cores, we have digital communication. And we're in this, you know, having connecting all these neurons to among each other would require a huge amount of memory unless you make some assumptions. And if we assume that we have clustered connectivity, like the type of connectivity that is measured in the cortex, then we can minimize the memory requirements and we can really have very efficient implementations. So for example, if you have a multicore and multi-chip system with just a few kilobytes of memory, not the petabytes that we heard about, you can really have networks in which one neuron can fan out to other domains like thousands of neurons in another core or other thousands of neurons in other cores. The only assumption we are making here is that we have clusters. We don't have all-to-all connectivity everywhere, but we can fan out to large populations in this clustered way. The chip that implements this was, we just received it back. It's again using a quite old process, 0.18 micron or 118 nanometer. But it allows us, because we have very large scale, we have very small circuits, to have 1,000 neurons per chip. And this could be one core. So these are actually four cores, but then you can integrate thousands of cores on a single chip and you can really have millions of neurons. Again, the goal is not to have very large scale systems. The goal is to have relatively small scale. We have thousands here, which allow us to explore different type of computational paradigms. As I told you, attractors, working memory models, learning perceptron models and spike time independent plasticity models. So in conclusion, the idea is that we can try to use these devices as an additional tool, in addition or in parallel with our MATLAB simulations and Python simulations and neurophysiology experiments to explore the principles of computation. And the things that we've been looking at recently are context dependent learning and processing with these chips. There are some papers, if you're interested, that show recent results. We've been trying to reproduce properties of working memory and decision making and trying to measure reaction times and see that the reaction times measured in these real time electronic systems match the reaction times of monkey in monkey experiments. And we have results also there. Or we've been trying to implement cognitive systems by using state dependent processing. By connecting many of these attractors together, you can implement sequences or finite state machine like sequences.  These type of cognitive experiments that are being made in monkeys can be completely reproduced without using any computer, just using the chips that I told you about. And we've been also trying to look at, because this is technology, not only neuroscience, but also applications. And of course, there is a lot of interest from the various companies Qualcomm, Samsung, IBM, which we are in contact with for autonomous sensory motor systems, embedded systems, these ARM processors on chips, and also for brain machine interfaces. These chips are extremely low power, so they don't heat up. And you can actually have chronic implants where you can try to decode real action potentials using electronic action potentials and really hopefully close the loop and have closed loop systems to augment or implement prosthetic devices. This is not a single work, it's a group effort. And there are many students in my lab that contributed to the work, Channing, Federico, Corradie, Hisham, Laurence, Mark, Jonathan, Dora, and Richard. And also it's something that's been going on for many years, as I said. And the collaborators, the previous students, I'm very proud now that they're professors elsewhere, are still collaborating with us. And they're at Columbia, at Samsung, Bielefeld in Italy, Irvine, England, and Residen. The funding organization that supports this is mainly European, but also National Swiss, Kistera. And of course there's the whole support of the Institute of Neuronformatics in Zurich. Finally the last slide, it's a slide of an advertisement. There is a workshop that we organize, it's called the Capocaccia workshop, Capocaccia Cognitive Neuromorphic Engineering Workshop. Every year we go there, it's in Italy and Sardinia, and we bring chips, robots, neurophysiologists, neuro-scientists, and we try to brainstorm for two weeks. It's really a working group, work group workshop, and you're all invited to look into it and participate. Registration is free, it's basically on a first come, first serve. And it's really an exciting place where computer scientists, mathematicians, physicists, and neuro-scientists get together to try to solve the problems that we are dealing with even here in this room. So thank you for your attention. I should also mention that electrical engineers are starting to become really interested in neuroscience, and this has been shown by the Proceedings of IEEE, not only was the Proceedings of IEEE publishing the first neuromorphic paper in 1992, but lately, if you look at the reviews, they have special sessions on brain-inspired computation. And there is a link that I can put on Twitter, but also here's the code. It's just saying search for neuromorphic in the Proceedings of IEEE, you get all the reviews. It's really interesting to see that companies and engineers are starting to look at the brain to try to understand how to build future generations of computing systems. Okay, thank you for that. Questions? That couldn't be included in the hardware implementation, but you thought it might be important. Yes, there are many aspects that we think are important, but we have to make compromises. Just like in software, when you're deciding if you want to do a large-scale software simulation, maybe you do integrating fire models, single compartment, or multi-compartment. So the chips that I told you about focus on plasticity and learning at the single sign-ups level, and we're using point neurons, but other colleagues like Shichi Liu and my institute and elsewhere in the world are trying to implement multi-compartmental models because they're interested in the spatial processing, the dendritic processing. And so the technology allows you to explore. So in my group, we're focusing on one specific aspect, but the community overall is following multiple approaches where the different ideas just get implemented in silicon. As I said, it's another tool. Instead of using Python or C, you use VLSI technology, and if you have some idea or interest in some specific topic, you can also implement that in electronics using this approach of sub-threshold transistors. Okay, thank you. So I'm wondering, in your view, what's the advantage of a spiking neural network has over artificial neural network? In terms of a computation efficiency or computation power? In terms of computational efficiency, the advantage is that when we want to build large-scale systems, spiking neural networks scale much better than non-spiking ones, at least when we're trying to implement them in analog VLSI. So analog VLSI is very useful because you can get really extremely low power, but if you want to have scalable systems, you need to find a way of communicating analog signals long distances. And it turns out, if you try to optimize for bandwidth and power, you end up finding that the optimal solution is pulse frequency modulation, which is what nature found by using axons and action potentials. So it's also nice to see that by optimization techniques through mathematics and engineering, you end up with the same solution that nature evolved using spikes and axons. Okay, thank you. Another question is, you mentioned in your single trip, it cannot learn to recognize the handwriting digitals and the... It can learn to... Yeah, the first step, you have only 50% success rate. Yeah, one single perceptron has really weak performance or happy performance. I see, so how many neurons in that single perceptron? One perceptron is one neuron. I just want your... But the chip has hundreds of neurons and if you put two chips together, four chips, you have thousands of neurons. So you can increase the accuracy just by using more neurons. Okay, thank you. No more questions. So then I guess we should thank you again and all the speakers of this session. Thank you.


Conflict is yet again brewing in Europe. There have been allegations and counter allegations as Russia and Ukraine and its western allies have been both alleging that the other side is about to cause a war. There have been even some mobilizations of troops, threats of sanctions and even more severe measures. We'll be talking about all this in Mapping Fortlines. We have with us Prabir Pulkajastha. Prabir, so over the past few days and even weeks for that matter, we've been hearing a lot of news items, some of them in isolation that there are soldiers mobilizing. There have been statements by leaders from Ukraine, leaders from the West, including the US, which is quite far away. But nonetheless, Antly Blinken has been making some statements. The Russians also have been saying warning against a return to the kind of armed confrontation that used to be the norm in Europe in decades past. So could you maybe quickly take us through what is the situation on the ground as far as Russia and Ukraine are concerned? What are the issues that take here? You see, the first thing of course is that the Donbas region, which has declared autonomy from Ukraine. It was not separated from Ukraine. It was promised that there will be under the Minsk Accords, Minsk 2 Accords, if I remember correctly, that there would be an autonomy which would be guaranteed by certain steps that the Ukrainian state would take. Those have not been implemented. And though Russia has talked about it and written to both Germany and France, you know, the guarantors of the Minsk Accords, no steps have been taken by the Western powers of this at all. And the United States from the beginning has been a spoiler, as we know, wanting Ukraine to really expand up to Russia's borders, meaning NATO comes up to Russian borders. It already has come to borders in other parts, but here too. And for Russia, that has been a no-no. A, of course, is Crimea and Sevastopol, the naval port is involved, but also the fact that they do see themselves as protectors of the Russian ethnicity within Ukraine, which is really the Donbas region, and what was really won through a civil war, virtual civil war in that part of the world. That gain should not be lost because both Germany and France took no measures. And Russia did not take also any measure to see that autonomy which is promised in the Minsk Accord is really observed. Now, it's also clear that the United States does not want to play by the Minsk playbook. They are very clear that they would like to come to the borders of Ukraine, the Donbas region autonomy that since Ukraine, the current dispensation or the earlier one does not accept, it would like to dismantle it if it can, that that is something they would back. Why they would back that? Because they want Ukraine to join the NATO, and Ukraine wants to that autonomy of Donbas region not be there. And therefore, they would like to position their troops there also. This is a red line that is there, and this is what Russia has said, that there is a red line they have that if NATO comes to the Donbas region, supports Ukraine to come to the Donbas region, if the army, Ukrainian army enters Donbas, then Russia would protect Donbas. That is the basic framework they have set. And this is where the conflict is because as far as US is concerned, that region belongs to Ukraine, therefore Ukraine presumably has a right to take it back by arms. And the Minsk accord, according to them, US was never a signatory to that and Germany and France do not seem to want to own up to that as well. So therefore, that accord for them is dead. So Russia, if it prevents Ukraine from coming up to the borders, then that would be taken as a gesture of bad faith. It if Russia intervenes, then it can possibly go to war, conflict on the ground with not only Ukrainian troops, but with NATO troops as well. And the disturbing part in all of this are two kinds of threats we see emanating. One is from what a German politician belonging to the Green Party, which is a part of the ruling coalition now, said that we could also use nuclear weapons in a first strike if Russians cross the border. Now threatening nuclear attacks in a first strike, this is extremely dangerous talk because what you're talking about is the destruction of the human civilization that should be done in this kind of way, itself raises the temperature and makes such a possibility even more likely that you can trigger a nuclear war without really intending to. So that is one part of it and the other threat that has come from Blinken and he seems to be the guy who seems to be handling our threats everywhere. And then we have various American emissaries to try and see, cool the waters, whatever it is, try and see that you can calm the issues down a little bit. So Blinken has said that severe financial sanctions which will hurt Russia far beyond what we have done now, aren't they really talking about throwing Russia out of the For instance, the Swift financial system? What are they really talking about when they say all of these things? Is it the interdependence of Europe that after all Russian gas comes to Western Europe that they would like to sanction so that ultimately Western Europe has to buy maybe American fracking gas? We're not clear about what these terms are. But the fact that Blinken is making this kind of threats also is war talk because ultimately financial war is a war. It should not be taken to be something which is not because it's not a kinetic war. Therefore, it is not war. It is also an economic war is also war. So I think this kind of scenarios brings back memories of what we have seen earlier. Brinkmanship of an order which we haven't seen in Europe for a long, long time. So I think that's what is should be of course were concerned for everybody. And this threat of sanctions which the United States holds out. If other countries do not listen to them, I think it's going to be on the agenda for all countries because we in India, for example, we have this 400 issue. And as you know, the Americans have also threatened India with sanctions, particularly if we buy this 400 anti-missile batteries. So if we do that, that will also mean that it's not something only Russia will be facing a problem. All countries would deal with Russia and we have serious economic as well as armaments deals with Russia. We would also be at risk as we were when the United States sanctioned Iran. And our oil supplies were hit. So I think this is something the world has to come to terms with. Does the US have the right to disturb the world in any way it seeks fit? As long as it holds the financial powers, then it believes it can do so. And according to American terminology, set new international rules in place. So basically, still want to be the global number one, even if the other political economic scenario does not square up to that. This is what we seem to be seeing at the moment. So Ukraine is a test case. Just as Taiwan is a test case under the freedom of navigation. On the other side, this is a test case to one side, Ukraine with Russia, and Taiwan with China. So I think these are the two things to watch in the near future. Well, Praveen, actually my second question was regarding that, because the Donbas issue has been around for quite some time. It's not something that began over the past few months. So what is the specific reason for this kind of escalation right now, when considering that Trump is no more there, people are saying that, OK, now it's a different ballgame. But the Democratic administration seems to be even more hawkish in some ways. Well, I think on Russia, as we know, the Democratic Party was always more hawkish than Trump was. Because if you don't forget, they said that Russia helped Trump to win. So from the beginning, they said we are the true opponents of Russians. On the question of China, Trump appeared to have been more hawkish by the economic war it waged on China. Coming back to the Donbas issue, as you know, Donbas is really a fallout of the breakup of the Soviet Union. And the fact that a strong Russian ethnicities, Russian speaking populations in different parts of Eastern Europe, and it was true also for the Baltic states. A lot of them were either disenfranchised and then left, or a lot of them actually left. So all those processes took place. I'm saying left because that's a nice way of putting it. But we know it was semi-coercive, if not coercive. In Ukraine, there is a very large population of that. And as we know, large population of even those who identify themselves as non-Russian speak Russian. In fact, some of the people who have ruled Ukraine were anti-Russia and anti-Russian's ethnicity. But this book, their major language was still Russian. So taken all of that, when the Russian television, radio stations, newspapers are banned, and I'm talking about Ukrainian Russian speakers, I'm not talking about Russia as such, then obviously there is a deeper fracture over there which has been created. And we have also had the rise of really fascist forces, right-wing forces in Ukraine, who swear by essentially those groups which aligned with the Nazis during the Second World War. So that is the internal fracture of Ukraine. But there is a much larger historical issue. And if you take the whole of Eastern Europe, particularly after the fall of Soviet Union, then it breaks up, Yeltsin leads the breakup, signing the pact with Ukraine and Belarus that we don't recognize the Soviet Union anymore, we are dissolving it. And that's how he replaces Gorbachev, becomes the president of Russia, rather than the president of Soviet Union. Now, when that happened, Yeltsin had the belief that all these countries will be partners of Western Europe, and they will be allowed to peacefully integrate themselves into Europe. And his belief was, therefore, the NATO should really not expand any further towards the East, up to Germany, yes, but no further. And all the Eastern European countries, including Russia, would be incorporated in a kind of friendship partnership. I think it was called Progress and Friendship Program, which by which they will be integrated. And they will have a European kind of European larger Europe, if you will, which will then be the part in which Russia will also be recognized. Russia's aspiration has been to be recognized for quite some time, and that goes back 300, 400 years. So if we look at that, the Americans were very much against it from the beginning, and now the internal correspondence has recently been made public by freedom of information. All these documents are now in public domain, and it is very clear, the Americans didn't want that to happen, but it's a very simple reason. They lose their dominance over Europe, and NATO was their instrument of doing that, so they wanted to reconfigure NATO as an instrument of domination of not only Europe, but also rest of the world. In fact, NATO was then going to look out of Europe as well, becoming the main genre arm, if you will, of big capital, global capital, of course, which United States was the key one. And in their view, therefore, Yeltsin had to be bamboozled into accepting the eastward march of NATO. Yeltsin was very upset at one point here, and Clinton had a public spat where he shouted to Clinton, but he was going to be very delicately placed, shall we say, in an election, I think in 1996, and Clinton said, okay, I'll help you win, and that seems to have got over Yeltsin. But the Russian state saw that as a threat as NATO marched right up to its borders and set up missile batteries from Baltics, Romania, Poland, and now it threatens to come up to Ukraine, and that Ukrainian border, it's already in Ukraine. So now it is giving, putting down red lines that if you do this, this is not acceptable. If you don't accept the red lines, what you're really saying, you can put missile batteries which can reach our command centers in Moscow in five minutes. And if you do that, that is something not acceptable to us. And Putin has said our response will then be, of course, hypersonic missiles and so on and so forth. So you get a renewed arms race.  You're talking of nuclear weapons, the use of nuclear weapons. You're really in a territory which is completely uncharted, except at the height of Cold War, the Cuban War, for example, you know, Cuban blockade, for example. And I think that is a very, very dangerous period to go back to.  So one side you have Ukraine, other side you have Taiwan, and they seem to be willing to ratchet up pressures against both Russia and China, simultaneously on these two fronts. I really am not clear what is the vision they have or are they walking us into a global Armageddon with their eyes open or eyes shut? Thank you so much for being here. That's all your time for today. Keep watching NewsClick.


The Chinese New Year 2021 is right around the corner and you need to know how to prepare your dropshipping stores in advance for this holiday where the Chinese are not working and whether you're dropshipping on eBay or Shopify. As long as you're sourcing products from China, you need to watch this video to prepare yourself accordingly. Quick intro, let's begin. What is up, everybody? Hope you're all having a wonderful day today. My name is Liran from AutoDS. I'm the content producer and I've also been dropshipping for the last four years. And as I've mentioned, this video is going to cover the Chinese New Year 2021 holiday which is coming up in less than one month. And since the Chinese are not working on this holiday, we have a lot of unsatisfied, mad and mean customers wondering where their packages are, opening up cases that their items were not received or not shipped out on time. And we do not want any of this trouble on our dropshipping stores. That is exactly why we created this video for you. So without further ado, let's begin. To start off, what is the Chinese New Year? Well, it's the New Year for the Chinese. If you haven't guessed that one yet, just like we have our New Year celebration between December 31st and January 1st every year, the Chinese have their Chinese New Year starting February 12th and ending on February 17th. That is the public holiday. Now the Chinese will continue celebrating after this date, but this is the public holiday between the 12th and the 17th of February, where the Chinese will not be working. Now keep in mind also that some Chinese even began their holidays before the 12th of February. Some even start one and even two weeks before. So we really need to make preparations and let our customers know what's going on. Whether you're dropshipping on eBay or Shopify, it doesn't matter. I will show you on both platforms how you can let all of your customers know what's going on. So there won't be any special surprises and even if there will be, I will prepare you guys for a counter on that scenario too. So let's begin. We know that the Chinese New Year is between February 12th and February 17th. Now what should we do? Let's start off by going through some of the common questions that dropshippers have during this time. And one second before we begin, I just want to remind you guys, if you are new to our YouTube channel, this is the part where you need to subscribe so that you can always stay up to date on all of the latest and all of the hottest topics that are coming out in the dropshipping industry, which is created from dropshippers for dropshippers such as yourselves. Hit that subscribe button right now and let's get on with the show. So the first question that people usually ask before this holiday comes is how will the Chinese New Year affect my dropshipping store? The Chinese are not working during this holiday between February 12th and February 17th. Some of them will not work even before that. So one way that it's going to affect your dropshipping store, the main way is your products are not going to ship out on time. There will be delays. You are not going to update your tracking information on time. The product will not ship out on time. The customers will notice this. The majority of customers notice when their shipments are delayed and they will not hesitate to reach out to you or just head on to PayPal or whatever method they paid you with and open a case over there without even letting you know we don't want this trouble on our stores. So that is how the Chinese New Year affects dropshipping stores. We will get to the solution soon. Let's just move on with the common questions and problems. Which suppliers are affected by the Chinese New Year? So anyone who's sourcing products from Chinese warehouses will be affected by the Chinese New Year. Sometimes you may find Chinese agents who are sourcing products from US warehouses. This is a different scenario. You need to reach out to that seller and ask him if he's working during the Chinese New Year. But in the majority of cases where you will find Chinese sources shipping out their products from Chinese warehouses, these are the products that are going to be delayed because they are not working during the Chinese New Year. So you can think of pretty much any Chinese source supplier like Aliexpress, Banggood, CJ Dropshipping and more. All of these suppliers will be affected so if you're working with any supplier who's sourcing from Chinese products, keep watching this video and we'll get to all of the solutions that you need to know about. The next frequently asked question is how long are the Chinese New Year shipping delays? So we know that the holiday dates are between February 12th and February 17th but as I've mentioned a minute ago, many Chinese workers stop working even a week or two before the start date. So we have at least two full weeks where the Chinese are not going to work and they will not be fulfilling our orders. So the Chinese New Year shipping delays are anywhere averaging two weeks and in some cases maybe even a little bit more. How can I avoid shipping delays with Chinese sources? So in some scenarios you may find that the product is being sold in different warehouses on the same product like in Aliexpress or Banggood or many other suppliers who are sourcing from China. On the product page you will see that there is also a UK warehouse, maybe warehouses from the Russian Federation, warehouses of course in the United States. So if you see that your supplier or the seller that you are working with is shipping from different warehouses, ask him if he's working during the Chinese New Year and which warehouses are working specifically and that way you'll get answers to those questions. You should do this mainly on your best selling products. Products that are not selling are probably less important but we will take care of those too. Start with your best sellers and reach out to your sellers, see if there's other warehouses that they are working with and if they are working during the Chinese New Year. The next question asked by many people is what niches are affected by the Chinese New Year and what does it mean what niches are affected? Well since you have the Chinese New Year and it goes around two weeks of not being able to ship out any of your products you should also know that there are other holidays that occur during the Chinese New Year and it's going to be hard to source products for those holidays because the Chinese are not shipping out anything during that time. So the first holiday that you have is Valentine's Day which is on February 14th. So if you want to prepare your stores for Valentine's Day I definitely do not recommend doing it from Chinese sources because they will simply not ship out your product on time. Your customer will not get the product on time for the Valentine's Day gift that they bought for someone else and they will be pretty mad about that. Another holiday that we have during the Chinese New Year is a holiday called Family Day which occurs in Canada on the 16th of February so if you want to sell products to them do not do it from Chinese sources. So let's get to the money time. How can we start preparing our dropshipping stores for the Chinese New Year? Let's begin for all of you guys who are dropshipping on eBay. Shopify your next on the list. So in order to start preparing your store for eBay the first thing that you want to do is schedule time away also known as vacation mode. On schedule time away there are a few things that you can configure to let the buyers know what's going on with your store. So the first thing that you can configure is whether you want to allow item sales or to pause item sales during this time. My recommendation is definitely to allow item sales we do not want to close our stores even temporarily but we do want the customers to know what's going on. So you're going to choose that you want to allow item sales and when you allow item sales you can put your store away for up to 15 days. If you don't allow item sales you can put your store away for 30 days. Now since we are allowing item sales we are only going to go for the maximum of 15 days and you can also set your start and your end date so in our case it's from February 12th to February 17th but we do want to start before because as I've mentioned the Chinese are going to start taking their holidays before so we're going to take 15 days back from the 17th. In our case we are going to choose February 2nd until February 17th. Now what happens during this time you can set an automatic response and all of your customers will read this response every time they enter your store so they'll get a message about your store that it's away. Let me show you guys really quick how it looks. Here is the time away on eBase platform. Once you click on schedule time away you can set your start and your end date and allow item sales while time is away. You do not want to pause so click over here set your start date and date and apply. Next you got your automatic message so click on that and write down the message. We are currently celebrating the Chinese New Year therefore all orders between the start date and the end date etc etc so once you set that ebay will put you on your time away and your customers will know what's going on before they even make a purchase from your store so those who do purchase already understand that the product will not chip out on time and they will not reach out to you with any surprise messages of where's our product why hasn't it been shipped yet and so forth. The next thing that you want to do on ebay is update your shipping policy accordingly to the new shipping times so even if a customer places an order you want the estimated shipping time to show a more precise time of when this product is going to arrive. We want to update the 11 to 23 business days to 11 to 35 business days you have that in your shipping policies i don't have to show you this example because if you're already drop shipping you know exactly where to find that your handling time should be updated to four business days or five business days i know that it's not as good as three business days ebay will rank you better if you have one two or three business days on your handling but again we do not want any surprises this way you'll have more than a month to deliver your product and that is more than enough for the Chinese New Year delays. The next tip to update your customers on what's going on is by updating the custom messages that you are sending your buyers after they successfully put an order on your store and AutoDS puts out that order automatically using the auto order method so let me show you exactly how that is done head to the AutoDS platform and click on monitors then click on orders processor and messages to buyers here you have the after order success message this is the message that is sent after AutoDS fulfills your order for you and sends a message to the buyer and i have a message template ready for you for this message that you should send the buyer over here in the blog that we have if you want the link to the blog you have it right under this video so copy this text it's a good example of a message that you should send your customers you can obviously create your own messages if you want to but if you need help you have it over here and as you see i and as you see i also put the triggers for AutoDS so it will automatically fill it in with the relevant information so copy that message head back to the AutoDS platform paste that message in the after order success message and then click on save changes that is how we will get that done it's that simple and now let's move on to the fourth solution that we have the fourth solution is to update your ebay template so you have your ebay template that you're using for all of your products you can update your template and add an extra text on the bottom reminding them of the holiday that is coming up so on every product listing that you have when the customer scrolls down he will see another message over there regarding the chinese new year but how can you do that for all of the products that you already have in your store let me show you let's take the ready message that we have on the blog now choose a template that you want to use on all of the products that you want to update with the new template and the new message scroll down a little bit you have your title description features about us shipping returns i don't know what you have in your template but in your shipping section if you don't have one create one and add that extra warning that we just talked about please note that we expect shipping delays during the chinese new year holiday from february 12 to the 17 thank you for your patience of course you can write whatever you want this is just a quick message to show you an example and once that is done go to your active listings now we want to grab all of our items and tell auto ds instead of using this default template that they already have i want you to override it with this template with that warning message on the bottom so grab all of your products click on bulk change and on template you're going to select that template that you just saved once you click on it click on save changes and now all of the products that you grabbed in that box selection will get the new template with that warning message about the holiday season that is coming up and that is the fourth way on ebay to let your customers know what's going on and once again combine that with the first three methods you don't have to use all four of these methods but i would definitely recommend it because this way you are filtering out all of the customers who can come out and say hey you didn't tell me that this is going on i didn't know that my product is not going to be shipped out on time so this way you're really protecting yourself in every single way possible that is more than enough for the ebay solution of how to let your customers know about the chinese new year and all of the upcoming delays that are expected now let's move on to Shopify the first thing that you want to do on Shopify to start preparing your store and let your customers know what's going on is by updating the announcement bar that you have on top and if you don't have an announcement bar it's really easy to set one up let me show you really quick how that's done head to your Shopify dashboard click on online store then under themes click customize on the right and your announcement bar should be in the header section on the top so let's wait for the left menu to load up click on header scroll down a little bit and you'll have the announcement bar so we're going to click on show announcement and as you can see the announcement bar just showed up on top now let's take a ready message that we already have in the blog below we are expecting temporary shipping delays during the chinese new year holiday between these dates i'm going to copy that text paste it over here on the left text field and once again it's updated on the announcement bar on top now why do we want to update the announcement bar i hope this one is obvious for you guys but one of the first things that your viewers will see when entering your website is the announcement bar on top so first and foremost we are going to let them know using the announcement bar now the second method to update your customers on what's going on on your Shopify stores is by updating your product pages accordingly this time you're going to have to enter your product page and in the product description add a warning message about the upcoming delays that are expected once again you have a ready message template in the blog that we have so i'm going to copy that text and this time we're going to do it using the autodesk platform because it's simply going to save us much more time this way so on the autodesk platform we're going to click on products now in the product section you need to highlight all of the products that are being sourced from chinese warehouses you can do this easily by creating product tags by the way but if you haven't done that let's just say that i want to update this product for now so click to edit then i'm going to click on description edit product description and on the bottom i'm going to add an extra text copying and pasting what i had there on the blog of course once again you can write your own message but here it is i'm going to click on save and i'm going to rinse and repeat this process on all of the products that we have from chinese sources and that is another way to let your customers know what's going on with this chinese new year holiday and the upcoming delays once again i encourage you to do this using the autodesk platform you'll see that it's much quicker and much easier than using the Shopify platform the next thing that you want to do on your Shopify platform is to set your email notifications after purchase what these email notifications are is these emails that your customers are getting every time they place an order on your store let me show you exactly what i mean head to your Shopify dashboard and click on settings notifications and order confirmation as you can see the order confirmation is sent automatically to the customer after they place their order here is where we also want to update them about the expected upcoming delays now do not be intimidated by all of this html and all of these triggers you don't have to understand it just get to the part where it says hi customer first name we're getting your order ready to be shipped we will notify you when it has been sent now what you want to do is click enter after that sentence but before the end case make sure that you're in between the else and end case so we created a line break now we're going to copy this text or once again create your own and paste it right over here so now the customer is going to receive a message hi customer's first name we're getting your order ready to be shipped will notify you when it has been sent and then we are currently celebrating the chinese new year from this day to this day blah blah blah all in that email and this is another great way to cover yourself and let the customer know what's going on now the next method to update your customers about the upcoming shipping delays is by updating your shipping policy also on Shopify so your shipping policy should be in your dashboard settings and legal pages unless you created another page for your shipping policy that is where it should be so navigate to the settings and then to the legal pages get to your shipping policy and here change the amount of business days how long it will take to reach the customer according to the chinese new year delays so in this case to the us we'll make it by 30 business days for example click on save and then you are protected also by your shipping policy so we updated the announcement bar we updated our product pages we updated our shipping policy now what do we do with customers who missed out anyway on the chinese new year holiday they missed all of the messages they didn't check your shipping policy hey it's their mistake but you will find a small number of customers who missed it out anyway and the more sales that you have the more you know exactly what i mean so we need to be able to reply to these customers who are reaching out to us and asking us what's up with the product shipping why didn't it deliver yet why don't i have tracking information yet so in this scenario again i prepared a message template for you now keep in mind these parentheses are not triggers you need to actually change the client's name with a client's name and your name or your store name with your name or your store's name so if you don't want to create your own message just copy this and use that to answer your customers who are asking you what's going on the next tip that i have for you guys to avoid these shipping delays is to diversify by working with multiple suppliers working with multiple suppliers is the best way to get out of these situations and not just the chinese new year holiday throughout the year you have all kinds of surprises like natural disasters and other things that are going on in the world so at some points some suppliers are going to stop working or they're going to slow down their shipping process and you won't want to lean on only one supplier who may cause these problems from time to time when you diversify working with multiple suppliers you are not putting all of your eggs in one basket it's much healthier for your business and your business will simply continue working even when there are problems i want to remind you guys that here at auto ds we support over 20 suppliers which means you have a reach to over 500 million products to choose from that is why you should not work with only one supplier my personal recommendation is to always work with three dropshipping suppliers if one is not good eliminate him and add another when you're working with three it's never going to get too messy or too confusing everything can stay under control and you can definitely maximize the potential of your dropshipping stores so diversify do not work only with chinese suppliers suppliers from the us are going to ship your items out quickly they may not always be as cheap as chinese suppliers if you want to know which are the best american suppliers we have blogs and videos ready on that subject too so diversify add multiple suppliers and this way your business will not be affected by the chinese new year holiday my next tip to you guys is to stay up to date with the e-commerce calendar for 2021 we have a link to the e-commerce calendar right below this video and on the blog below once you click on that e-commerce calendar for 2021 your google calendar will be updated with all of the special events and all of the holidays that we have coming up in 2021 and we're always updating this e-commerce calendar with dropshipping strategies for each special event what you should do how to prepare your stores for it and which products you should add to your stores that have a much higher chance of selling during these holidays so when you have your list of holidays for the upcoming year it's going to be much easier to prepare for them you'll have enough time because you'll see it in advance and you have the strategies that we are writing for you guys to help you on each and every special event so add that e-commerce calendar once again it's completely free and we are updating it all of the time the next and final tip that i have for you guys during the chinese new year holiday is to definitely not close your stores but if you are sourcing only from chinese suppliers you will notice a decrease in sales that is one reason why you should work with multiple suppliers and add also warehouses from the us that way your store will continue working during the chinese new year but even so you should take some time off during this time and learn dropshipping now what do i mean by this well we have a full course a to z on Shopify absolutely for free on how to find products that will sell create your e-commerce store and market your products the right way to start getting those sales this full a to z course will take you to where you want to go with your dropshipping business even if you have no skills and no prior knowledge whatsoever on the subject we also have an ebay dropshipping course for those of you who haven't started on ebay yet or for those who have started but are having some challenges trying to get those sales and profits this full walkthrough will take you on your dropshipping journey and turn it into a successful one that is all i have for you guys for the chinese new year holiday i hope that you found this video informational and if you did give us a like give us a share so that your friends and family will also have the opportunity to learn about the wild and amazing world of dropshipping and if you are new to our channel do not forget to subscribe so that you will always know about all of the hottest topics strategies product finding suppliers marketplaces and everything else that we have on the dropshipping world all of the latest news and updates all for free all for you from dropshippers for dropshippers thank you for watching see you in the next videos


What's good, YouTube? This is your boy Chiwara back to y'all again with another art tutorial, man. If you're new to the channel, go ahead and hit that like button, come in, subscribe, make sure you click post notifications so you'll be notified anytime your butt drops a heat. In today's video, I will be showing you guys how to sketch an Adobe Illustrator. I'm gonna drop a lot of these videos and every time we do it, the picture gonna slowly get more advanced. And I will have a link to this picture in the description below so you can follow along and you can practice by yourself if you want to. So without further ado, let's go right to the video.  I can say that a thousand times and people will still ask me in the comments. You got to have a digital art tablet. So now I'm going to set my pressure on my brush, go to pressure, turn that number to three, and then number three hit OK. And now I got my pressure brush.  And drop it a little lower. OK. If you get in the habit of sketching as much as you can, man, you're gonna get better. Your art skills just gonna drastically get better, man. So make sure you do a lot of practicing. Let's go ahead and sketch this picture out. It'll improve your all-around skills, man. Just sketching a lot, practicing. Every picture I draw, I make sure I do a nice sketch first. I don't just go straight into it, always sketch. So I think that's the key to art, like really mapping everything out before you before you really start.  And the more you practice, the better you'll become. This mouth is close this way. So we might, through this whole week, we might do a sketch tutorial for this whole week, you know what I'm saying? Y'all just let me know what y'all think in the comments. Would y'all like to see that? Just me doing different pictures that y'all can follow along and practice with. Let me know because we can show enough do it.  Create us a new layer. Let's make some clean lines. All we're gonna do is just go through and clean up our lines. Do two also. I want to make this brush just a little bigger. Just like I tell you guys, the more you practice, the better you'll be, man. So it's up to you to improve, you know what I'm saying? It's how much work you're willing to put in to get to that next level. You gotta practice. So two, move this black to the top so we can fill all this in. Let's highlight everything. Object, expand the pinch, merge on our Pathfinder. Lock that layer. Create a new layer right under our line work. Let's color this thing.  Color this thing. Now we got that boy. Tigger me Elmo. Let me create a new layer right above that and we're gonna do like a shadow. You know what I'm saying? Let me see how I'm gonna do it. I might do a shadow coming from this side. Yeah, that's what we're gonna do. So let's go ahead and do that. Just color down a song. Blend it in a little bit. Okay, cool. And you'll get better at just coming up with your own shadows with practice. Everything will come over time as far as getting better. Your skills will get better over time. You just gotta put in the time. You gotta put in the work. Gotta practice. I practice a lot and that's why on the level I'm on right now as an artist, just practice. There you have it you guys. How to sketch to make a dope picture in Adobe Illustrator. Man, we're gonna do a lot of these practices throughout this week. You know what I'm saying?  Just like I said, I will have this Elmo picture in the link below so you can grab it yourself and you can practice on it.