pace of change catalyzed by the internet was unprecedented. Consider now that AI user and usage trending is ramping materially faster…and the machines can outpace us. The pace and scope of change related illustrate observations that follow. We hope this compilation adds to the discussion of the breadth of change at play – technical / financial / social / physical / geopolitical. No doubt, people (and machines) technology builders who helped, directly or via your work, and are driving technology forward.• Seem Like Change Happening Faster Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model

1 − ?, 1 + ? � ?? � − ?D?? � ??||??? ? �� , (32) D?? � ??||??? ? � = ??? ? (??|?) ??(??|?) − log ??? ? (??|?) ??(??|?) − 1, (33) where ? and ? are hyper-parameters; and ?? is the advantage, computed when the temperature was 3°C. At night the outside temperature dropped to -2°C. This temperature change most likely caused the water to _ A. condense B. evaporate C. remain a liquid D. become a solid Answer: }.$$ Final Answer: The final answer is $-\frac{2}{3}$. I hope it is correct. Problem: Evaluate $\log_21$. Solution: Table 27 | An example of MATH. 45 PROMPT You are an expert Python programmer, and

platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application options if options else [ “-shared”, “-fPIC”, “-m32”] b. python tensorflow_blur.py to get the .log c. Use the .log, with target=“llvm –mcpu=i686 –mtriple=i686-linux-gnu” then TVM_NDK_CC=“clang –m32” python

prompting The above example shows an example of taking the role of a travel agent. When you would change the role to a teacher of geography, you would notice that you will receive a different response Confrontational, Descriptive, Direct, Formal, Humorous, Influential, Informal, Inspirational, Persuasive Let’s change our prompt in Table 6 to include a humorous and inspirational style. Prompt I want you to act as based on the model’s performance. Keep experimenting until you achieve the desired output. When you change a model or model configuration, go back and keep experimenting with the previously used prompts

exp_dispatcher) vmc = relay.backend.vm.VMCompiler() with tvm.autotvm.apply_graph_best("resnet50_v1_graph_opt.log"): vm = vmc.compile(mod, "llvm") vm.init(ctx) vm.load_params(params)

models produce. Rigorous evals lead to more stable, reliable applications that are resilient to change. 
 Evals are built around tasks that measure 
 the quality of the output of a model against 
 a benchmark—is

whether that's resolving a customer service issue, booking a restaurant reservation, committing a code change, 
 or generating a report. Applications that integrate LLMs but don’t use them to control workflow