Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Those are a few examples of challenges a product manager might face when working with artificial intelligence. As someone who has practiced the craft for decades (I founded a company that built an AI-related, algorithmic product and now run product management for applied AI for Xerox at PARC), I want to share some thoughts on what is distinctive about product management for AI. Product managing AI-based applications is still product management, but it requires some additional know-how, and maybe even some magic dust. While the role of product manager has been around since the '30s, the specifics of the job function (the "mini-CEO") have generally been vague. Nevertheless, the popularity of careers in product management have soared in recent years.
As the Director of Data Science for an advanced analytics company, Sean brings machine learning automation into business applications to help organizations build core strategies around data. Having worked across diverse industries, and alongside many talented professionals, Sean has seen the blend of approaches required to successfully convert raw data into real world value. Sean holds his doctorate in scientific computing where he used advanced mathematics, parallel computing and optimization to solve challenges in nanotechnology, chemistry and renewable energy. After completing his Ph.D. Sean started his own Data Science consulting practice, helping companies automate decision-making and uncover the underlying patterns that drive business environments. Sean has since worked for global consulting firms and silicon valley startups to help bring the advances in machine learning to business applications.
The HDAWG from Zurich Instruments is an arbitrary waveform generator (AWG) with the highest channel density (HD) and shortest trigger latency available. HDAWG offers up to eight channels with 16-bit output and a sample rate of 2.4 gigasamples per second. A waveform memory of 500 megasamples, 32-bit digital input/output (I/O), and transistor–transistor logic (TTL) marker output complete the package. For applications requiring many channels, multiple instruments can be synchronized and centrally controlled. HDAWG includes the LabOne user interface and multiple application programming interfaces.
Gene transcription (TX) and translation (TL) is executed in a single reaction tube by a highly efficient cell-free system that utilizes the endogenous TXTL machinery from Escherichia coli. This all-in-one solution offers convenient, one-step gene expression from a simple nucleotide template for various applications in synthetic biology and biomanufacturing. The system employs endogenous core RNA polymerase and primary sigma factor 70 (σ70) present in the E. coli cytoplasm. The technology has been well characterized and proven useful for applications including high-yield protein synthesis, prototyping of biomolecular networks, bacteriophage production, and high-throughput protein expression analysis.