In lieu of abstract, first paragraph reads: Six months after the author derived a constant of motion for a 1D damped harmonic oscillator [1], a similar result appeared by Liu, Madhavan, and Tegmark [2, 3], without citing the author. However, their derivation contained six serious errors, causing both their method and result to be incorrect. In this Comment, those errors are reviewed.

Artificial intelligence and machine learning (AI/ML) are key to enabling drug discovery and development, and Pfizer is leading the biopharma industry into the next wave of innovation. The company is rapidly scaling up and recruiting talent for a collaborative effort intended to get transformative medicines to patients faster. The mandate is "uncompromising and extremely high-quality science," Sandeep Menon, chief scientific officer, AI digital sciences, SVP and head of early clinical development told BioSpace. The vision is three-fold: uncover disease biology with AI; use these insights to design the right molecules; determine the right patient population for clinical trial success. "We're building the next generation of tools to use across the preclinical and clinical development spectrum," said Jared Christensen, vice president and head of early clinical development, clinical AI/ML and quantitative sciences.

Now that data is the new oil, AI software startups are sprouting across the tech terrain like pumpjacks in Texas. A whopping $80 billion in venture capital is fueling as many as 12,000 new companies. Only a few will tap a gusher. Those who do, experts say, will practice six key success factors. Some of the biggest wins will come from startups with AI apps that "turn an existing provider on its head by figuring out a new approach for call centers, healthcare or whatever it is," said Rajeev Madhavan who manages a $300 million fund at Clear Ventures, nurturing nine AI startups.

Parentof, a decision sciences start-up that provides insights into child growth and decision analytics, has raised $1 million in seed funding, bringing their total funding since inception to $2 million. The round was led by angel investor V Srinivas and other existing investors. The capital raised will be used to further evolve the start-up's technology, and to expand its partner network, including schools, vocational institutions and e-learning platforms. Founded by Swaroop Madhavan, Dr Manohar Chikkanna and Dhananjay Prabhu in August 2015, the Bengaluru-based parent-tech start-up uses AI-powered ARC Engine (analysis, recommendation and curation) to identify and assess each child's unique growth and developmental journey. This has helped the over two lakh children and is supported by over 500 schools, the start-up claims.

Wishing to learn Python's machine-learning toolkit - I am an emigrant from R Country - I rounded up several relevant books, and set out to narrow the field to one or two suitable for further study. No point continuing: this is the Packt-standard low-quality fare, written without any expertise, care or spell-check; an obvious step-down from the two earlier books. The positive reviews on this page do not surprise me at all: Packt titles are often supported by praise from people who had not reviewed anything, or only reviewed - and loved - other Packt titles.

Many web-search queries serve as the beginning of an exploration of an unknown space of information, rather than looking for a specific web page. To answer such queries effec- tively, the search engine should attempt to organize the space of relevant information in a way that facilitates exploration. We describe the Aspector system that computes aspects for a given query. Each aspect is a set of search queries that together represent a distinct information need relevant to the original search query. To serve as an effective means to explore the space, Aspector computes aspects that are orthogonal to each other and to have high combined coverage. Aspector combines two sources of information to compute aspects. We discover candidate aspects by analyzing query logs, and cluster them to eliminate redundancies. We then use a mass-collaboration knowledge base (e.g., Wikipedia) to compute candidate aspects for queries that occur less frequently and to group together aspects that are likely to be semantically related. We present a user study that indicates that the aspects we compute are rated favorably against three competing alternatives related searches proposed by Google, cluster labels assigned by the Clusty search engine, and navigational searches proposed by Bing.